Patent Publication Number: US-2022239609-A1

Title: System and method for executing operations in a performance engineering environment

Description:
TECHNICAL FIELD 
     The following relates generally to executing operations in a performance engineering environment, such as in an application testing and/or application development environment. 
     BACKGROUND 
     As the number of mobile users increases, so too does the importance of measuring performance metrics on mobile devices. For example, it is found that users expect applications (also referred to herein as “apps”) to load within a short amount of time, e.g., about two seconds. Because of this, some feel that native app load times should be as fast as possible. Additionally, poor app performance can impact an organization in other ways, for example, by increasing the number of technical service requests or calls, as well as negatively impacting ratings or rankings in application marketplaces (e.g., app stores), or more generally reviews or reputation. These negative impacts can also impact customer retention and uptake, particularly for younger generations who value their ability to perform many tasks remotely and with mobility. 
     Mobile performance testing typically measures key performance indicators (KPIs) from three perspectives, namely the end-user perspective, the network perspective, and the server perspective. The end-user perspective looks at installation, launch, transition, navigation, and uninstallation processes. The network perspective looks at network performance on different network types. The server perspective looks at transaction response times, throughput, bandwidth, and latency. This type of testing is performed in order to identify root causes of application performance bottlenecks to fix performance issues, lower the risk of deploying systems that do not meet business requirements, reduce hardware and software costs by improving overall system performance, and support individual, project-based testing and centers of excellence. 
     In addition to the above technical challenges, performance engineers are typically faced with several different testing and monitoring platforms and often require specialized knowledge or training in order to use the platforms and associated applications. This can limit the persons that are able to execute performance engineering tasks involved in implementing application testing and application development. For example, performance engineering tasks can include interacting with various systems to load builds, initiate and execute tests, gather test results, analyze test results, and package or provide the results to interested parties. However, these various tasks may require access to several different systems and can require a technical understanding of how these systems work and what output they provide. Moreover, many performance engineering tasks such as data gathering, and data analyses, can include significant manual efforts, consuming additional time and effort. With several systems being used, it can also be difficult for a single user to manage all of the tasks required. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Embodiments will now be described with reference to the appended drawings wherein: 
         FIG. 1  is a schematic diagram of an example computing environment. 
         FIG. 2  is a block diagram of an example configuration of an application development environment. 
         FIG. 3  is a block diagram of an example configuration of an application testing environment. 
         FIG. 4  is a schematic diagram of an example of a task automation system integrated with application development and testing environments. 
         FIG. 5  is a block diagram of an example configuration of a task automation system. 
         FIG. 6  is a block diagram of an example configuration of an enterprise system. 
         FIG. 7  is a block diagram of an example configuration of a test device used to test an application build in the application testing environment. 
         FIG. 8  is a block diagram of an example configuration of a client device used to interface with, for example, the task automation system. 
         FIG. 9  is a flow diagram of an example of computer executable instructions for executing tasks in a performance engineering environment such as an application testing or development environment. 
         FIG. 10  is a screen shot of an example of a graphical user interface (GUI) for a chat user interface. 
         FIG. 11  is a screen shot of the chat user interface of  FIG. 10  after subsequent messaging. 
         FIG. 12  is a flow diagram of an example of computer executable instructions for initiating periodic testing based on acquiring a latest build for an application. 
     
    
    
     DETAILED DESCRIPTION 
     It will be appreciated that for simplicity and clarity of illustration, where considered appropriate, reference numerals may be repeated among the figures to indicate corresponding or analogous elements. In addition, numerous specific details are set forth in order to provide a thorough understanding of the example embodiments described herein. However, it will be understood by those of ordinary skill in the art that the example embodiments described herein may be practiced without these specific details. In other instances, well-known methods, procedures and components have not been described in detail so as not to obscure the example embodiments described herein. Also, the description is not to be considered as limiting the scope of the example embodiments described herein. 
     The following generally relates to a task automation system that can be integrated with or within a performance engineering environment such as a computing environment which includes an application testing environment and/or an application development environment to enable operations and instructions to be requested and executed using a conversational chat user interface (UI) to simplify the interactions with such an environment as well as simplify the gathering and consumption of data generated through performance engineering tasks and operations. 
     The task automation system described herein provides both chat UI and background functionality to automate certain performance engineering tasks, in order to enable, for example, non-technical persons to execute technical or specialized work items, to enable more efficient operations within these environments, and to decrease time, effort and costs associated with manual efforts normally associated with performance engineering execution and monitoring. 
     A conversational-based logic system is also provided that can be implemented with third party or built-in natural language processing (NLP) and/or natural language understanding (NLU), a user friendly GUI, and a conversation automation module or “dialog manager”. The logic system and GUI can allow a non-technical user to initiate tests, analyze results and trigger actions through a conversational exchange with a chatbot that is tied into a background system to automate as much of the desired process as possible. With this logic system in place, users can execute mobile device management, such as build download and installation for “build on build” performance engineering. Users can also be provided with a test execution assistant via the chatbot, to allow the user to request and initiate any type of test, such as performance tests, UI tests (for both browser and mobile versions), etc. The chatbot can also provide a conversational tool to allow users to conduct basic conversations about the performance testing, fetch or generate test results or status updates, determine what the results mean, what the user may be looking for, etc. 
     As used herein a “build” may refer to the process of creating an application program for a software release, by taking all the relevant source code files and compiling them and then creating build artifacts, such as binaries or executable program(s), etc. “Build data” may therefore refer to any files or other data associated with a build. The terms “build” and “build data” (or “build file”) may also be used interchangeably to commonly refer to a version or other manifestation of an application, or otherwise the code or program associated with an application that can be tested for performance related metrics. 
     It will be appreciated that while examples provided herein may be primarily directed to automated testing of mobile applications, the principles discussed herein equally apply to applications deployed on or otherwise used by other devices, such as desktop or laptop computers, e.g., to be run on a web browser or locally installed instance of an application. Similarly, the principles described herein can also be adapted to any performance engineering environment in which executable tasks are implemented, whether they include development, testing, implementation, production, quality assurance, etc. 
     Certain example systems and methods described herein are able to execute operations in a performance engineering environment. In one aspect, there is provided a device for executing operations in such a performance engineering environment. The device includes a processor, a communications module coupled to the processor, and a memory coupled to the processor. The memory stores computer executable instructions that when executed by the processor cause the processor to receive via the communications module, a request to implement a task within the environment, from an input to a chat user interface. The computer executable instructions, when executed, also cause the processor to communicate with a logic system to determine an intent from the request; determine one or more executable instructions to implement one or more operations associated with the task, based on the determined intent; and communicate via the communications module, with at least one endpoint to trigger execution of the one or more operations using the one or more executable instructions. The computer executable instructions, when executed, also cause the processor to receive via the communications module, data from the at least one endpoint, the data associated with execution of the one or more operations; generate a conversational response to the request based on or including the data received from the at least one endpoint; and have the conversational response rendered in the chat user interface. 
     In another aspect, there is provided a method of executing operations in a performance engineering environment. The method is executed by a device having a communications module. The method includes receiving via the communications module, a request to implement a task within the environment, from an input to a chat user interface; communicating with a logic system to determine an intent from the request; and determining one or more executable instructions to implement one or more operations associated with the task, based on the determined intent. The method also includes communicating via the communications module, with at least one endpoint to trigger execution of the one or more operations using the one or more executable instructions; receiving via the communications module, data from the at least one endpoint, the data associated with execution of the one or more operations; generating a conversational response to the request based on or including the data received from the at least one endpoint; and having the conversational response rendered in the chat user interface. 
     In another aspect, there is provided non-transitory computer readable medium for executing operations in a performance engineering environment. The computer readable medium includes computer executable instructions for receiving via a communications module, a request to implement a task within the environment, from an input to a chat user interface. The computer readable medium also includes instructions for communicating with a logic system to determine an intent from the request; determining one or more executable instructions to implement one or more operations associated with the task, based on the determined intent; and communicating via the communications module, with at least one endpoint to trigger execution of the one or more operations using the one or more executable instructions. The computer readable medium also includes instructions for receiving via the communications module, data from the at least one endpoint, the data associated with execution of the one or more operations; generating a conversational response to the request based on or including the data received from the at least one endpoint; and having the conversational response rendered in the chat user interface. 
     In certain example embodiments, the device can further have the logic system access a language server to determine the intent from the request. This may include having the logic system use the language server to apply a natural language understanding (NLU) process to correlate the request to the one or more operations associated with the task. 
     In certain example embodiments, the device can generate the executable instructions to implement the one or more operations in a format that is understandable to the at least one endpoint, wherein the device is configured to generate executable instructions in formats understandable to a plurality of disparate systems. 
     In certain example embodiments, the device can have the logic system communicate with the at least one endpoint via a respective application programming interface (API) exposed by a respective endpoint, to trigger execution of the one or more operations. 
     In certain example embodiments, the one or more operations can include initiating an application build download process, wherein the corresponding endpoint returns an initiation status. 
     In certain example embodiments, the one or more operations can include initiating a performance test, wherein the corresponding endpoint returns test data. 
     In certain example embodiments, the one or more operations can include checking the status of an executed or currently executing performance test, wherein the corresponding endpoint returns a status indicator. 
     In certain example embodiments, the request can be translated from a first spoken language to a second spoken language processed by the logic system. 
     In certain example embodiments, the device can have the logic system access a model generated by a machine learning system. The machine learning system can use messages exchanged via the chat user interface to build and/or refine the model over time. 
     In certain example embodiments, the performance engineering environment includes an application testing environment and an application development environment. The device can be coupled to both the application testing and application development environments to provide a centrally placed chat user interface to execute operations and receive data and status information from a plurality of disparate systems. 
       FIG. 1  illustrates an exemplary computing environment  8 . In this example, the computing environment  8  may include an application testing environment  10 , an application development environment  12 , and a communications network  14  connecting one or more components of the computing environment  8 . The computing environment  8  may also include or otherwise be connected to an application deployment environment  16 , which provides a platform, service, or other entity responsible for posting or providing access to applications that are ready for use by client devices. The computing environment  8  may also include or otherwise be connected to a task automation system  24 , which provides both chat UI and background functionality to automate certain performance engineering tasks, in order to enable, for example, non-technical workforce to execute technical or specialized work items, to enable more efficient operations within these environments, and to decrease time, effort and costs associated with manual efforts normally associated with performance engineering execution and monitoring. The application development environment  12  includes or is otherwise coupled to one or more repositories or other data storage elements for storing application build data  18 . The application build data  18  can include any computer code and related data and information for an application to be deployed, e.g., for testing, execution or other uses. 
     In this example, the application build data  18  can be provided via one or more repositories and include the data and code required to perform application testing on a device or simulator. It can be appreciated that while  FIG. 1  illustrates a number of test devices  22  that resemble a mobile communication device, such testing devices  22  can also include simulators, simulation devices or simulation processes, all of which may be collectively referred to herein as “test devices  22 ” for ease of illustration. The application testing environment  10  may include or otherwise have access to one or more repositories or other data storage elements for storing application test data  20 , which includes any files, reports, information, results, metadata or other data associated with and/or generated during a test implemented within the application testing environment  10 . Also shown in  FIG. 1  is a client device  26 , which may represent any electronic device that can be operated by a user to interact or otherwise use the task automation system  24  as herein described. 
     The computing environment  8  may be part of an enterprise or other organization that both develops and tests applications. In such cases, the communication network  14  may not be required to provide connectivity between the application development environment  12 , the task automation system  24 , and the application testing environment  10 , wherein such connectivity is provided by an internal network. The application development environment  12 , task automation system  24 , and application testing environment  10  may also be integrated into the same enterprise environment as subsets thereof. That is, the configuration shown in  FIG. 1  is illustrative only. Moreover, the computing environment  8  can include multiple enterprises or organizations, e.g., wherein separate organizations are configured to, and responsible for, application testing and application development. For example, an organization may contract a third-party to develop an app for their organization but perform testing internally to meet proprietary or regulatory requirements. Similarly, an organization that develops an app may outsource the testing stages, particularly when testing is performed infrequently. The application deployment environment  16  may likewise be implemented in several different ways. For example, the deployment environment  16  may include an internal deployment channel for employee devices, may include a public marketplace such as an app store, or may include any other channel that can make the app available to clients, consumers or other users. 
     One example of the computing environment  8  may include a financial institution system (e.g., a commercial bank) that provides financial services accounts to users and processes financial transactions associated with those financial service accounts. Such a financial institution system may provide to its customers various browser-based and mobile applications, e.g., for mobile banking, mobile investing, mortgage management, etc. 
     Test devices  22  can be, or be simulators for, client communication devices that would normally be associated with one or more users. Users may be referred to herein as customers, clients, correspondents, or other entities that interact with the enterprise or organization associated with the computing environment  8  via one or more apps. Such customer communication devices are not shown in  FIG. 1  since such devices would typically be used outside of the computing environment  8  in which the development and testing occurs. Client device  26  shown in  FIG. 1  may be a similar type of device as a customer communication device and is shown to illustrate a manner in which an individual can interact with the task automation system  24 . However, it may be noted that such customer communication devices and/or client device  26  may be connectable to the application deployment environment  16 , e.g., to download newly developed apps, to update existing apps, etc. 
     In certain embodiments, a user may operate the customer communication devices such that customer device performs one or more processes consistent with what is being tested in the disclosed embodiments. For example, the user may use customer device to engage and interface with a mobile or web-based banking application which has been developed and tested within the computing environment  8  as herein described. In certain aspects, test devices  22 , customer devices, and client device  26  can include, but are not limited to, a personal computer, a laptop computer, a tablet computer, a notebook computer, a hand-held computer, a personal digital assistant, a portable navigation device, a mobile phone, a wearable device, a gaming device, an embedded device, a smart phone, a virtual reality device, an augmented reality device, third party portals, an automated teller machine (ATM), and any additional or alternate computing device, and may be operable to transmit and receive data across communication networks such as the communication network  14  shown by way of example in  FIG. 1 . 
     Communication network  14  may include a telephone network, cellular, and/or data communication network to connect different types of electronic devices. For example, the communication network  14  may include a private or public switched telephone network (PSTN), mobile network (e.g., code division multiple access (CDMA) network, global system for mobile communications (GSM) network, and/or any 3G, 4G, or 5G wireless carrier network, etc.), WiFi or other similar wireless network, and a private and/or public wide area network (e.g., the Internet). 
     Referring back to  FIG. 1 , the computing environment  8  may also include a cryptographic server (not shown) for performing cryptographic operations and providing cryptographic services (e.g., authentication (via digital signatures), data protection (via encryption), etc.) to provide a secure interaction channel and interaction session, etc. Such a cryptographic server can also be configured to communicate and operate with a cryptographic infrastructure, such as a public key infrastructure (PKI), certificate authority (CA), certificate revocation service, signing authority, key server, etc. The cryptographic server and cryptographic infrastructure can be used to protect the various data communications described herein, to secure communication channels therefor, authenticate parties, manage digital certificates for such parties, manage keys (e.g., public and private keys in a PKI), and perform other cryptographic operations that are required or desired for particular applications of the application development environment  12 , task automation system  24 , and/or application testing environment  10 . The cryptographic server may be used to protect data within the computing environment  8  (include the application build data  18  and/or application test data  20 ) by way of encryption for data protection, digital signatures or message digests for data integrity, and by using digital certificates to authenticate the identity of the users and entity devices with which the application development environment  12 , task automation system  24 , and application testing environment  10  communicate to inhibit data breaches by adversaries. It can be appreciated that various cryptographic mechanisms and protocols can be chosen and implemented to suit the constraints and requirements of the particular deployment of the application development environment  12  and application testing environment  10  as is known in the art. 
     In  FIG. 2 , an example configuration of the application development environment  12  is shown. It can be appreciated that the configuration shown in  FIG. 2  has been simplified for ease of illustration. In certain example embodiments, the application development environment  12  may include an editor module  30 , a version and access control manager  32 , one or more libraries  34 , and a compiler  36 , which would be typical components utilized in application development. In this example, the application development environment  12  also includes the application build data  18 , which, while shown within the environment  12 , may also be a separate entity (e.g., repository) used to store and provide access to the stored build files. The application development environment  12  also includes or is provided with (e.g., via an application programming interface (API)), a development environment interface  38 . The development environment interface  38  provides communication and data transfer capabilities between the application development environment  12  and the application testing environment  10  from the perspective of the application development environment  12 . As shown in  FIG. 2 , the development environment interface  38  can connect to the communication network  14  to send/receive data and communications to/from the application testing environment  10 , including instructions or commands initiated by/from the task automation system  24 , as discussed further below. 
     The editor module  30  can be used by a developer/programmer to create and edit program code associated with an application being developed. This can include interacting with the version and access control manager  32  to control access to current build files and libraries  34  while honoring permissions and version controls. The compiler  36  may then be used to compile an application build file and other data to be stored with the application build data  18 . It can be appreciated that a typical application or software development environment  12  may include other functionality, modules, and systems, details of which are omitted for brevity and ease of illustration. It can also be appreciated that the application development environment  12  may include modules, accounts, and access controls for enabling multiple developers to participate in developing an application, and modules for enabling an application to be developed for multiple platforms. For example, a mobile application may be developed by multiple teams, each team potentially having multiple programmers. Also, each team may be responsible for developing the application on a different platform, such as Apple iOS or Google Android for mobile versions, and Google Chrome or Microsoft Edge for web browser versions. Similarly, applications may be developed for deployment on different device types, even with the same underlying operating system. 
     By having build files stored for all of the various operating systems, device types, and versions that are currently compatible and being used, and providing access via the development environment interface  38 , the application testing environment  10  can automatically obtain and deploy the latest builds to perform application testing in different scenarios. Such scenarios can include not only different device types, operating systems, and versions, but also the same build under different operating conditions. 
     While not shown in  FIG. 2  for clarity of illustration, in example embodiments, the application development environment  12  may be implemented using one or more computing devices such as terminals, servers, and/or databases, having one or more processors, communications modules, and database interfaces. Such communications modules may include the development environment interface  38 , which enables the application development environment  12  to communicate with one or more other components of the computing environment  8 , such as the application testing environment  10 , via a bus or other communication network, such as the communication network  14 . While not delineated in  FIG. 2 , the application development environment  12  (and any of its devices, servers, databases, etc.) includes at least one memory or memory device that can include a tangible and non-transitory computer-readable medium having stored therein computer programs, sets of instructions, code, or data to be executed by the one or more processors.  FIG. 2  illustrates examples of modules, tools and engines stored in memory within the application development environment  12 . It can be appreciated that any of the modules, tools, and engines shown in  FIG. 2  may also be hosted externally and be available to the application development environment  12 , e.g., via communications modules such as the development environment interface  38 . 
     Turning now to  FIG. 3 , an example configuration of the application testing environment  10  is shown. The application testing environment  10  includes a testing environment interface  40 , which is coupled to the development environment interface  38  in the application development environment  12 , a testing execution module  42 , and one or more testing hosts  44 . The testing environment interface  40  can provide a UI for personnel or administrators in the application testing environment  10  to coordinate an automated build management process as herein described and to initiate or manage a test execution process as herein described. The testing environment interface  40  can also include, as illustrated in  FIG. 3 , the task automation system  24  to provide such UI for personnel or administrators, e.g., via a chat UI as described in greater detail below. 
     The testing environment interface  40  can provide a platform on which the task automation system  24  can operate to instruct the development environment interface  38 , e.g., by sending a message or command via the communication network  14 , to access the application build data  18  to obtain the latest application build(s) based on the number and types of devices being tested by the testing host(s)  44 . The latest application builds are then returned to the application testing environment  10  by the development environment interface  38  to execute an automated build retrieval operation. As shown in  FIG. 3 , the application build data  18  can be sent directly to the testing host(s)  44  and thus the testing host(s)  44  can also be coupled to the communication network  14 . It can be appreciated that the application build data  18  can also be provided to the testing host(s)  44  via the testing environment interface  40 , e.g., through messages handled by the task automation system  24  via the chat UI  52  (see also  FIG. 4 ). The host(s)  44  in this example have access to a number of test devices  22  which, as discussed above, can be actual devices or simulators for certain devices. The testing host(s)  44  are also scalable, allowing for additional test devices  22  to be incorporated into the application testing environment  10 . For example, a new test device  22  may be added when a new device type is released and will be capable of using the application being tested. Upon installation, the application on each test device  22  can be configured to point to the appropriate environment under test and other settings can be selected/deselected. 
     The test devices  22  are also coupled to the testing execution module  42  to allow the testing execution module  42  to coordinate tests  46  to evaluate metrics, for example, by executing tests for application traffic monitoring, determining UI response times, examining device logs, and determining resource utilization metrics (with Test 1, Test 2, . . . , Test N; shown in  FIG. 3  for illustrative purposes). The tests  46  can generate data logs, reports and other outputs, stored as application test data  20 , which can be made available to various entities or components, such as a dashboard  48 . The framework shown in  FIG. 3  enables the application testing environment  10  to download the latest builds from the respective repositories for the respective device/OS platform(s) and run a UI flow on all test devices  22  to configure the environment, disable system pop-ups, and set feature flags. In this way, the framework can automate the build download and installation process. The framework shown in  FIG. 3  can also enable tests  46  to be initiated, status updates for such tests  46  to be obtained, and other information gathered concerning the tests  46  and/or test data  20 , through inputs interpreted by a chat UI of the task automation system  24 . 
     It can be appreciated that while the testing environment interface  40 , the testing host(s)  44 , and the testing execution module  42  are shown as separate modules in  FIG. 3 , such modules may be combined in other configurations and thus the delineations shown in  FIG. 3  are for illustrative purposes. 
     Referring now to  FIG. 4 , a schematic diagram of the task automation system  24 , integrated with the application development environment  10  and application testing environment  12 , is shown. The configuration shown in  FIG. 4  provides a backend system that can be implemented with a conversational-style message exchange user interface, also referred to herein as a “chat” user interface (UI)  52 . In this configuration, a task automation user interface  50  is provided, which includes the chat UI  52 , namely an application or front-end for users of client devices  26  to interact with the task automation system  24 . The task automation user interface  50  is coupled to a logic system  54 , which is used to determine an intent or instruction from a request made to the chat UI  52  via a chat message. In this way, a less technically inclined or trained user can interact with the environments  10 ,  12  to obtain information and data, to obtain the latest application build data  18 , to initiate test, etc. The logic system  54  can be implemented using, for example the open source Botpress™ conversational AI platform. 
     The logic system  54  is coupled to a language server  58  in this example to access and leverage NLP and NLU modules/processes to assist in determining the intent from the request. It can be appreciated that while the language server  58  is provided as a separate component in  FIG. 4 , the logic system  54  can also adopt or otherwise include or provide the functionalities of the language server  58 . The language server  58  can also be used to allow spoken language translation, e.g., to allow users to input messages in one spoken language that can be translated and interpreted without having the user perform any translation at his/her end. In this way, the task automation system  24  can provide a convenient way for users to interact in a language with which they are comfortable, which can make interpreting the requests more accurate. 
     The logic system  54  includes an API module  56  that is configured to make API calls to the different endpoints that can/should be reached based on the intent of the user&#39;s request. The API module  56  can therefore maintain the appropriate API calls for the various endpoints in the computing environment  8  that can be interacted with or controlled via the chat UI  52 . The API module  56  is therefore coupled to one or more internal APIs  62  that connect into the various endpoints. In addition, the API module  56  can interface with a conversation automation module  60 , which can be an internal component or third party component that can initiate a conversation in the chat UI  52  and collect responses as if it was a regular or routine conversation. 
     The internal APIs  62  in this example enable the logic system  54  to communicate with an application build download module  64 , mobile UI testing module  66 , reporting and logging module  68 , and performance testing module  69  by way of example. It can be appreciated that other types of endpoints with API access can be plugged into the configuration shown in  FIG. 4 . 
     Referring to the encircled stage markers in  FIG. 4 , the user can, e.g., via their client device  26 , initiate a request at stage  1 . At stage  2 , the task automation user interface  50  calls the logic system  54  to process the request, e.g., by inferring an intent from the text in a message input to the chat UI  52 . The logic system  54  may return data at stage  3 , either in response to the call at stage  2  or later if additional data such as statistics, dashboards, logs, etc. At stage  4 , the logic system  54  can interface and communicate with the language server  58  to apply NLP/NLU processes to determine an intent from the request that can be translated into an actionable set of commands executable instructions via one or more API calls to the internal APIs  62  at stage  5 . The API module  56  can also be executed at stage  6 , to initiate a conversational response that incorporates any feedback from the API calls. Stage  7  in this example includes four sub-stages  7   a ,  7   b ,  7   c , and  7   d , each of which corresponds to an API call directed to a corresponding endpoint. For example, sub-stage  7   a  includes a call to the application build download module  64  to have the application testing environment  10  request the latest build from the application development environment  12 . In the other examples shown in  FIG. 4 , testing execution or status updates can be initiated/fetched (sub-stages  7   b ,  7   d ), and reports or logs can be requested at sub-stage  7   c.    
     Sub-stages  8   a ,  8   b ,  8   c , and  8   d  represent individual endpoint responses to the request generated by the respective module  64 ,  66 ,  68 , and  69 . These responses can be collected by the conversation automation module  60  to generate one or more responses to the request that can be rendered in the chat UI  52  at stage  9 . This allows the user of the client device  26  to view or otherwise observe such response(s) at stage  10 , e.g., using a chat application  138  (see  FIG. 8 ) on the client device  26 . 
     The task automation system  24  thus provides a backend platform on which the chat UI  52  can sit to enable the user of a client device  26  to interact with the performance engineering environment (i.e., the computing environment  8  in this example) to execute, initiate, request or perform various operations, tasks or routines, without necessarily requiring knowledge or expertise of the underlying the system(s) that are required to perform these operations. The task automation system  24  also leverages a logic system  54  and language server  58  to provide a seamless conversational experience for the user while implementing largely technical background tasks, by inferring the intent of the user&#39;s request from the messages exchanged with the chat UI  52 . That is, the chat UI  52  can provide both a front-end messaging-based user interface as well as an embedded or background “chatbot” with which to communicate. 
     In  FIG. 5 , an example configuration of the task automation system  24  is shown. In certain embodiments, the task automation system  24  may include one or more processors  70 , a communications module  72 , and a database interface module  74  for interfacing with the datastores for the build data  18  and test data  20  to retrieve, modify, and store (e.g., add) data. Communications module  72  enables the task automation system  24  to communicate with one or more other components of the computing environment  8 , such as client device  26  (or one of its components), via a bus or other communication network, such as the communication network  14 . While not delineated in  FIG. 5 , the task automation system  24  includes at least one memory or memory device that can include a tangible and non-transitory computer-readable medium having stored therein computer programs, sets of instructions, code, or data to be executed by processor  70 .  FIG. 5  illustrates examples of modules, tools and engines stored in memory on the task automation system  24  and operated by the processor  70 . It can be appreciated that any of the modules, tools, and engines shown in  FIG. 5  may also be hosted externally and be available to the task automation system  24 , e.g., via the communications module  72 . In the example embodiment shown in  FIG. 5 , the task automation system  24  includes the logic system  54 , which includes a recommendation engine  76 , a machine learning engine  78 , a classification module  80 , a training module  82 , and a trained model  84 . The logic system  54  also includes or has access to a language server interface module  88  to interface and/or communicate with the language server  58  as described above. The task automation system  24  also includes an access control module  86  and the task automation user interface  50 . The task automation user interface  50  includes or has access to the chat UI  52  as shown in  FIG. 4 . The task automation system  24  also includes the conversation automation module  60 , the API module  62  (which may instead be part of the logic system  54 ), and an enterprise system interface module  87 . 
     The recommendation engine  76  is used by the logic system  54  of the task automation system  24  to generate one or more recommendations for the task automation system  24  and/or a client device  26  that is/are related to an association between inputs (requests) to the chat UI  52  and responses to these requests. For example, the logic system  54  can obtain a textual input from a user of the client device  26  requesting that a test be initiated, a status update to be obtained or other data to be gathered with respect to a process engineering task. As discussed above, this can include accessing the language server  58  via the language server interface module  88  in order to apply NLP/NLU processes. It may be noted that a recommendation as used herein may refer to a prediction, suggestion, inference, association or other recommended identifier that can be used to generate a suggestion, notification, command, instruction or other data that can be viewed, used or consumed by the task automation system  24 , the testing environment interface  40  and/or the client devices  26  interacting with same. The recommendation engine  76  can access chat data (not shown) stored for/by the chat UI  52  and apply one or more inference processes to generate the recommendation(s). The recommendation engine  76  may utilize or otherwise interface with the machine learning engine  78  to both classify data currently being analyzed to generate a suggestion or recommendation, and to train classifiers using data that is continually being processed and accumulated by the task automation system  24 . That is, the recommendation engine  76  can learn request- or response-related preferences and revise and refine classifications, rules or other analytics-related parameters over time. For example, the logic system  54  can be used to update and refine the trained model  84  using the training module  82  as client devices  26  interact with the chat UI  52  during various interactions to improve the NLP/NLU parameters and understanding of how users interact with the processing engineering environment. 
     The machine learning engine  78  may also perform operations that classify the chat data in accordance with corresponding classifications parameters, e.g., based on an application of one or more machine learning algorithms to the data or groups of the data (also referred to herein as “chat content”, “conversation content”, “user requests” or “user intent”). The machine learning algorithms may include, but are not limited to, a one-dimensional, convolutional neural network model (e.g., implemented using a corresponding neural network library, such as Keras®), and the one or more machine learning algorithms may be trained against, and adaptively improved, using elements of previously classified profile content identifying suitable matches between content identified and potential actions to be executed. Subsequent to classifying the event- or workflow-related content or content being analyzed, the recommendation engine  76  may further process each element of the content to identify, and extract, a value characterizing the corresponding one of the classification parameters, e.g., based on an application of one or more additional machine learning algorithms to each of the elements of the chat-related content. By way of example, the additional machine learning algorithms may include, but are not limited to, an adaptive NLP algorithm that, among other things, predicts starting and ending indices of a candidate parameter value within each element of the content, extracts the candidate parameter value in accordance with the predicted indices, and computes a confidence score for the candidate parameter value that reflects a probability that the candidate parameter value accurately represents the corresponding classification parameter. As described herein, the one or more additional machine learning algorithms may be trained against, and adaptively improved using, the locally maintained elements of previously classified content. Classification parameters may be stored and maintained using the classification module  80 , and training data may be stored and maintained using the training module  82 . 
     The trained model  84  may also be created, stored, refined, updated, re-trained, and referenced by the task automation system  24  (e.g., by way of the logic system  54 ) to determine associations between request messages and suitable responses or actions, and/or content related thereto. Such associations can be used to generate recommendations or suggestions for improving the conversational exchange and understanding of the users&#39; intents via the text or other information input to the chat UI  52 . 
     In some instances, classification data stored in the classification module  80  may identify one or more parameters, e.g., “classification” parameters, that facilitate a classification of corresponding elements or groups of recognized content based on any of the exemplary machine learning algorithms or processes described herein. The one or more classification parameters may correspond to parameters that can indicate an affinity or compatibility between the request and response (chat) data, and certain potential actions. For example, a request to initiate a test can include recognition of the message as being a request and the parsing of the message to determine a suitable endpoint and instruction, command or request to forward along to that endpoint. 
     In some instances, the additional, or alternate, machine learning algorithms may include one or more adaptive, NLP algorithms capable of parsing each of the classified portions of the content and predicting a starting and ending index of the candidate parameter value within each of the classified portions. Examples of the adaptive, NLP algorithms include, but are not limited to, NLP models that leverage machine learning processes or artificial neural network processes, such as a named entity recognition model implemented using a SpaCy® library. 
     Examples of these adaptive, machine learning processes include, but are not limited to, one or more artificial, neural network models, such as a one-dimensional, convolutional neural network model, e.g., implemented using a corresponding neural network library, such as Keras®. In some instances, the one-dimensional, convolutional neural network model may implement one or more classifier functions or processes, such a Softmax® classifier, capable of predicting an association between an element of event data (e.g., a value or type of data being augmented with an event or workflow) and a single classification parameter and additionally, or alternatively, multiple classification parameters. 
     Based on the output of the one or more machine learning algorithms or processes, such as the one-dimensional, convolutional neural network model described herein, machine learning engine  78  may perform operations that classify each of the discrete elements of event- or workflow-related content as a corresponding one of the classification parameters, e.g., as obtained from classification data stored by the classification module  80 . 
     The outputs of the machine learning algorithms or processes may then be used by the recommendation engine  76  to generate one or more suggested recommendations, instructions, commands, notifications, rules, or other instructional or observational elements that can be presented to the client device  26  via the chat UI  52 . 
     Referring again to  FIG. 5 , the access control module  86  may be used to apply a hierarchy of permission levels or otherwise apply predetermined criteria to determine what chat data or other client/user, financial or transactional data can be shared with which entity in the computing environment  8 . For example, the task automation system  24  may have been granted access to certain sensitive user profile data for a user, which is associated with a certain client device  26  in the computing environment  8 . Similarly, certain client data may include potentially sensitive information such as age, date of birth, or nationality, which may not necessarily be needed by the task automation system  24  to execute certain actions (e.g., to more accurately determine the spoken language or conversational style of that user). As such, the access control module  86  can be used to control the sharing of certain client data or chat data, a permission or preference, or any other restriction imposed by the computing environment  8  or application in which the task automation system  24  is used. 
     The task automation system  24  in this example also includes the task automation user interface  50  described above, which provides the chat UI  52 . The conversation automation module  60  and API module  62  are also shown in  FIG. 5  which, as described above, can be used to generate a response to a request entered in the chat UI  52  and communicate with endpoints within the process engineering environment such as the application testing environment  10  or application development environment  12  to implement a task such as initiating a test or obtaining status events, etc. 
     As illustrated in  FIG. 5 , the logic system  54  as well as the task automation system  24  can be considered one or more devices having a processor  70 , memory and a communications module  72  configured to work with, or as part of, the computing environment  8 , to perform the operations described herein. It can be appreciated that the various elements of the task automation system  24  and logic system  54  are shown delineated as such in  FIG. 5  for illustrative purposes and clarity of description and could be provided using other configurations and distribution of functionality and responsibilities. 
     The task automation system  24  may also include the enterprise system interface module  87  to provide a graphical user interface (GUI) or API connectivity to communicate with an enterprise system  90  (see  FIG. 6 ) to obtain client data  98  for a certain user interacting with the task automation system  24 . It can be appreciated that the enterprise system interface module  87  may also provide a web browser-based interface, an application or “app” interface, a machine language interface, etc. 
     In  FIG. 6 , an example configuration of an enterprise system  90  is shown. The enterprise system  90  includes a communications module  92  that enables the enterprise system  90  to communicate with one or more other components of the computing environment  8 , such as the application testing environment  10 , application development environment  12 , or task automation system  24 , via a bus or other communication network, such as the communication network  14 . While not delineated in  FIG. 6 , the enterprise system  90  includes at least one memory or memory device that can include a tangible and non-transitory computer-readable medium having stored therein computer programs, sets of instructions, code, or data to be executed by one or more processors (not shown for clarity of illustration).  FIG. 6  illustrates examples of servers and datastores/databases operable within the enterprise system  90 . It can be appreciated that any of the components shown in  FIG. 6  may also be hosted externally and be available to the enterprise system  90 , e.g., via the communications module  92 . In the example embodiment shown in  FIG. 6 , the enterprise system  90  includes one or more servers to provide access to client data  98 , e.g., to assist in determining an intent from a request input to the chat UI  52  or for development or testing purposes. Exemplary servers include a mobile application server  94 , a web application server  96  and a data server  100 . Although not shown in  FIG. 6 , the enterprise system  90  may also include a cryptographic server for performing cryptographic operations and providing cryptographic services. The cryptographic server can also be configured to communicate and operate with a cryptographic infrastructure. The enterprise system  90  may also include one or more data storage elements for storing and providing data for use in such services, such as data storage for storing client data  98 . 
     Mobile application server  94  supports interactions with a mobile application installed on client device  26  (which may be similar or the same as a test device  22 ). Mobile application server  94  can access other resources of the enterprise system  90  to carry out requests made by, and to provide content and data to, a mobile application on client device  26 . In certain example embodiments, mobile application server  94  supports a mobile banking application to provide payments from one or more accounts of user, among other things. 
     Web application server  96  supports interactions using a website accessed by a web browser application running on the client device. It can be appreciated that the mobile application server  94  and the web application server  96  can provide different front ends for the same application, that is, the mobile (app) and web (browser) versions of the same application. For example, the enterprise system  90  may provide a banking application that be accessed via a smartphone or tablet app while also being accessible via a browser on any browser-enabled device. 
     The client data  98  can include, in an example embodiment, financial data that is associated with users of the client devices (e.g., customers of the financial institution). The financial data may include any data related to or derived from financial values or metrics associated with customers of a financial institution system (i.e., the enterprise system  60  in this example), for example, account balances, transaction histories, line of credit available, credit scores, mortgage balances, affordability metrics, investment account balances, investment values and types, among many others. Other metrics can be associated with the financial data, such as financial health data that is indicative of the financial health of the users of the client devices  26 . 
     An application deployment module  102  is also shown in the example configuration of  FIG. 6  to illustrate that the enterprise system  90  can provide its own mechanism to deploy the developed and tested applications onto client devices  26  within the enterprise. It can be appreciated that the application deployment module  102  can be utilized in conjunction with a third-party deployment environment such as an app store to have tested applications deployed to employees and customers/clients. 
     In  FIG. 7 , an example configuration of a test device  22  is shown. It can be appreciated that the test device  22  shown in  FIG. 7  can correspond to an actual device or represent a simulation of such a device  22 . In certain embodiments, the client device  22  may include one or more processors  110 , a communications module  112 , and a data store  124  storing device data  126  and application data  128 . Communications module  112  enables the test device  22  to communicate with one or more other components of the computing environment  8  via a bus or other communication network, such as the communication network  14 . While not delineated in  FIG. 7 , the client device  22  includes at least one memory or memory device that can include a tangible and non-transitory computer-readable medium having stored therein computer programs, sets of instructions, code, or data to be executed by processor  110 .  FIG. 7  illustrates examples of modules and applications stored in memory on the test device  22  and operated by the processor  110 . It can be appreciated that any of the modules and applications shown in  FIG. 7  may also be hosted externally and be available to the test device  22 , e.g., via the communications module  112 . 
     In the example embodiment shown in  FIG. 7 , the test device  22  includes a display module  114  for rendering GUIs and other visual outputs on a display device such as a display screen, and an input module  116  for processing user or other inputs received at the test device  22 , e.g., via a touchscreen, input button, transceiver, microphone, keyboard, etc. The test device  22  may also include an application  118  to be tested that includes the latest application build data  18  to be tested using the test device  22 , e.g., by executing tests. The test device  22  may include a host interface module  120  to enable the test device  22  to interface with a testing host for loading an application build. The test device  22  in this example embodiment also includes a test execution interface module  122  for interfacing the application  118  with the testing execution module. The data store  124  may be used to store device data  126 , such as, but not limited to, an IP address or a MAC address that uniquely identifies test device  22 . The data store  124  may also be used to store application data  128 , such as, but not limited to, login credentials, user preferences, cryptographic data (e.g., cryptographic keys), etc. 
     In  FIG. 8 , an example configuration of the client device  26  is shown. In certain embodiments, the client device  26  may include one or more processors  130 , a communications module  132 , and a data store  144  storing device data  146  and application data  148 . Communications module  132  enables the client device  26  to communicate with one or more other components of the computing environment  8 , such as the task automation system  24 , via a bus or other communication network, such as the communication network  14 . While not delineated in  FIG. 8 , the client device  26  includes at least one memory or memory device that can include a tangible and non-transitory computer-readable medium having stored therein computer programs, sets of instructions, code, or data to be executed by processor  130 .  FIG. 8  illustrates examples of modules and applications stored in memory on the client device  26  and operated by the processor  130 . It can be appreciated that any of the modules and applications shown in  FIG. 8  may also be hosted externally and be available to the client device  26 , e.g., via the communications module  132 . 
     In the example embodiment shown in  FIG. 8 , the client device  26  includes a display module  134  for rendering GUIs and other visual outputs on a display device such as a display screen, and an input module  136  for processing user or other inputs received at the client device  26 , e.g., via a touchscreen, input button, transceiver, microphone, keyboard, etc. The client device  26  may also include a chat application  138 , which may take the form of a customized app, plug-in, widget, or software component provided by the task automation system  24  for use by the client device  26  to use the chat UI  52 . Similarly, the client device  26  may include an enterprise system application  142  provided by their enterprise system  90 . The client device  26  in this example embodiment also includes a web browser application  140  for accessing Internet-based content, e.g., via a mobile or traditional website. The data store  144  may be used to store device data  146 , such as, but not limited to, an IP address or a MAC address that uniquely identifies client device  26  within environment  8 . The data store  144  may also be used to store application data  148 , such as, but not limited to, login credentials, user preferences, cryptographic data (e.g., cryptographic keys), etc. 
     It will be appreciated that only certain modules, applications, tools and engines are shown in  FIGS. 2 to 8  for ease of illustration and various other components would be provided and utilized by the application testing environment  10 , application development environment  12 , task automation system  24 , test device  22 , enterprise system  90 , and client device  26  as is known in the art. 
     It will also be appreciated that any module or component exemplified herein that executes instructions may include or otherwise have access to computer readable media such as storage media, computer storage media, or data storage devices (removable and/or non-removable) such as, for example, magnetic disks, optical disks, or tape. Computer storage media may include volatile and non-volatile, removable and non-removable media implemented in any method or technology for storage of information, such as computer readable instructions, data structures, program modules, or other data. Examples of computer storage media include RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information, and which can be accessed by an application, module, or both. Any such computer storage media may be part of any of the servers or other devices in the application testing environment  10 , application development environment  12 , task automation system  24 , enterprise system  90 , client device  26 , or test device  22 , or accessible or connectable thereto. Any application or module herein described may be implemented using computer readable/executable instructions that may be stored or otherwise held by such computer readable media. 
     Referring to  FIG. 9 , an example embodiment of computer executable instructions for executing tasks in a performance engineering environment, such as an application testing or development environment  10 ,  12 , or another computing environment  8 , is shown. At block  150 , the task automation system  24  receives a request to implement a task within the computing environment  8 , from an input to the chat UI  52 . At block  152 , the task automation system  24  communicates with the logic system  54  to determine or infer an intent from the request. This can include communicating with the language server  58  and/or referencing the trained model  84 . Moreover, this can also include translating the text of the request from one spoken language into another that can be interpreted by the language server  58  or the logic system  54 . 
     At block  154 , the logic system  54  determines executable instructions to implement one or more operations associated with the task based on the intent. That is, the logic system  54  uses the intent inferred from the request to determine which endpoints, if any, should be communicated with to satisfy the request. For example, the request may involve fetching a latest application build, executing one or more tests, and returning a status of the initiated test(s), which would require determining which endpoints require instructions and the associated API calls to generate. This can also include generating executable instructions in a format that is understandable to that endpoint. At block  156 , the logic system  54  uses the API module  56  to communicate, via the internal APIs  62 , with the endpoints (e.g., modules  64 ,  66 ,  68 ,  69  in  FIG. 4 ). At block  158 , the task automation system  24  receives data associated with the execution of the one or more operations, from the endpoints. As illustrated in  FIG. 4 , this can include receiving multiple replies to the conversation automation module  60  to be formatted or otherwise rendered as conversational response(s) based on or including the received data, at block  160 . The conversational response(s) can then be rendered in the chat UI  52  at block  162 . 
     Turning now to  FIG. 10 , a screen shot  200  of the chat UI  52  is shown. In this example, a first user message  204  includes a textual request  206 , namely: “Please initiate a UI test on the latest mobile release” that can be entered via a message input box  202 . The chat UI  52  in this example replies immediately with a status message  210 , namely: “Starting that for you . . . ”. Once communication has been established with the appropriate endpoint, the chat UI  52  can display a first response message  212  with response text  214  and a link  216  to further content. In this example, the response text  214  indicates: “The test has been initiated and is in progress”, and the link  216  allows the user to click through to status information on that test. By selecting this link  216 , the user can be navigated to another UI, a dashboard, or additional data can be inserted with in the chat UI screen. 
     Turning now to  FIG. 11 , the screen shot  200  of the chat UI  52  is shown in a subsequent stage of conversation. In this example, after selecting the link  216  as illustrated in  FIG. 10 , a further status message  210  is displayed, in this case indicating: “Getting that for you . . . ”, with “that” referring to the status information associated with the link  216 . A further response message  212  is then displayed, which includes a visual representation  218  of the mobile UI test status. It can be appreciated that this message can include the status itself if space permits or, as shown in  FIG. 11 , the user can select the visual representation  218  to navigate to another user interface such as a statistics or monitoring dashboard. As such, it can be seen that the chat UI  52  can provide a central point for the user to issue commands, obtain data, and navigate to other programs without the need to have specialized knowledge or training with respect to these other programs or interfaces. 
     Referring to  FIG. 12 , an example embodiment of computer executable instructions for using the chat UI  52  to initiate periodic testing based on acquiring a latest build for an application is shown. In this example embodiment, the process flow proceeds from block  156  (see  FIG. 9 ) to the periodic testing execution and returns a result to the chat UI  52  at block  158  (see  FIG. 9 ). That is,  FIG. 12  illustrates an underlying workflow that can be initiated by the user via the chat UI  52 , without requiring knowledge of that workflow. At block  300 , one or more tests are run for a current build. At block  302 , test results data are acquired. It can be appreciated that the current test results data acquired at block  302  can be compared with previous test results data at block  304 , e.g., to determine whether feedback provided to the application development environment  12  in a previous iteration has led to an improvement in the application. As shown in  FIG. 12 , this comparison can be used to provide additional feedback with the test results data send to the application development environment  12  at block  306 . After a period of time, e.g., one day, one week, etc.; the application testing environment  10  detects the next testing cycle at block  308  and requests a new build from the application development environment  12  at block  310 . It can be appreciated that blocks  306 ,  308  and  310  can be coordinated and/or executed by the testing environment interface  50 . The process can be repeated by returning to block  300  wherein new testing is performed using the latest build files that have been acquired, installed, and provisioned on the test devices  22 . 
     From  FIG. 12  it can be seen that the chat UI can provide a familiar and user friendly front end experience for a user to interface with a more complex process via the backend functionality provided by the task automation system  24 . 
     It will be appreciated that the examples and corresponding diagrams used herein are for illustrative purposes only. Different configurations and terminology can be used without departing from the principles expressed herein. For instance, components and modules can be added, deleted, modified, or arranged with differing connections without departing from these principles. 
     The steps or operations in the flow charts and diagrams described herein are just for example. There may be many variations to these steps or operations without departing from the principles discussed above. For instance, the steps may be performed in a differing order, or steps may be added, deleted, or modified. 
     Although the above principles have been described with reference to certain specific examples, various modifications thereof will be apparent to those skilled in the art as outlined in the appended claims.