Patent Application
20240420189
An unmet need in the software engineering field revolves around obtaining accurate and timely feedback from users of a software application. Often, a stakeholder associated with development of a software application (e.g., a software engineer) is unable to receive any user feedback regarding the software application. Moreover, even if feedback is received, the feedback lacks useful information regarding existing and candidate software capabilities. For example, the feedback is limited to a numeric form or some other type of fixed scale that provides little insight into why a certain type of feedback was given. In some cases, the feedback might be from an individual or group of individuals who are not actual users of the software application. As a consequence, making improvements to software applications proceeds in an inefficient fashion if at all.
The implementations herein overcome these and potentially other technical problems by way of a system for collecting, analyzing, and presenting user feedback with improved accuracy and relevance. These implementations may take advantage of large, multiuser computing platforms hosting hundreds of applications that are used by tens of thousands of users. With such an extensive user base, more feedback can be collected, thus improving feedback quality through sheer volume. But beyond that, detailed free-form feedback (e.g., text in the form of text strings) can be gathered rather than just fixed-scale feedback. Free-form feedback cannot easily be summarized mathematically, and thus is harder to quantify. Accordingly, new ways of making sense of this feedback are needed.
Various implementations disclosed herein include a distributed feedback system with at least three features. First, a customizable feedback component for a user interface allows users to be queried for feedback in relation to the application that they are currently using. The feedback may use free-form input and possible fixed-scale input as well. Second, this feedback is given to a supervised or unsupervised machine learning model that performs similarity determination, clustering, and/or sentiment analysis on the feedback to generate specific summarizations of the users' experience with the software and/or actionable steps that can be taken to improve the software. Third, these summarizations and/or steps are displayed on an administrative interface in a fashion that can readily be used determine how the software can be improved and by what means.
Accordingly, a first example embodiment may involve receiving, via a user interface, a plurality of textual user feedback regarding operation of a software application; aggregating, via a trained machine-learning model, the plurality of textual user feedback into a discrete number of observations regarding the operation of the software application, wherein the observations are in textual form; determining a subset of the observations that satisfy a relevance criterion; and providing the subset of the observations for display.
A second example embodiment may involve a non-transitory computer-readable medium, having stored thereon program instructions that, upon execution by a computing system, cause the computing system to perform operations in accordance with the first and/or second example embodiment.
In a third example embodiment, a computing system may include at least one processor, as well as memory and program instructions. The program instructions may be stored in the memory, and upon execution by the at least one processor, cause the computing system to perform operations in accordance with the first and/or second example embodiment.
In a fourth example embodiment, a system may include various means for carrying out each of the operations of the first and/or second example embodiment.
These, as well as other embodiments, aspects, advantages, and alternatives, will become apparent to those of ordinary skill in the art by reading the following detailed description, with reference where appropriate to the accompanying drawings. Further, this summary and other descriptions and figures provided herein are intended to illustrate embodiments by way of example only and, as such, that numerous variations are possible. For instance, structural elements and process steps can be rearranged, combined, distributed, eliminated, or otherwise changed, while remaining within the scope of the embodiments as claimed.
Example methods, devices, and systems are described herein. It should be understood that the words “example” and “exemplary” are used herein to mean “serving as an example, instance, or illustration.” Any embodiment or feature described herein as being an “example” or “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments or features unless stated as such. Thus, other embodiments can be utilized and other changes can be made without departing from the scope of the subject matter presented herein.
Accordingly, the example embodiments described herein are not meant to be limiting. It will be readily understood that the aspects of the present disclosure, as generally described herein, and illustrated in the figures, can be arranged, substituted, combined, separated, and designed in a wide variety of different configurations. For example, the separation of features into “client” and “server” components may occur in a number of ways.
Further, unless context suggests otherwise, the features illustrated in each of the figures may be used in combination with one another. Thus, the figures should be generally viewed as component aspects of one or more overall embodiments, with the understanding that not all illustrated features are necessary for each embodiment.
Additionally, any enumeration of elements, blocks, or steps in this specification or the claims is for purposes of clarity. Thus, such enumeration should not be interpreted to require or imply that these elements, blocks, or steps adhere to a particular arrangement or are carried out in a particular order.
A large enterprise is a complex entity with many interrelated operations. Some of these are found across the enterprise, such as human resources (HR), supply chain, information technology (IT), and finance. However, each enterprise also has its own unique operations that provide essential capabilities and/or create competitive advantages.
To support widely-implemented operations, enterprises typically use off-the-shelf software applications, such as customer relationship management (CRM) and human capital management (HCM) packages. However, they may also need custom software applications to meet their own unique requirements. A large enterprise often has dozens or hundreds of these custom software applications. Nonetheless, the advantages provided by the embodiments herein are not limited to large enterprises and may be applicable to an enterprise, or any other type of organization, of any size.
Many such software applications are developed by individual departments within the enterprise. These range from simple spreadsheets to custom-built software tools and databases. But the proliferation of siloed custom software applications has numerous disadvantages. It negatively impacts an enterprise's ability to run and grow its operations, innovate, and meet regulatory requirements. The enterprise may find it difficult to integrate, streamline, and enhance its operations due to lack of a single system that unifies its subsystems and data.
To efficiently create custom applications, enterprises would benefit from a remotely-hosted application platform that eliminates unnecessary development complexity. The goal of such a platform would be to reduce time-consuming, repetitive application development tasks so that software engineers and individuals in other roles can focus on developing unique, high-value features.
In order to achieve this goal, the concept of Application Platform as a Service (aPaaS) is introduced, to intelligently automate workflows throughout the enterprise. An aPaaS system is hosted remotely from the enterprise, but may access data, applications, and services within the enterprise by way of secure connections. Such an aPaaS system may have a number of advantageous capabilities and characteristics. These advantages and characteristics may be able to improve the enterprise's operations and workflows for IT, HR, CRM, customer service, application development, and security. Nonetheless, the embodiments herein are not limited to enterprise applications or environments, and can be more broadly applied.
The aPaaS system may support development and execution of model-view-controller (MVC) applications. MVC applications divide their functionality into three interconnected parts (model, view, and controller) in order to isolate representations of information from the manner in which the information is presented to the user, thereby allowing for efficient code reuse and parallel development. These applications may be web-based, and offer create, read, update, and delete (CRUD) capabilities. This allows new applications to be built on a common application infrastructure. In some cases, applications structured differently than MVC, such as those using unidirectional data flow, may be employed.
The aPaaS system may support standardized application components, such as a standardized set of widgets for graphical user interface (GUI) development. In this way, applications built using the aPaaS system have a common look and feel. Other software components and modules may be standardized as well. In some cases, this look and feel can be branded or skinned with an enterprise's custom logos and/or color schemes.
The aPaaS system may support the ability to configure the behavior of applications using metadata. This allows application behaviors to be rapidly adapted to meet specific needs. Such an approach reduces development time and increases flexibility. Further, the aPaaS system may support GUI tools that facilitate metadata creation and management, thus reducing errors in the metadata.
The aPaaS system may support clearly-defined interfaces between applications, so that software developers can avoid unwanted inter-application dependencies. Thus, the aPaaS system may implement a service layer in which persistent state information and other data are stored.
The aPaaS system may support a rich set of integration features so that the applications thereon can interact with legacy applications and third-party applications. For instance, the aPaaS system may support a custom employee-onboarding system that integrates with legacy HR, IT, and accounting systems.
The aPaaS system may support enterprise-grade security. Furthermore, since the aPaaS system may be remotely hosted, it should also utilize security procedures when it interacts with systems in the enterprise or third-party networks and services hosted outside of the enterprise. For example, the aPaaS system may be configured to share data amongst the enterprise and other parties to detect and identify common security threats.
Other features, functionality, and advantages of an aPaaS system may exist. This description is for purpose of example and is not intended to be limiting.
As an example of the aPaaS development process, a software developer may be tasked to create a new application using the aPaaS system. First, the developer may define the data model, which specifies the types of data that the application uses and the relationships therebetween. Then, via a GUI of the aPaaS system, the developer enters (e.g., uploads) the data model. The aPaaS system automatically creates all of the corresponding database tables, fields, and relationships, which can then be accessed via an object-oriented services layer.
In addition, the aPaaS system can also build a fully-functional application with client-side interfaces and server-side CRUD logic. This generated application may serve as the basis of further development for the user. Advantageously, the developer does not have to spend a large amount of time on basic application functionality. Further, since the application may be web-based, it can be accessed from any Internet-enabled client device. Alternatively or additionally, a local copy of the application may be able to be accessed, for instance, when Internet service is not available.
The aPaaS system may also support a rich set of pre-defined functionality that can be added to applications. These features include support for searching, email, templating, workflow design, reporting, analytics, social media, scripting, mobile-friendly output, and customized GUIs.
Such an aPaaS system may represent a GUI in various ways. For example, a server device of the aPaaS system may generate a representation of a GUI using a combination of HyperText Markup Language (HTML) and JAVASCRIPT®. The JAVASCRIPT® may include client-side executable code, server-side executable code, or both. The server device may transmit or otherwise provide this representation to a client device for the client device to display on a screen according to its locally-defined look and feel. Alternatively, a representation of a GUI may take other forms, such as an intermediate form (e.g., JAVA® byte-code) that a client device can use to directly generate graphical output therefrom. Other possibilities exist.
Further, user interaction with GUI elements, such as buttons, menus, tabs, sliders, checkboxes, toggles, etc. may be referred to as “selection”, “activation”, or “actuation” thereof. These terms may be used regardless of whether the GUI elements are interacted with by way of keyboard, pointing device, touchscreen, or another mechanism.
An aPaaS architecture is particularly powerful when integrated with an enterprise's network and used to manage such a network. The following embodiments describe architectural and functional aspects of example aPaaS systems, as well as the features and advantages thereof.
In this example, computing device 100 includes processor 102, memory 104, network interface 106, and input/output unit 108, all of which may be coupled by system bus 110 or a similar mechanism. In some embodiments, computing device 100 may include other components and/or peripheral devices (e.g., detachable storage, printers, and so on).
Processor 102 may be one or more of any type of computer processing element, such as a central processing unit (CPU), a co-processor (e.g., a mathematics, graphics, or encryption co-processor), a digital signal processor (DSP), a network processor, and/or a form of integrated circuit or controller that performs processor operations. In some cases, processor 102 may be one or more single-core processors. In other cases, processor 102 may be one or more multi-core processors with multiple independent processing units. Processor 102 may also include register memory for temporarily storing instructions being executed and related data, as well as cache memory for temporarily storing recently-used instructions and data.
Memory 104 may be any form of computer-usable memory, including but not limited to random access memory (RAM), read-only memory (ROM), and non-volatile memory (e.g., flash memory, hard disk drives, solid state drives, compact discs (CDs), digital video discs (DVDs), and/or tape storage). Thus, memory 104 represents both main memory units, as well as long-term storage. Other types of memory may include biological memory.
Memory 104 may store program instructions and/or data on which program instructions may operate. By way of example, memory 104 may store these program instructions on a non-transitory, computer-readable medium, such that the instructions are executable by processor 102 to carry out any of the methods, processes, or operations disclosed in this specification or the accompanying drawings.
As shown in
Network interface 106 may take the form of one or more wireline interfaces, such as Ethernet (e.g., Fast Ethernet, Gigabit Ethernet, and so on). Network interface 106 may also support communication over one or more non-Ethernet media, such as coaxial cables or power lines, or over wide-area media, such as Synchronous Optical Networking (SONET) or digital subscriber line (DSL) technologies. Network interface 106 may additionally take the form of one or more wireless interfaces, such as IEEE 802.11 (Wifi), BLUETOOTH®, global positioning system (GPS), or a wide-area wireless interface. However, other forms of physical layer interfaces and other types of standard or proprietary communication protocols may be used over network interface 106. Furthermore, network interface 106 may comprise multiple physical interfaces. For instance, some embodiments of computing device 100 may include Ethernet, BLUETOOTH®, and Wifi interfaces.
Input/output unit 108 may facilitate user and peripheral device interaction with computing device 100. Input/output unit 108 may include one or more types of input devices, such as a keyboard, a mouse, a touch screen, and so on. Similarly, input/output unit 108 may include one or more types of output devices, such as a screen, monitor, printer, and/or one or more light emitting diodes (LEDs). Additionally or alternatively, computing device 100 may communicate with other devices using a universal serial bus (USB) or high-definition multimedia interface (HDMI) port interface, for example.
In some embodiments, one or more computing devices like computing device 100 may be deployed to support an aPaaS architecture. The exact physical location, connectivity, and configuration of these computing devices may be unknown and/or unimportant to client devices. Accordingly, the computing devices may be referred to as “cloud-based” devices that may be housed at various remote data center locations.
For example, server devices 202 can be configured to perform various computing tasks of computing device 100. Thus, computing tasks can be distributed among one or more of server devices 202. To the extent that these computing tasks can be performed in parallel, such a distribution of tasks may reduce the total time to complete these tasks and return a result. For purposes of simplicity, both server cluster 200 and individual server devices 202 may be referred to as a “server device.” This nomenclature should be understood to imply that one or more distinct server devices, data storage devices, and cluster routers may be involved in server device operations.
Data storage 204 may be data storage arrays that include drive array controllers configured to manage read and write access to groups of hard disk drives and/or solid state drives. The drive array controllers, alone or in conjunction with server devices 202, may also be configured to manage backup or redundant copies of the data stored in data storage 204 to protect against drive failures or other types of failures that prevent one or more of server devices 202 from accessing units of data storage 204. Other types of memory aside from drives may be used.
Routers 206 may include networking equipment configured to provide internal and external communications for server cluster 200. For example, routers 206 may include one or more packet-switching and/or routing devices (including switches and/or gateways) configured to provide (i) network communications between server devices 202 and data storage 204 via local cluster network 208, and/or (ii) network communications between server cluster 200 and other devices via communication link 210 to network 212.
Additionally, the configuration of routers 206 can be based at least in part on the data communication requirements of server devices 202 and data storage 204, the latency and throughput of the local cluster network 208, the latency, throughput, and cost of communication link 210, and/or other factors that may contribute to the cost, speed, fault-tolerance, resiliency, efficiency, and/or other design goals of the system architecture.
As a possible example, data storage 204 may include any form of database, such as a structured query language (SQL) database. Various types of data structures may store the information in such a database, including but not limited to tables, arrays, lists, trees, and tuples. Furthermore, any databases in data storage 204 may be monolithic or distributed across multiple physical devices.
Server devices 202 may be configured to transmit data to and receive data from data storage 204. This transmission and retrieval may take the form of SQL queries or other types of database queries, and the output of such queries, respectively. Additional text, images, video, and/or audio may be included as well. Furthermore, server devices 202 may organize the received data into web page or web application representations. Such a representation may take the form of a markup language, such as HTML, the extensible Markup Language (XML), or some other standardized or proprietary format. Moreover, server devices 202 may have the capability of executing various types of computerized scripting languages, such as but not limited to Perl, Python, PHP Hypertext Preprocessor (PHP), Active Server Pages (ASP), JAVASCRIPT®, and so on. Computer program code written in these languages may facilitate the providing of web pages to client devices, as well as client device interaction with the web pages. Alternatively or additionally, JAVA® may be used to facilitate generation of web pages and/or to provide web application functionality.
Managed network 300 may be, for example, an enterprise network used by an entity for computing and communications tasks, as well as storage of data. Thus, managed network 300 may include client devices 302, server devices 304, routers 306, virtual machines 308, firewall 310, and/or proxy servers 312. Client devices 302 may be embodied by computing device 100, server devices 304 may be embodied by computing device 100 or server cluster 200, and routers 306 may be any type of router, switch, or gateway.
Virtual machines 308 may be embodied by one or more of computing device 100 or server cluster 200. In general, a virtual machine is an emulation of a computing system, and mimics the functionality (e.g., processor, memory, and communication resources) of a physical computer. One physical computing system, such as server cluster 200, may support up to thousands of individual virtual machines. In some embodiments, virtual machines 308 may be managed by a centralized server device or application that facilitates allocation of physical computing resources to individual virtual machines, as well as performance and error reporting. Enterprises often employ virtual machines in order to allocate computing resources in an efficient, as needed fashion. Providers of virtualized computing systems include VMWARE® and MICROSOFT®.
Firewall 310 may be one or more specialized routers or server devices that protect managed network 300 from unauthorized attempts to access the devices, applications, and services therein, while allowing authorized communication that is initiated from managed network 300. Firewall 310 may also provide intrusion detection, web filtering, virus scanning, application-layer gateways, and other applications or services. In some embodiments not shown in
Managed network 300 may also include one or more proxy servers 312. An embodiment of proxy servers 312 may be a server application that facilitates communication and movement of data between managed network 300, remote network management platform 320, and public cloud networks 340. In particular, proxy servers 312 may be able to establish and maintain secure communication sessions with one or more computational instances of remote network management platform 320. By way of such a session, remote network management platform 320 may be able to discover and manage aspects of the architecture and configuration of managed network 300 and its components.
Possibly with the assistance of proxy servers 312, remote network management platform 320 may also be able to discover and manage aspects of public cloud networks 340 that are used by managed network 300. While not shown in
Firewalls, such as firewall 310, typically deny all communication sessions that are incoming by way of Internet 350, unless such a session was ultimately initiated from behind the firewall (i.e., from a device on managed network 300) or the firewall has been explicitly configured to support the session. By placing proxy servers 312 behind firewall 310 (e.g., within managed network 300 and protected by firewall 310), proxy servers 312 may be able to initiate these communication sessions through firewall 310. Thus, firewall 310 might not have to be specifically configured to support incoming sessions from remote network management platform 320, thereby avoiding potential security risks to managed network 300.
In some cases, managed network 300 may consist of a few devices and a small number of networks. In other deployments, managed network 300 may span multiple physical locations and include hundreds of networks and hundreds of thousands of devices. Thus, the architecture depicted in
Furthermore, depending on the size, architecture, and connectivity of managed network 300, a varying number of proxy servers 312 may be deployed therein. For example, each one of proxy servers 312 may be responsible for communicating with remote network management platform 320 regarding a portion of managed network 300. Alternatively or additionally, sets of two or more proxy servers may be assigned to such a portion of managed network 300 for purposes of load balancing, redundancy, and/or high availability.
Remote network management platform 320 is a hosted environment that provides aPaaS services to users, particularly to the operator of managed network 300. These services may take the form of web-based portals, for example, using the aforementioned web-based technologies. Thus, a user can securely access remote network management platform 320 from, for example, client devices 302, or potentially from a client device outside of managed network 300. By way of the web-based portals, users may design, test, and deploy applications, generate reports, view analytics, and perform other tasks. Remote network management platform 320 may also be referred to as a multi-application platform.
As shown in
For example, managed network 300 may be an enterprise customer of remote network management platform 320, and may use computational instances 322, 324, and 326. The reason for providing multiple computational instances to one customer is that the customer may wish to independently develop, test, and deploy its applications and services. Thus, computational instance 322 may be dedicated to application development related to managed network 300, computational instance 324 may be dedicated to testing these applications, and computational instance 326 may be dedicated to the live operation of tested applications and services. A computational instance may also be referred to as a hosted instance, a remote instance, a customer instance, or by some other designation. Any application deployed onto a computational instance may be a scoped application, in that its access to databases within the computational instance can be restricted to certain elements therein (e.g., one or more particular database tables or particular rows within one or more database tables).
For purposes of clarity, the disclosure herein refers to the arrangement of application nodes, database nodes, aPaaS software executing thereon, and underlying hardware as a “computational instance.” Note that users may colloquially refer to the graphical user interfaces provided thereby as “instances.” But unless it is defined otherwise herein, a “computational instance” is a computing system disposed within remote network management platform 320.
The multi-instance architecture of remote network management platform 320 is in contrast to conventional multi-tenant architectures, over which multi-instance architectures exhibit several advantages. In multi-tenant architectures, data from different customers (e.g., enterprises) are comingled in a single database. While these customers' data are separate from one another, the separation is enforced by the software that operates the single database. As a consequence, a security breach in this system may affect all customers' data, creating additional risk, especially for entities subject to governmental, healthcare, and/or financial regulation. Furthermore, any database operations that affect one customer will likely affect all customers sharing that database. Thus, if there is an outage due to hardware or software errors, this outage affects all such customers. Likewise, if the database is to be upgraded to meet the needs of one customer, it will be unavailable to all customers during the upgrade process. Often, such maintenance windows will be long, due to the size of the shared database.
In contrast, the multi-instance architecture provides each customer with its own database in a dedicated computing instance. This prevents comingling of customer data, and allows each instance to be independently managed. For example, when one customer's instance experiences an outage due to errors or an upgrade, other computational instances are not impacted. Maintenance down time is limited because the database only contains one customer's data. Further, the simpler design of the multi-instance architecture allows redundant copies of each customer database and instance to be deployed in a geographically diverse fashion. This facilitates high availability, where the live version of the customer's instance can be moved when faults are detected or maintenance is being performed.
In some embodiments, remote network management platform 320 may include one or more central instances, controlled by the entity that operates this platform. Like a computational instance, a central instance may include some number of application and database nodes disposed upon some number of physical server devices or virtual machines. Such a central instance may serve as a repository for specific configurations of computational instances as well as data that can be shared amongst at least some of the computational instances. For instance, definitions of common security threats that could occur on the computational instances, software packages that are commonly discovered on the computational instances, and/or an application store for applications that can be deployed to the computational instances may reside in a central instance. Computational instances may communicate with central instances by way of well-defined interfaces in order to obtain this data.
In order to support multiple computational instances in an efficient fashion, remote network management platform 320 may implement a plurality of these instances on a single hardware platform. For example, when the aPaaS system is implemented on a server cluster such as server cluster 200, it may operate virtual machines that dedicate varying amounts of computational, storage, and communication resources to instances. But full virtualization of server cluster 200 might not be necessary, and other mechanisms may be used to separate instances. In some examples, each instance may have a dedicated account and one or more dedicated databases on server cluster 200. Alternatively, a computational instance such as computational instance 322 may span multiple physical devices.
In some cases, a single server cluster of remote network management platform 320 may support multiple independent enterprises. Furthermore, as described below, remote network management platform 320 may include multiple server clusters deployed in geographically diverse data centers in order to facilitate load balancing, redundancy, and/or high availability.
Public cloud networks 340 may be remote server devices (e.g., a plurality of server clusters such as server cluster 200) that can be used for outsourced computation, data storage, communication, and service hosting operations. These servers may be virtualized (i.e., the servers may be virtual machines). Examples of public cloud networks 340 may include Amazon AWS Cloud, Microsoft Azure Cloud (Azure), Google Cloud Platform (GCP), and IBM Cloud Platform. Like remote network management platform 320, multiple server clusters supporting public cloud networks 340 may be deployed at geographically diverse locations for purposes of load balancing, redundancy, and/or high availability.
Managed network 300 may use one or more of public cloud networks 340 to deploy applications and services to its clients and customers. For instance, if managed network 300 provides online music streaming services, public cloud networks 340 may store the music files and provide web interface and streaming capabilities. In this way, the enterprise of managed network 300 does not have to build and maintain its own servers for these operations.
Remote network management platform 320 may include modules that integrate with public cloud networks 340 to expose virtual machines and managed services therein to managed network 300. The modules may allow users to request virtual resources, discover allocated resources, and provide flexible reporting for public cloud networks 340. In order to establish this functionality, a user from managed network 300 might first establish an account with public cloud networks 340, and request a set of associated resources. Then, the user may enter the account information into the appropriate modules of remote network management platform 320. These modules may then automatically discover the manageable resources in the account, and also provide reports related to usage, performance, and billing.
Internet 350 may represent a portion of the global Internet. However, Internet 350 may alternatively represent a different type of network, such as a private wide-area or local-area packet-switched network.
In data center 400A, network traffic to and from external devices flows either through VPN gateway 402A or firewall 404A. VPN gateway 402A may be peered with VPN gateway 412 of managed network 300 by way of a security protocol such as Internet Protocol Security (IPSEC) or Transport Layer Security (TLS). Firewall 404A may be configured to allow access from authorized users, such as user 414 and remote user 416, and to deny access to unauthorized users. By way of firewall 404A, these users may access computational instance 322, and possibly other computational instances. Load balancer 406A may be used to distribute traffic amongst one or more physical or virtual server devices that host computational instance 322. Load balancer 406A may simplify user access by hiding the internal configuration of data center 400A, (e.g., computational instance 322) from client devices. For instance, if computational instance 322 includes multiple physical or virtual computing devices that share access to multiple databases, load balancer 406A may distribute network traffic and processing tasks across these computing devices and databases so that no one computing device or database is significantly busier than the others. In some embodiments, computational instance 322 may include VPN gateway 402A, firewall 404A, and load balancer 406A.
Data center 400B may include its own versions of the components in data center 400A. Thus, VPN gateway 402B, firewall 404B, and load balancer 406B may perform the same or similar operations as VPN gateway 402A, firewall 404A, and load balancer 406A, respectively. Further, by way of real-time or near-real-time database replication and/or other operations, computational instance 322 may exist simultaneously in data centers 400A and 400B.
Data centers 400A and 400B as shown in
Should data center 400A fail in some fashion or otherwise become unavailable to users, data center 400B can take over as the active data center. For example, domain name system (DNS) servers that associate a domain name of computational instance 322 with one or more Internet Protocol (IP) addresses of data center 400A may re-associate the domain name with one or more IP addresses of data center 400B. After this re-association completes (which may take less than one second or several seconds), users may access computational instance 322 by way of data center 400B.
As stored or transmitted, a configuration item may be a list of attributes that characterize the hardware or software that the configuration item represents. These attributes may include manufacturer, vendor, location, owner, unique identifier, description, network address, operational status, serial number, time of last update, and so on. The class of a configuration item may determine which subset of attributes are present for the configuration item (e.g., software and hardware configuration items may have different lists of attributes).
As noted above, VPN gateway 412 may provide a dedicated VPN to VPN gateway 402A. Such a VPN may be helpful when there is a significant amount of traffic between managed network 300 and computational instance 322, or security policies otherwise suggest or require use of a VPN between these sites. In some embodiments, any device in managed network 300 and/or computational instance 322 that directly communicates via the VPN is assigned a public IP address. Other devices in managed network 300 and/or computational instance 322 may be assigned private IP addresses (e.g., IP addresses selected from the 10.0.0.0-10.255.255.255 or 192.168.0.0-192.168.255.255 ranges, represented in shorthand as subnets 10.0.0.0/8 and 192.168.0.0/16, respectively). In various alternatives, devices in managed network 300, such as proxy servers 312, may use a secure protocol (e.g., TLS) to communicate directly with one or more data centers.
In order for remote network management platform 320 to administer the devices, applications, and services of managed network 300, remote network management platform 320 may first determine what devices are present in managed network 300, the configurations, constituent components, and operational statuses of these devices, and the applications and services provided by the devices. Remote network management platform 320 may also determine the relationships between discovered devices, their components, applications, and services. Representations of each device, component, application, and service may be referred to as a configuration item. The process of determining the configuration items and relationships within managed network 300 is referred to as discovery, and may be facilitated at least in part by proxy servers 312. Representations of configuration items and relationships are stored in a CMDB.
While this section describes discovery conducted on managed network 300, the same or similar discovery procedures may be used on public cloud networks 340. Thus, in some environments, “discovery” may refer to discovering configuration items and relationships on a managed network and/or one or more public cloud networks.
For purposes of the embodiments herein, an “application” may refer to one or more processes, threads, programs, client software modules, server software modules, or any other software that executes on a device or group of devices. A “service” may refer to a high-level capability provided by one or more applications executing on one or more devices working in conjunction with one another. For example, a web service may involve multiple web application server threads executing on one device and accessing information from a database application that executes on another device.
In
As discovery takes place, computational instance 322 may store discovery tasks (jobs) that proxy servers 312 are to perform in task list 502, until proxy servers 312 request these tasks in batches of one or more. Placing the tasks in task list 502 may trigger or otherwise cause proxy servers 312 to begin their discovery operations. For example, proxy servers 312 may poll task list 502 periodically or from time to time, or may be notified of discovery commands in task list 502 in some other fashion. Alternatively or additionally, discovery may be manually triggered or automatically triggered based on triggering events (e.g., discovery may automatically begin once per day at a particular time).
Regardless, computational instance 322 may transmit these discovery commands to proxy servers 312 upon request. For example, proxy servers 312 may repeatedly query task list 502, obtain the next task therein, and perform this task until task list 502 is empty or another stopping condition has been reached. In response to receiving a discovery command, proxy servers 312 may query various devices, components, applications, and/or services in managed network 300 (represented for sake of simplicity in
IRE 514 may be a software module that removes discovery information from task list 502 and formulates this discovery information into configuration items (e.g., representing devices, components, applications, and/or services discovered on managed network 300) as well as relationships therebetween. Then, IRE 514 may provide these configuration items and relationships to CMDB 500 for storage therein. The operation of IRE 514 is described in more detail below.
In this fashion, configuration items stored in CMDB 500 represent the environment of managed network 300. As an example, these configuration items may represent a set of physical and/or virtual devices (e.g., client devices, server devices, routers, or virtual machines), applications executing thereon (e.g., web servers, email servers, databases, or storage arrays), as well as services that involve multiple individual configuration items. Relationships may be pairwise definitions of arrangements or dependencies between configuration items.
In order for discovery to take place in the manner described above, proxy servers 312, CMDB 500, and/or one or more credential stores may be configured with credentials for the devices to be discovered. Credentials may include any type of information needed in order to access the devices. These may include userid/password pairs, certificates, and so on. In some embodiments, these credentials may be stored in encrypted fields of CMDB 500. Proxy servers 312 may contain the decryption key for the credentials so that proxy servers 312 can use these credentials to log on to or otherwise access devices being discovered.
There are two general types of discovery-horizontal and vertical (top-down). Each are discussed below.
Horizontal discovery is used to scan managed network 300, find devices, components, and/or applications, and then populate CMDB 500 with configuration items representing these devices, components, and/or applications. Horizontal discovery also creates relationships between the configuration items. For instance, this could be a “runs on” relationship between a configuration item representing a software application and a configuration item representing a server device on which it executes. Typically, horizontal discovery is not aware of services and does not create relationships between configuration items based on the services in which they operate.
There are two versions of horizontal discovery. One relies on probes and sensors, while the other also employs patterns. Probes and sensors may be scripts (e.g., written in JAVASCRIPT®) that collect and process discovery information on a device and then update CMDB 500 accordingly. More specifically, probes explore or investigate devices on managed network 300, and sensors parse the discovery information returned from the probes.
Patterns are also scripts that collect data on one or more devices, process it, and update the CMDB. Patterns differ from probes and sensors in that they are written in a specific discovery programming language and are used to conduct detailed discovery procedures on specific devices, components, and/or applications that often cannot be reliably discovered (or discovered at all) by more general probes and sensors. Particularly, patterns may specify a series of operations that define how to discover a particular arrangement of devices, components, and/or applications, what credentials to use, and which CMDB tables to populate with configuration items resulting from this discovery.
Both versions may proceed in four logical phases: scanning, classification, identification, and exploration. Also, both versions may require specification of one or more ranges of IP addresses on managed network 300 for which discovery is to take place. Each phase may involve communication between devices on managed network 300 and proxy servers 312, as well as between proxy servers 312 and task list 502. Some phases may involve storing partial or preliminary configuration items in CMDB 500, which may be updated in a later phase.
In the scanning phase, proxy servers 312 may probe each IP address in the specified range(s) of IP addresses for open Transmission Control Protocol (TCP) and/or User Datagram Protocol (UDP) ports to determine the general type of device and its operating system. The presence of such open ports at an IP address may indicate that a particular application is operating on the device that is assigned the IP address, which in turn may identify the operating system used by the device. For example, if TCP port 135 is open, then the device is likely executing a WINDOWS® operating system. Similarly, if TCP port 22 is open, then the device is likely executing a UNIX® operating system, such as LINUX®. If UDP port 161 is open, then the device may be able to be further identified through the Simple Network Management Protocol (SNMP). Other possibilities exist.
In the classification phase, proxy servers 312 may further probe each discovered device to determine the type of its operating system. The probes used for a particular device are based on information gathered about the devices during the scanning phase. For example, if a device is found with TCP port 22 open, a set of UNIX®-specific probes may be used. Likewise, if a device is found with TCP port 135 open, a set of WINDOWS®-specific probes may be used. For either case, an appropriate set of tasks may be placed in task list 502 for proxy servers 312 to carry out. These tasks may result in proxy servers 312 logging on, or otherwise accessing information from the particular device. For instance, if TCP port 22 is open, proxy servers 312 may be instructed to initiate a Secure Shell (SSH) connection to the particular device and obtain information about the specific type of operating system thereon from particular locations in the file system. Based on this information, the operating system may be determined. As an example, a UNIX® device with TCP port 22 open may be classified as AIX®, HPUX, LINUX®, MACOS®, or SOLARIS®. This classification information may be stored as one or more configuration items in CMDB 500.
In the identification phase, proxy servers 312 may determine specific details about a classified device. The probes used during this phase may be based on information gathered about the particular devices during the classification phase. For example, if a device was classified as LINUX®, a set of LINUX®-specific probes may be used. Likewise, if a device was classified as WINDOWS® 10, as a set of WINDOWS®-10-specific probes may be used. As was the case for the classification phase, an appropriate set of tasks may be placed in task list 502 for proxy servers 312 to carry out. These tasks may result in proxy servers 312 reading information from the particular device, such as basic input/output system (BIOS) information, serial numbers, network interface information, media access control address(es) assigned to these network interface(s), IP address(es) used by the particular device and so on. This identification information may be stored as one or more configuration items in CMDB 500 along with any relevant relationships therebetween. Doing so may involve passing the identification information through IRE 514 to avoid generation of duplicate configuration items, for purposes of disambiguation, and/or to determine the table(s) of CMDB 500 in which the discovery information should be written.
In the exploration phase, proxy servers 312 may determine further details about the operational state of a classified device. The probes used during this phase may be based on information gathered about the particular devices during the classification phase and/or the identification phase. Again, an appropriate set of tasks may be placed in task list 502 for proxy servers 312 to carry out. These tasks may result in proxy servers 312 reading additional information from the particular device, such as processor information, memory information, lists of running processes (software applications), and so on. Once more, the discovered information may be stored as one or more configuration items in CMDB 500, as well as relationships.
Running horizontal discovery on certain devices, such as switches and routers, may utilize SNMP. Instead of or in addition to determining a list of running processes or other application-related information, discovery may determine additional subnets known to a router and the operational state of the router's network interfaces (e.g., active, inactive, queue length, number of packets dropped, etc.). The IP addresses of the additional subnets may be candidates for further discovery procedures. Thus, horizontal discovery may progress iteratively or recursively.
Patterns are used only during the identification and exploration phases-under pattern-based discovery, the scanning and classification phases operate as they would if probes and sensors are used. After the classification stage completes, a pattern probe is specified as a probe to use during identification. Then, the pattern probe and the pattern that it specifies are launched.
Patterns support a number of features, by way of the discovery programming language, that are not available or difficult to achieve with discovery using probes and sensors. For example, discovery of devices, components, and/or applications in public cloud networks, as well as configuration file tracking, is much simpler to achieve using pattern-based discovery. Further, these patterns are more easily customized by users than probes and sensors. Additionally, patterns are more focused on specific devices, components, and/or applications and therefore may execute faster than the more general approaches used by probes and sensors.
Once horizontal discovery completes, a configuration item representation of each discovered device, component, and/or application is available in CMDB 500. For example, after discovery, operating system version, hardware configuration, and network configuration details for client devices, server devices, and routers in managed network 300, as well as applications executing thereon, may be stored as configuration items. This collected information may be presented to a user in various ways to allow the user to view the hardware composition and operational status of devices.
Furthermore, CMDB 500 may include entries regarding the relationships between configuration items. More specifically, suppose that a server device includes a number of hardware components (e.g., processors, memory, network interfaces, storage, and file systems), and has several software applications installed or executing thereon. Relationships between the components and the server device (e.g., “contained by” relationships) and relationships between the software applications and the server device (e.g., “runs on” relationships) may be represented as such in CMDB 500.
More generally, the relationship between a software configuration item installed or executing on a hardware configuration item may take various forms, such as “is hosted on”, “runs on”, or “depends on”. Thus, a database application installed on a server device may have the relationship “is hosted on” with the server device to indicate that the database application is hosted on the server device. In some embodiments, the server device may have a reciprocal relationship of “used by” with the database application to indicate that the server device is used by the database application. These relationships may be automatically found using the discovery procedures described above, though it is possible to manually set relationships as well.
In this manner, remote network management platform 320 may discover and inventory the hardware and software deployed on and provided by managed network 300.
Vertical discovery is a technique used to find and map configuration items that are part of an overall service, such as a web service. For example, vertical discovery can map a web service by showing the relationships between a web server application, a LINUX® server device, and a database that stores the data for the web service. Typically, horizontal discovery is run first to find configuration items and basic relationships therebetween, and then vertical discovery is run to establish the relationships between configuration items that make up a service.
Patterns can be used to discover certain types of services, as these patterns can be programmed to look for specific arrangements of hardware and software that fit a description of how the service is deployed. Alternatively or additionally, traffic analysis (e.g., examining network traffic between devices) can be used to facilitate vertical discovery. In some cases, the parameters of a service can be manually configured to assist vertical discovery.
In general, vertical discovery seeks to find specific types of relationships between devices, components, and/or applications. Some of these relationships may be inferred from configuration files. For example, the configuration file of a web server application can refer to the IP address and port number of a database on which it relies. Vertical discovery patterns can be programmed to look for such references and infer relationships therefrom. Relationships can also be inferred from traffic between devices—for instance, if there is a large extent of web traffic (e.g., TCP port 80 or 8080) traveling between a load balancer and a device hosting a web server, then the load balancer and the web server may have a relationship.
Relationships found by vertical discovery may take various forms. As an example, an email service may include an email server software configuration item and a database application software configuration item, each installed on different hardware device configuration items. The email service may have a “depends on” relationship with both of these software configuration items, while the software configuration items have a “used by” reciprocal relationship with the email service. Such services might not be able to be fully determined by horizontal discovery procedures, and instead may rely on vertical discovery and possibly some extent of manual configuration.
Regardless of how discovery information is obtained, it can be valuable for the operation of a managed network. Notably, IT personnel can quickly determine where certain software applications are deployed, and what configuration items make up a service. This allows for rapid pinpointing of root causes of service outages or degradation. For example, if two different services are suffering from slow response times, the CMDB can be queried (perhaps among other activities) to determine that the root cause is a database application that is used by both services having high processor utilization. Thus, IT personnel can address the database application rather than waste time considering the health and performance of other configuration items that make up the services.
In another example, suppose that a database application is executing on a server device, and that this database application is used by an employee onboarding service as well as a payroll service. Thus, if the server device is taken out of operation for maintenance, it is clear that the employee onboarding service and payroll service will be impacted. Likewise, the dependencies and relationships between configuration items may be able to represent the services impacted when a particular hardware device fails.
In general, configuration items and/or relationships between configuration items may be displayed on a web-based interface and represented in a hierarchical fashion. Modifications to such configuration items and/or relationships in the CMDB may be accomplished by way of this interface.
Furthermore, users from managed network 300 may develop workflows that allow certain coordinated activities to take place across multiple discovered devices. For instance, an IT workflow might allow the user to change the common administrator password to all discovered LINUX® devices in a single operation.
A CMDB, such as CMDB 500, provides a repository of configuration items and relationships. When properly provisioned, it can take on a key role in higher-layer applications deployed within or involving a computational instance. These applications may relate to enterprise IT service management, operations management, asset management, configuration management, compliance, and so on.
For example, an IT service management application may use information in the CMDB to determine applications and services that may be impacted by a component (e.g., a server device) that has malfunctioned, crashed, or is heavily loaded. Likewise, an asset management application may use information in the CMDB to determine which hardware and/or software components are being used to support particular enterprise applications. As a consequence of the importance of the CMDB, it is desirable for the information stored therein to be accurate, consistent, and up to date.
A CMDB may be populated in various ways. As discussed above, a discovery procedure may automatically store information including configuration items and relationships in the CMDB. However, a CMDB can also be populated, as a whole or in part, by manual entry, configuration files, and third-party data sources. Given that multiple data sources may be able to update the CMDB at any time, it is possible that one data source may overwrite entries of another data source. Also, two data sources may each create slightly different entries for the same configuration item, resulting in a CMDB containing duplicate data. When either of these occurrences takes place, they can cause the health and utility of the CMDB to be reduced.
In order to mitigate this situation, these data sources might not write configuration items directly to the CMDB. Instead, they may write to an identification and reconciliation application programming interface (API) of IRE 514. Then, IRE 514 may use a set of configurable identification rules to uniquely identify configuration items and determine whether and how they are to be written to the CMDB.
In general, an identification rule specifies a set of configuration item attributes that can be used for this unique identification. Identification rules may also have priorities so that rules with higher priorities are considered before rules with lower priorities. Additionally, a rule may be independent, in that the rule identifies configuration items independently of other configuration items. Alternatively, the rule may be dependent, in that the rule first uses a metadata rule to identify a dependent configuration item.
Metadata rules describe which other configuration items are contained within a particular configuration item, or the host on which a particular configuration item is deployed. For example, a network directory service configuration item may contain a domain controller configuration item, while a web server application configuration item may be hosted on a server device configuration item.
A goal of each identification rule is to use a combination of attributes that can unambiguously distinguish a configuration item from all other configuration items, and is expected not to change during the lifetime of the configuration item. Some possible attributes for an example server device may include serial number, location, operating system, operating system version, memory capacity, and so on. If a rule specifies attributes that do not uniquely identify the configuration item, then multiple components may be represented as the same configuration item in the CMDB. Also, if a rule specifies attributes that change for a particular configuration item, duplicate configuration items may be created.
Thus, when a data source provides information regarding a configuration item to IRE 514, IRE 514 may attempt to match the information with one or more rules. If a match is found, the configuration item is written to the CMDB or updated if it already exists within the CMDB. If a match is not found, the configuration item may be held for further analysis.
Configuration item reconciliation procedures may be used to ensure that only authoritative data sources are allowed to overwrite configuration item data in the CMDB. This reconciliation may also be rules-based. For instance, a reconciliation rule may specify that a particular data source is authoritative for a particular configuration item type and set of attributes. Then, IRE 514 might only permit this authoritative data source to write to the particular configuration item, and writes from unauthorized data sources may be prevented. Thus, the authorized data source becomes the single source of truth regarding the particular configuration item. In some cases, an unauthorized data source may be allowed to write to a configuration item if it is creating the configuration item or the attributes to which it is writing are empty.
Additionally, multiple data sources may be authoritative for the same configuration item or attributes thereof. To avoid ambiguities, these data sources may be assigned precedences that are taken into account during the writing of configuration items. For example, a secondary authorized data source may be able to write to a configuration item's attribute until a primary authorized data source writes to this attribute. Afterward, further writes to the attribute by the secondary authorized data source may be prevented.
In some cases, duplicate configuration items may be automatically detected by IRE 514 or in another fashion. These configuration items may be deleted or flagged for manual de-duplication.
A large-scale multiuser computing system, such as remote network management platform 320, may facilitate the operation of hundreds of applications by tens of thousands of users. Each application may consist of some number of discrete features or functions, such as web interfaces, database tables, communication interfaces to external services, and program logic that integrates the operation of these features and functions.
It is desirable for software engineers to be able to obtain user feedback, either on an application basis or a feature basis. In this manner, the software engineers can determine whether and to what extent the applications are operating as expected. With such feedback, the software engineers also determine new features to implement, upgrades to existing features, and other improvements to the applications. Consequently, the feedback can be used to improve software application quality (e.g., increase functionality, increase ease of use, decrease processing and memory requirements, and so on).
In some cases, users may provide feedback on software applications using a fixed scale. One such fixed scale might be numbers in the range of 1 to 10, where a 1 indicates the most negative possible feedback, a 10 indicates the most positive possible feedback, and the numbers in between indicate proportionally less extreme feedback. Another such fixed scale might be a star-rating system in the range of 1 to 5 stars, where 1 star indicates the most negative possible feedback, 5 stars indicates the most positive possible feedback, and 2, 3, and 4 stars proportionally less extreme feedback. Other types of fixed-scale feedback mechanisms may exist.
A notable problem with these fixed-scale systems is that they fail to inform the software engineers as to why users like or dislike an application or feature. For instance, an application that has an average feedback level of 4 stars might be considered to be “better” than another application that has an average feedback level of only 3 stars. But the software engineers are given no insight into what behaviors of the application are viewed positively or negatively. Further, fixed-scale feedback can be fundamentally inaccurate in some cases (e.g., when users rate the software highly because they are able to use it for free even though it does not meet all of their needs).
To avoid the drawbacks of relying solely on fixed-scale feedback, users may be prompted to provide free-form feedback. An example of this might be a graphical user interface with a popup window or sidebar that asks the user to type their feedback into a text box. Such free-form feedback has the advantage of being able to provide more a useful and granular response that can define exactly what aspects of the software the user considers to be positive or negative. However, free-form feedback from multiple users cannot easily be aggregated or quantified as is the case for fixed-scale feedback. Therefore, more sophisticated techniques should be used to determine the meaning of free-form feedback and to summarize this meaning in a fashion that is useful for software engineers.
The embodiments herein provide a system for distributed feedback that can include several modules. A customizable feedback component for a user interface allows users to be queried for feedback on the application that they are currently using. The feedback may use free-form input and possibly fixed-scale input as well. This feedback may be given to a supervised or unsupervised machine learning model that performs similarity determination, clustering, or sentiment analysis on the feedback to generate specific summarizations of the users' experience with the software and/or actionable steps that can be taken to improve the software. These summarizations and/or steps are displayed on a feedback portal user interface in a fashion that can readily be used determine how the software can be improved and by what means.
These modules are depicted in
User workspace 600 represents a user-facing interface (e.g., a graphical user interface) that can be configured to support feedback-connected application 602. This application may include, among other modules that facilitate its operation, API layer 604 and feedback UI component 606. API layer 604 provides communication services between feedback UI component 606 and feedback configuration and data store 610. These services may include the temporary storage of data as well as transformation of this data between various formats. Feedback UI component 606 may be a part of a user interface (e.g., a pane, column, popup window, or overlay on a graphical user interface) that prompts a user to provide feedback, and collects the feedback provided. Feedback UI component 606 may display prompts for feedback in various ways, some of which are described below. Notably, there may be a different instance of feedback UI component 606 defined for each aspect of a user interface (e.g., each web page or screen) for which feedback is sought.
API layer 604 may provide bindings between the application and feedback UI component 606 and/or bindings between questions displayed on feedback UI component 606 and any respective responses received from users. API layer 604 may also receive feedback in the form of payloads from feedback UI component 606. These payloads may encode the feedback in a fixed-scale format (e.g., numerical or alphanumerical) or text strings. Further, API layer 604 may communicate with feedback configuration and data store 610 by way of a representational state transfer (REST) interface (e.g., using GET, PUT, and POST commands not unlike HTTP).
Feedback configuration and data store 610 may be configured to store per-component questions 612, per-question options 614, feedback responses 616, and processed feedback responses 618. Per-component questions 612 may include one or more pre-defined questions for each instance of feedback UI component 606. As noted, these questions may use fixed scales and/or free-form text, for example. Per-question options 614 capture the form of this feedback, defining the response options for fixed-scale feedback or indicating that one or more questions are free-form. Feedback responses 616 may be a repository that stores feedback received from users. Processed feedback responses 618 may be a repository that stores the result of applying trained machine learning model 630 to feedback responses 616.
Feedback configuration and data store 610 may be implemented in the form of one or more databases and/or one or more database tables. When displaying an instance of feedback UI component 606, feedback-connected application 602 may use API layer 604 to obtain questions from per-component questions 612 and their response options from per-question options 614. These may be displayed on feedback UI component 606. User feedback received by way of feedback UI component 606 may be stored in feedback responses 616.
Administrator feedback portal 620 may take the form of a web page or web site that allows an administrative user to select one or more instances of feedback UI component 606 or feedback-connected application 602 and responsively display the associated feedback. Thus, administrator feedback portal 620 may query feedback configuration and data store 610 for one or more of per-component questions 612, per-question options 614, feedback responses 616, and processed feedback responses 618. Administrator feedback portal 620 may present this information in various ways, some of which are described below.
Trained machine learning model 630 may be a natural language processing model that has been trained to classify, cluster, determine similarity between, determine the sentiments of, and/or summarize feedback responses 616. Thus, feedback configuration and data store 610 may transmit feedback responses 616 (with or without information from per-component questions 612 and/or per-question options 614) to trained machine learning model 630 and store the results in processed feedback responses 618. Trained machine learning model 630 may take the form of one or more artificial intelligence constructs, such as artificial neural networks, decision trees, supervised or unsupervised clustering techniques, transformer-based architectures, expert systems, and so on.
Customizable feedback UI component 700 may be a pane, column, popup window, or overlay on a graphical user interface displayed by feedback UI component 606. Customizable feedback UI component 700 may present one or more customized questions to a user in accordance with definitions from per-component questions 612 and per-question options 614.
As an example, customizable feedback UI component 700 includes three different types of questions. Section 702 asks the user to rate a software module using a fixed scale of one to five stars. Section 704 asks the user whether the module has addressed their needs, and allows the user to select one option from a pre-determined list (which may be considered another type of fixed-scale feedback). Section 706 asks the user to describe their experiences with the module using free-form text entered into text box 708.
In various situations, more or fewer questions may be asked, any of which may be fixed scale, free-form, or of some other modality. Additionally, customizable feedback UI component 700 may make one or more of these questions mandatory or optional for the user to answer. Mandatory questions must be answered before the user can progress in the application, while optional questions need not be answered. Regardless, answers to these questions may be provided to feedback configuration and data store 610 for storage in feedback responses 616. Notably, at least free-form text-based feedback may be further processed by trained machine learning model 630.
The embodiments herein may use or rely upon various types of natural language processing techniques and models applied to strings of text that make up free-form text-based feedback stored in feedback responses 616. This section describes some possible natural language processing that can be used on feedback responses 616. Thus, any of these techniques, or other techniques, can be incorporated into the embodiments herein in various arrangements. Notably, such techniques are desirable because the amount of user feedback can be vast, often reaching hundreds of thousands of samples or more.
In particular, natural language processing may employ one or more types of machine-learning language models. These models may utilize the classification and/or clustering techniques described below to facilitate various aspects of natural language understanding, such as sentiment analysis or similarity. But other machine-learning-based techniques may be used. Further, there can be overlap between the functionality of these techniques (e.g., clustering techniques can be used for classification or similarity operations).
Machine-learning techniques can include determining word and/or paragraph vectors from samples of text by artificial neural networks (ANNs), large language models, sentiment analysis mechanisms, and/or other deep learning procedures. These techniques are used to determine a similarity value between samples of text, to group multiple samples of text together according to topic or content, to partition a sample of text into discrete internally-related segments, to determine statistical associations between words, to determine the contextual sentiment of text, to summarize text, or to perform some other language processing task.
A word vector may be determined for each word present in a corpus of textual records such that words having similar meanings (or semantic content) are associated with vectors that are near one other within a semantically encoded vector space. Such vectors may have dozens, hundreds, or more elements and thus may be in an m-space where m is a number of dimensions. These word vectors allow the underlying meanings of words to be compared or otherwise operated on by a computing device (e.g., by determining a distance, a cosine similarity, or some other measure of similarity between the word vectors). Accordingly, the use of word vectors may allow for a significant improvement over simpler word list or word matrix methods. These models also have the benefit of being adapted to the vocabulary, topics, and idiomatic word use common in their intended semantic area of focus.
Additionally or alternatively, the word vectors may be provided as input to an ANN, a support vector machine, a decision tree, or some other machine-learning algorithm in order to perform sentiment analysis, to classify or cluster samples of text, to determine a level of similarity between samples of text, or to perform some other language processing task.
Despite the usefulness of word vectors, the complete semantic meaning of a sentence or other passage (e.g., a phrase, several sentences, a paragraph, a text segment within a larger sample of text, or a document) cannot always be captured from the individual word vectors of a sentence (e.g., by applying vector algebra). Word vectors represent the semantic content of individual words and may be trained using short context windows. Thus, the semantic content of word order and any information outside the short context window is lost when operating based only on word vectors.
Similar to the methods above for learning word vectors, an ANN or other machine-learning models may be trained using a large number of paragraphs in a corpus to determine the contextual meaning of entire paragraphs, sentences, phrases, or other multi-word text samples as well as to determine the meaning of the individual words that make up the paragraphs in the corpus. For example, for each paragraph in a corpus, an ANN can be trained with fixed-length contexts generated from moving a sliding window over the paragraph. Thus, a given paragraph vector is shared across all training contexts created from its source paragraph, but not across training contexts created from other paragraphs.
Word vectors and paragraph vectors are two approaches for training an ANN model to represent the sematic meanings of words. Variants of these techniques, e.g., using continuous bag of words, skip-gram, paragraph vector-distributed memory, or paragraph vector-distributed bag of words, may also be used.
LLMs are machine-learning constructs that can be trained on vast amounts of textual data, such as books, articles, and websites, to learn patterns and relationships between words and phrases. Some examples of LLMs include GPT-4, bidirectional encoder representations from transformers (BERT), language model for dialogue applications (LaMDA), and Transformer-XL. LLMs can perform a wide range of natural language processing tasks, such as summarization, text classification, question answering, and language translation. These LLMs also have the ability to create coherent and human-like text. Many LLMs are based on the transformer architecture, which employs self-attention when considering different parts of an input sequence (e.g., of tokens such as words) to compute a representation of each element in the sequence taking long-range dependence between elements into account.
The above techniques can be used for sentiment analysis of a string of text. The result of the sentiment analysis could be a list of one or more sentiments found in the text (e.g., happy, angry, confused) and respective confidence levels thereof (e.g., a number in the range of 0.0 to 1.0 where higher values indicate more confidence). Other techniques may be employed, such as lexicons mapping words or phrases to sentiments, word frequency counts, or any other technique that can classify text into one or more categories, such as positive, negative, or neutral categories. Further, these techniques may be combined with one another or with other techniques.
As an example relevant to the embodiments herein, vector models can be trained using word vector, paragraph vector, or LLM techniques for example. To that point, trained vector model 800 in
Accordingly, trained similarity model 802 takes a vector representation of input text and finds zero or more similar units of text from a set or a database of such text—i.e., records in feedback responses 616 or elsewhere that contain similar text. The degree of similarity between two units of text can be determined by calculating a similarity measurement between their respective vector representations. One such measurement may be based on cosine similarity, which is defined by the following equations:
In these equations, vector A could represent one input vector and vector B could represent another input vector, each of which could be derived from different input texts, for example. Vector A and vector B could both be of dimension m. The similarity calculation may have an output a number between −1.0 and +1.0, where the closer this result is to +1.0, the more similar vectors A and B are to each other.
Thus, the similar texts produced by trained similarity model 802 may be those with vector representations for which the respective cosine similarities with the vector representation of the input text are above a threshold value (e.g., 0.2, 0.3, 0.5, or 0.8). Alternatively, the output of similar texts may be a certain number of texts (or identifiers for the certain number of input texts) for which the respective cosine similarities with the input vector representation of the input text are the most similar.
The similarity calculations described above may also be used to cluster similar texts. Such clustering may be performed to provide a variety of benefits. For example, clustering may be applied to a set of input texts in order to identify patterns or groups within the set of texts that have relevance to a particular semantic meaning or intent. Such groups may facilitate the identification and/or classification of input text based on such meanings or intents.
Clustering may be performed in an unsupervised manner in order to generate clusters without the requirement of manually-labeled input texts, to identify previously unidentified clusters within the input texts, or to provide some other benefit. A variety of methods and/or machine-learning techniques could be applied to identify clusters within a set of input texts and/or to assign input texts (e.g., newly received input texts) to already-identified clusters. For example, decision trees, ANNs, k-means, support vector machines, independent component analysis, principal component analysis, or some other method could be trained based on a set of available input texts in order to generate a machine-learning model that classifies available texts and/or input texts not present in the training set of available texts.
For instance, clusters may be identified, for example, to include vector representations that are within a particular extent of similarity from one another, or not more than a particular Euclidian distance from a centroid in m-space. In these models, some outlying vector representations may remain un-clustered.
Once a machine-learning model has been determined, the machine-learning model can be applied to assign additional input texts to the identified clusters represented by the machine-learning model and/or to assign input texts to a set of residual input texts. The machine-learning model could include parameter values, neural network hyperparameters, cluster centroid locations in feature space, cluster boundary locations in feature space, threshold similarity values, or other information used, by the machine-learning model, to determine which cluster to assign an input texts and/or to determine that the input texts should not be assigned to a cluster (e.g., should be stored in a set of residual, unassigned input texts). Such information could define a region, within a feature space, that corresponds to each cluster. That is, the information in the machine-learning model could be such that the machine-learning model assigns an input text to a particular cluster if the features of the input text correspond to a location, within the feature space, that is inside the defined region for the particular cluster. The defined regions could be closed (being fully enclosed by a boundary) or open (having one or more boundaries but extending infinitely outward in one or more directions in the feature space).
Trained clustering model 804 depicts such an arrangement in general. Particularly, trained clustering model 804 takes a vector representation of input text and identifies a cluster of similar input texts (if one exists). To the extent that clusters overlap in the model, more than one cluster can be identified. The cluster or clusters may be determined based on similarity calculations (e.g., cosine similarities) between the vector representation of the input text and that of other input texts in the cluster or a centroid of the cluster, for example.
Machine-learning trainer 810 may also produce vector database 814 as part of the training process. Thus, vector database 814 may contain one vector representation per training text (e.g., if the training texts contain k texts, there may be k vector representations, one for each texts). In some embodiments, vector database 814 may be produced by providing the training texts to trained machine learning model 812 and storing their respective vector representations as vector database 814.
Similarity and/or clustering model 816 may take the vector representation from trained machine learning model 812 as input, retrieve one or more stored vector representations from vector database 814, and calculate similarity measures (e.g., cosine similarities) between the vector representation and one or more vector representations retrieved from vector database 814. These similarity measures may be used to identify training texts that are similar to the input text from which the vector representation was derived (e.g., having a calculated similarity value that is greater than a threshold). For example, if vector representation v1 derived from text t1 is determined to be similar to vector representation v2 derived from text t2, then it can be concluded that there is a semantic similarity between text t1 and text t2. Alternatively, certain intents or topics may be predefined in vector space and requests may be compared to these predefined vectors in order to classify the intent(s) and/or topic(s) of each request.
Alternatively or additionally, similarity and/or clustering model 816 may take the vector representation from trained machine learning model 812 as input and determine one or more clusters to which it belongs or to which it is within a threshold distance. These clusters may contain semantically similar content to that of the input text.
It should be noted that these inputs, outputs, and models are provided for purposes of example, and other inputs, outputs, and model architectures may be possible.
For example, the cluster for “user interface concerns” is the most populated in
Accordingly, section 902 aggregates user feedback from the question in section 702 of customizable feedback UI component 700, section 904 aggregates user feedback from the question in section 704 of customizable feedback UI component 700, and section 906 aggregates user feedback from the question in section 706 of customizable feedback UI component 700. Section 908 also aggregates user feedback from the question in section 704 of customizable feedback UI component 700, but does so by listing only the most populated clusters from clusters 822, thus identifying the most common and prevalent user sentiments. Notably, feedback portal 900 can be arranged differently and can display different types of information in different ways.
To that point,
Both summary 914 and summary 918 explain respective issues raised by users in a concise yet readable format, one that can be used by software engineers, product managers, and/or executives to rapidly understand the substance of these issues. Notably, summary 914 and summary 918 were produced by GPT 3.5, but other LLMs might produce differently-worded summaries.
These embodiments provide a technical solution to a technical problem. One technical problem being solved is determination of defects, bugs, or missing features exhibited by software applications. In practice, this is problematic because such information is often unavailable to software engineers, or is in an unreliable form.
In the prior art, software engineers were forced to make educated guesses regarding the defects, bugs, or missing features that they should address. However, these techniques do not always result in the most problematic defects, bugs, or missing features being resolved, as feedback from users was either unavailable, unreliable, or too sparse in detail (e.g., based on a fixed scale). Moreover, the prior art relies on subjective decisions and experiences of the software engineers, which leads to wildly varying outcomes from instance to instance. Thus, prior art techniques did little if anything to reliably provide improvements to software applications.
The embodiments herein overcome these limitations by facilitating the aggregation and summarization of free-form text-based user feedback. In this manner, software application improvements can be accomplished in a more accurate and robust fashion. This results in several advantages. First, software applications can be made more efficient, thus reducing their memory and/or processor utilization. Second, improvements can be made more quickly, enhancing the usefulness of the software applications. Third, user feedback can be neatly and accurately summarized so that software engineers can prioritize the issues that they address.
Other technical improvements may also flow from these embodiments, and other technical problems may be solved. Thus, this statement of technical improvements is not limiting and instead constitutes examples of advantages that can be realized from the embodiments.
The embodiments of
Block 1000 may involve receiving, via a user interface, a plurality of textual user feedback regarding operation of a software application.
Block 1002 may involve aggregating, via a trained machine-learning model, the plurality of textual user feedback into a discrete number of observations regarding the operation of the software application, wherein the observations are in textual form.
Block 1004 may involve determining a subset of the observations that satisfy a relevance criterion.
Block 1006 may involve providing the subset of the observations for display.
In some implementations, the plurality of textual user feedback is received from a plurality of users.
In some implementations, the relevance criterion is that the subset of the observations are most prevalent within the observations.
In some implementations, the operation of the software application relates to operation of a module of the software application, wherein the plurality of textual user feedback is obtained via a feedback component displayed in conjunction with the user interface.
In some implementations, the feedback component comprises one or more of a pane, a column, a popup window, or an overlay on the user interface.
In some implementations, the feedback component displays one or more per-component questions, wherein the plurality of textual user feedback includes respective feedback responses to the per-component questions, and wherein at least some of the respective feedback responses are to respective per-question options.
Some implementations may involve prior to receiving the plurality of textual user feedback, reading, from a database structure, the per-component questions and the respective per-question options; and after receiving the plurality of textual user feedback, writing, to the database structure, the respective feedback responses.
Some implementations may involve, after aggregating the plurality of textual user feedback into the observations, writing, to the database structure, the observations.
In some implementations, the trained machine-learning model includes a clustering model, wherein aggregating the plurality of textual user feedback into the observations comprises applying the clustering model to the plurality of textual user feedback in order to identify semantic clusters thereof, wherein the observations relate to the semantic clusters, and wherein the relevance criterion is that the semantic clusters are populated with at least a threshold number of the observations.
In some implementations, the trained machine-learning model includes a similarity model, wherein aggregating the plurality of textual user feedback into the observations comprises applying the similarity model to the plurality of textual user feedback in order to identify similar feedback thereof, wherein the observations relate to the similar feedback, and wherein the relevance criterion is that the similar feedback has at least a threshold degree of similarity.
In some implementations, the trained machine-learning model includes a sentiment analysis model, wherein aggregating the plurality of textual user feedback into the observations comprises applying the sentiment analysis model to the plurality of textual user feedback in order to identify sentiments therein, wherein the observations relate to the sentiments, and wherein the relevance criterion is that each of the sentiments has at least a threshold level of confidence.
In some implementations, the trained machine-learning model includes a summarization model, wherein aggregating the plurality of textual user feedback into the observations comprises applying the summarization model to the plurality of textual user feedback in order to identify summaries of the plurality of textual user feedback, and wherein the observations relate to the summaries.
In some implementations, the summarization model includes a transformer-based large language model that is prompted with a request to summarize the plurality of textual user feedback.
In some implementations, providing the subset of the observations for display comprises providing the subset of the observations for display on an administrative user interface, wherein the administrative user interface includes one or more of the plurality of textual user feedback as well as the observations
The present disclosure is not to be limited in terms of the particular embodiments described in this application, which are intended as illustrations of various aspects. Many modifications and variations can be made without departing from its scope, as will be apparent to those skilled in the art. Functionally equivalent methods and apparatuses within the scope of the disclosure, in addition to those described herein, will be apparent to those skilled in the art from the foregoing descriptions. Such modifications and variations are intended to fall within the scope of the appended claims.
The above detailed description describes various features and operations of the disclosed systems, devices, and methods with reference to the accompanying figures. The example embodiments described herein and in the figures are not meant to be limiting. Other embodiments can be utilized, and other changes can be made, without departing from the scope of the subject matter presented herein. It will be readily understood that the aspects of the present disclosure, as generally described herein, and illustrated in the figures, can be arranged, substituted, combined, separated, and designed in a wide variety of different configurations.
With respect to any or all of the message flow diagrams, scenarios, and flow charts in the figures and as discussed herein, each step, block, and/or communication can represent a processing of information and/or a transmission of information in accordance with example embodiments. Alternative embodiments are included within the scope of these example embodiments. In these alternative embodiments, for example, operations described as steps, blocks, transmissions, communications, requests, responses, and/or messages can be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved. Further, more or fewer blocks and/or operations can be used with any of the message flow diagrams, scenarios, and flow charts discussed herein, and these message flow diagrams, scenarios, and flow charts can be combined with one another, in part or in whole.
A step or block that represents a processing of information can correspond to circuitry that can be configured to perform the specific logical functions of a herein-described method or technique. Alternatively or additionally, a step or block that represents a processing of information can correspond to a module, a segment, or a portion of program code (including related data). The program code can include one or more instructions executable by a processor for implementing specific logical operations or actions in the method or technique. The program code and/or related data can be stored on any type of computer readable medium such as a storage device including RAM, a disk drive, a solid-state drive, or another storage medium.
The computer readable medium can also include non-transitory computer readable media such as non-transitory computer readable media that store data for short periods of time like register memory and processor cache. The non-transitory computer readable media can further include non-transitory computer readable media that store program code and/or data for longer periods of time. Thus, the non-transitory computer readable media may include secondary or persistent long-term storage, like ROM, optical or magnetic disks, solid-state drives, or compact disc read only memory (CD-ROM), for example. The non-transitory computer readable media can also be any other volatile or non-volatile storage systems. A non-transitory computer readable medium can be considered a computer readable storage medium, for example, or a tangible storage device.
Moreover, a step or block that represents one or more information transmissions can correspond to information transmissions between software and/or hardware modules in the same physical device. However, other information transmissions can be between software modules and/or hardware modules in different physical devices.
The particular arrangements shown in the figures should not be viewed as limiting. It should be understood that other embodiments could include more or less of each element shown in a given figure. Further, some of the illustrated elements can be combined or omitted. Yet further, an example embodiment can include elements that are not illustrated in the figures.
While various aspects and embodiments have been disclosed herein, other aspects and embodiments will be apparent to those skilled in the art. The various aspects and embodiments disclosed herein are for purpose of illustration and are not intended to be limiting, with the true scope being indicated by the following claims.
