Automatic generation of datasets by processing collaborative forums using artificial intelligence techniques

Abstract

Provided herein are methods, systems, and computer program products for automatically generating datasets by processing federated forums with artificial intelligence techniques. The computer-implemented method includes obtaining conversational data from a federated forum source, categorizing at least one portion of the conversational data based on a designated application using a first set of artificial intelligence techniques, extracting information from at least a portion of the categorized data related to test case-related issues, validating at least a portion of the extracted information by analyzing a portion of the conversational data belonging to a plurality of entities and related to the extracted information using a second set of artificial intelligence techniques, generating one or more datasets related to at least one of the test case-related issues for at least one of the designated applications using the validated information, and performing at least one automated action based on the one or more generated datasets.

Description

[Technical Field]

[0001] This application relates generally to information technology, and more particularly to data processing technology. More specifically, feature testing is a process typically performed on new features that are added to one or more systems, applications, etc. [Background technology]

[0002] As part of the feature testing process, one or more required sets of ground truth test data must be created. As used herein, a ground truth dataset (also referred to as a gold standard dataset) refers to labeled data containing inputs and expected outputs. However, traditional dataset generation techniques typically involve a labor-intensive and tedious series of tasks, often resulting in time and resource challenges. Summary of the Invention

[0003] In one embodiment of the present invention, techniques are provided for automatically generating datasets by processing federated forums using artificial intelligence techniques. An exemplary computer-implemented method may include acquiring conversational data from one or more federated forum sources and classifying at least a portion of the acquired conversational data into one or more categories based, at least in part, on one or more designated applications using a first set of one or more artificial intelligence techniques. The method also includes extracting information from at least a portion of the classified conversational data related to one or more test case-related issues and validating at least a portion of the extracted information by analyzing, using a second set of one or more artificial intelligence techniques, portions of the conversational data belonging to a plurality of entities and related to the extracted information. The method further includes using the validated information to generate one or more datasets related to at least one of the one or more test case-related issues for at least one of the one or more designated applications and performing at least one automated action based, at least in part, on the one or more generated datasets.

[0004] Another embodiment of the present invention, or elements thereof, may be implemented in the form of a computer program product tangibly embodying computer-readable instructions that, when executed, cause a computer to perform a plurality of method steps as described herein. Furthermore, another embodiment of the present invention, or elements thereof, may be implemented in the form of a system including a memory and at least one processor coupled to the memory and configured to perform the method steps. Furthermore, another embodiment of the present invention, or elements thereof, may be implemented in the form of means, or elements thereof, for performing the method steps described herein. The means may include hardware modules or a combination of hardware and software modules, with the software modules being stored on a tangible computer-readable storage medium (or multiple such media).

[0005] These and other objects, features and advantages of the present invention will become apparent from the following detailed description of illustrative embodiments, which is to be read in connection with the accompanying drawings. [Brief explanation of the drawings]

[0006] [Figure 1] FIG. 1 is a diagram of a system architecture according to an exemplary embodiment of the present invention. [Figure 2] FIG. 1 is a diagram of intent classification for a dialog utterance, according to an exemplary embodiment of the present invention. [Figure 3] FIG. 1 is a diagram of intent classification and slot filling for a dialog utterance, according to an exemplary embodiment of the present invention. [Figure 4] 1 is a flow diagram illustrating a technique according to an embodiment of the present invention. [Figure 5] FIG. 1 is a system diagram of an exemplary computer system capable of implementing at least one embodiment of the present invention. [Figure 6] 1 is a diagram of a cloud computing environment according to an embodiment of the present invention. [Figure 7]FIG. 1 is a diagram of an abstract model layer according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0007] As described herein, at least one embodiment includes automatically generating datasets by processing federated forums using artificial intelligence techniques. Such an embodiment may include generating at least one ground truth dataset for one or more test cases by analyzing and / or validating information collected from federated forum sources (e.g., group chats, open source communities, message boards, workflow management tools, etc.) to enable applications such as fault localization, incident similarity, solution recommendation, and anomaly detection. Such information, in one or more embodiments, may include information related to test cases for one or more specific applications from a variety of channels (as described above), and such data sources may include structured data as well as unstructured data (e.g., text, etc.).

[0008] At least one embodiment also includes classifying the test cases collected from such diverse channels. By way of example, such an embodiment includes, after collecting the data, mapping at least a portion of the data to one or more downstream tasks. For example, if the extracted text relates to a solution to be implemented to resolve an issue, the text can be mapped to a downstream task of action recommendation. Accordingly, one or more embodiments include generating at least one ground truth test dataset for various downstream applications, such as fault localization, incident similarity, solution recommendation, anomaly detection, etc. Additionally, at least one embodiment includes extracting knowledge of at least a portion of the application from the classified test cases based at least in part on the mapped data. Such knowledge (which can be thought of as output labels) can include details of the application, such as critical components, key actions to be performed, and key failures commonly encountered in the system.

[0009] Further, such embodiments may include analyzing and / or validating the accuracy and / or effectiveness of at least some of the collected test cases based at least in part on the extracted knowledge to generate one or more ground truth datasets used to test at least some of the applications. According to one or more embodiments, at least one output label is required to generate the test cases, and as described in more detail herein, such embodiments may include checking and / or analyzing agreement between the various entities involved.

[0010] As described above and further herein, at least one embodiment includes extracting information from a segmented conversation (e.g., derived from a collaborative forum). In such an embodiment, one or more features can be used to determine whether two utterances i and j are part of the same conversation. One such feature includes using thread structure, where i and j are determined to be part of the same conversation if they are part of the same thread structure. Another such feature includes using temporal information, where i and j are determined to be part of the same conversation if they are close in time. Yet another such feature includes detecting whether i and j are a question-answer pair, where i and j are determined to be part of the same conversation if they are determined to be a question-answer pair. Yet another such feature includes using topic keywords and / or sentence embeddings to determine whether i and j are similar, where i and j are determined to be part of the same conversation if they are deemed similar. Yet another such feature involves using context utterances and relevant keywords for i and j to determine whether i and j are part of the same conversation.

[0011] FIG. 1 is a diagram of a system architecture according to an embodiment of the present invention. By way of example, FIG. 1 depicts one or more input sources 102 and an automated dataset generation system 105, which includes multiple components (e.g., components 104, 106, 108, 110, and 112), as described in further detail below. More specifically, input source 102 provides input data to input source quality determination component 104, which performs one or more filtering and / or preprocessing actions on at least a portion of the input data. Such preprocessing steps may include, for example, segmenting the input data into various chunks (since such conversations are typically intermittent). The remaining data and / or preprocessed data output from component 104 is provided to intent classification component 106, which labels the input source of the data for downstream applications, as described in further detail in connection with FIGS. 2 and 3.

[0012] At least a portion of the output from the intent classification component 106 is provided to a standard dataset creation component 112 and a key answer extraction and slot filling component 108 (which extracts and / or inserts key entity-identifying information and / or key action-identifying information). The output from component 108 is provided to a consensus detection component 110, which processes the provided information to determine and / or detect consensus from one or more federated forums. More specifically, from the answers extracted by component 108, one or more embodiments include checking and / or determining whether one or more of the answers are correct.

[0013] For example, using data from a group chat and / or workflow management tool, such embodiments may include observing a site reliability engineer (SRE) mention a particular component as having a given issue, but later observe one or more other entities reply with different findings and explanations. Note that not all mentions in answers are correct, and thus one or more embodiments may include determining which of such answers are correct. Such embodiments may include using at least one conditional random field (CRF)-based sequential model to make predictions based on the sentence level and / or segment level. In connection with such embodiments, features used to identify agreement and / or disagreement between answers may include one or more lexical features, one or more sentiment-based features, and one or more structural features.

[0014] As described herein, CRF refers to a class of techniques that can be applied, for example, in the context of pattern recognition and machine learning, and can be used for structured prediction. While classifiers predict labels for single samples without considering neighboring samples, CRF can take context into account. For example, in one or more embodiments, at least one CRF model can be implemented to take context into account in the form of preceding message segments. Additionally, based on the final output, such embodiments can include assigning a label to each input.

[0015] Based at least in part on the determined level of agreement, output from component 110 is provided to standard dataset creation component 112, which creates at least one dataset for use, for example, in one or more feature test operations. In at least one embodiment, creating such a dataset can include using labeled and / or categorized input sources and answers extracted after the agreement process (e.g., via component 110), process-related information, such as logs, alerts, problem logs, etc., to create the dataset needed for a downstream application. In one or more embodiments, an example output can include a two-column data frame, one column for the input and the second column for the output. In the case of fault localization, for example, one column can contain alert information (e.g., details of the error reported by the system) and the other column can contain identification of the component responsible for the fault.

[0016] As described above and further below, one or more embodiments include intent classification techniques. By way of example, such techniques may include using predefined intent categories, such as impact measurement, fault location, incident and / or alert similarity, and fault isolation. More specifically, the impact measurement intent category may relate to utterances describing error measurement and service and / or customer impact of one or more faults. The fault location intent category may relate to utterances describing checked and / or potential locations of one or more faults. The incident and / or alert similarity intent category may relate to utterances describing whether particular alerts and / or incidents are similar. Additionally, the fault isolation intent category may relate to utterances describing root cause fault location information for one or more incidents. Furthermore, one or more embodiments may include other intent categories that may relate to utterances covering other topics.

[0017] Figure 2 is a diagram of intent classification for a dialog utterance, according to an exemplary embodiment of the present invention. By way of example, Figure 2 depicts instances of long short-term memory (LSTM) models 214-1 through 214-6 (collectively referred to herein as LSTM models 214) used for intent classification. The current utterance is presented to this model 214 in a tokenized manner, indicated by tokens 202-212 and also by the ordinal position of the utterance in the conversation. These two components, indicated by token component 216 and utterance position component 218, are combined (as illustrated by element 220) and used via output layer 222 to generate an understanding, indicated by element 224, that the user utterance relates to one or more particular tasks or intents.

[0018] FIG. 3 is a diagram of intent classification and slot filling for a dialog utterance, according to an exemplary embodiment of the present invention. By way of example, FIG. 3 depicts an embodiment similar to that depicted in FIG. 2, but with one or more additional features. Specifically, along with intent classification (similar to that depicted in FIG. 2 and depicted by tokens 302-312, LSTM model instances 314-1-314-6, and output associates 320-324), FIG. 3 further depicts implementation type detection for each of the tokens, as illustrated by LSTM model instances 314-7-314-12. Thus, the user can obtain more information related to the entities manifested within the utterance (e.g., detected token types, such as "component," "action," and "other (O)").

[0019] 4 is a flow diagram illustrating a technique according to an embodiment of the present invention. Step 400 includes obtaining conversational data from one or more federated forum sources. In one or more embodiments, the one or more federated forum sources may include one or more group chats, one or more open source communities, one or more message boards, or one or more workflow management tools, or a combination thereof. Additionally, in such embodiments, the conversational data may include structured and unstructured data.

[0020] Step 402 includes using a first set of one or more artificial intelligence techniques to classify at least a portion of the acquired conversational data into one or more categories based at least in part on the one or more specified applications. In at least one embodiment, using the first set of one or more artificial intelligence techniques includes using at least one recurrent neural network (e.g., at least one LSTM network) to map at least a portion of the acquired conversational data to one or more application tasks associated with the one or more categories. Step 404 includes extracting information from at least a portion of the classified conversational data regarding one or more test case-related challenges.

[0021] Step 406 includes validating at least a portion of the extracted information by analyzing a portion of conversational data belonging to the plurality of entities and associated with the extracted information using a second set of one or more artificial intelligence techniques. In at least one embodiment, using the second set of one or more artificial intelligence techniques includes processing the portion of the conversational data in association with at least a portion of the extracted information using at least one conditional random field-based model.

[0022] Step 408 includes using the verified information to generate one or more data sets related to at least one of the one or more test case-related problems for at least one of the one or more designated applications, in one or more embodiments, the one or more designated applications include one or more fault localization applications, one or more incident similarity applications, one or more solution recommendation applications, and / or one or more anomaly detection applications.

[0023] Step 410 includes performing at least one automated action based at least in part on the one or more generated datasets. In at least one embodiment, performing the at least one automated action includes automatically performing a feature test for at least one of the one or more specified applications using at least one of the one or more generated datasets.

[0024] Also, one or more embodiments include automatically updating the first set of one or more artificial intelligence techniques, or the second set of one or more artificial intelligence techniques, or both, based at least in part on the one or more generated datasets. Additionally or alternatively, according to at least one embodiment, software implementing the techniques depicted in FIG. 4 may be provided as a service in a cloud environment.

[0025] The technology depicted in FIG. 4 may also include providing a system, as described herein, including separate software modules, each embodied in a tangible computer-readable, recordable storage medium. All of the modules (or any subset thereof) may reside on the same medium, for example, or each may reside on a different medium. The modules may include any or all of the components shown in the figures or described herein, or any combination thereof. In embodiments of the invention, the modules may be executed, for example, by a hardware processor. In this case, method steps may be performed using separate software modules of such a system executed by the hardware processor. Furthermore, a computer program product may include providing a system having separate software modules, and may include a tangible computer-readable, recordable storage medium having code adapted to be executed to perform at least one method step described herein.

[0026] 4 may be implemented using a computer program product that may include computer-usable program code stored on a computer-readable storage medium in a data processing system, the computer-usable program code being downloaded over a network from a remote data processing system. Also, in embodiments of the present invention, the computer program product may include computer-usable program code stored on a computer-readable storage medium in a server data processing system, the computer-usable program code being downloaded over a network to the remote data processing system for use in the remote system's computer-readable storage medium.

[0027] Embodiments of the present invention or elements thereof may be implemented in the form of an apparatus including a memory and at least one processor, coupled to the memory, configured to perform the exemplary method steps.

[0028] Additionally, embodiments of the present invention may utilize software running on a computer or workstation. Referring to FIG. 5, such implementations may utilize, for example, a processor 502, memory 504, and an input / output interface formed, for example, from a display 506 and a keyboard 508. As used herein, the term "processor" is intended to include any processing device, such as a device including a CPU (central processing unit) or other forms of processing circuitry, or both. Furthermore, the term "processor" may refer to two or more individual processors. The term "memory" is intended to include memory associated with a processor or CPU, such as, for example, RAM (random access memory), ROM (read-only memory), fixed memory devices (e.g., hard drives), removable memory devices (e.g., diskettes), flash memory, and the like. Additionally, as used herein, the phrase "input / output interface" is intended to include, for example, mechanisms for inputting data into a processing unit (e.g., a mouse) and mechanisms for providing results associated with a processing unit (e.g., a printer). The processor 502, memory 504, and input / output interfaces such as a display 506 and keyboard 508 may be interconnected, for example, via a bus 510 as part of a data processing unit 512. Suitable interconnections may also be provided, for example, via the bus 510, to a network interface 514, such as a network card, that may be provided to interface with a computer network, and to a media interface 516, such as a diskette or CD-ROM drive, that may be provided to interface with media 518.

[0029] Thus, computer software containing instructions or code for performing the methods of the present invention, as described herein, can be stored in an associated memory drive (e.g., ROM, fixed or removable memory) and, when ready for use, can be loaded partially or wholly (e.g., into RAM) and implemented by a CPU. Such software can include, but is not limited to, firmware, resident software, microcode, etc.

[0030] A data processing system suitable for storing and / or executing program code includes at least one processor 502 coupled directly or indirectly to memory elements 504 via a system bus 510. The memory elements may include local memory utilized during the actual execution of the program code, bulk storage, and cache memory that provides temporary storage of at least some program code to reduce the number of times the code must be retrieved from bulk storage during execution.

[0031] Input / output devices, or I / O devices (including but not limited to a keyboard 508, a display 506, a pointing device, etc.) can be coupled to the system either directly (such as via a bus 510) or through an intervening I / O controller (omitted for clarity).

[0032] Network adapters, such as network interface 514, may also be coupled to the system to enable the data processing system to become coupled to other data processing systems or remote printers or storage devices through intervening private or public networks. Currently available types of network adapters include modems, cable modems, and Ethernet cards.

[0033] As used herein, including in the claims, "server" includes a physical data processing system (e.g., system 512 as shown in FIG. 5) running a server program. It should be understood that such a physical server may or may not include a display and keyboard.

[0034] The present invention may be a system, method, or computer program product, or combination thereof, at any possible level of technical detail of integration. The computer program product may include a computer-readable storage medium having computer-readable program instructions for causing a processor to perform aspects of the present invention.

[0035] A computer-readable storage medium may be a tangible device capable of retaining and storing instructions for use by an instruction execution device. A computer-readable storage medium may be, for example, but not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of computer-readable storage media includes: portable computer diskettes, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disk read-only memory (CD-ROM), digital versatile disk (DVD), memory stick, floppy disk, mechanically encoded devices such as punch cards or grooved structures having instructions recorded thereon, and any suitable combination of the foregoing. As used herein, computer-readable storage media should not be construed as ephemeral signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission medium (e.g., light pulses passing through fiber optic cable), or electrical signals transmitted over electrical wires.

[0036] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to an individual computing / processing device or to an external computer or external storage device over a network, such as the Internet, a local area network, a wide area network, or a wireless network, or a combination thereof. The network can include copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, or edge servers, or a combination thereof. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium within the individual computing / processing device.

[0037] Computer-readable program instructions for carrying out the operations of the present invention may be either source code or object code written in any combination of one or more programming languages, including assembler instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, configuration data for integrated circuits, or object-oriented programming languages ​​such as Smalltalk®, C++, and procedural programming languages ​​such as the "C" programming language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partly on the user's computer as a standalone software package, partly on the user's computer and partly on a remote computer, or all on a remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be to an external computer (e.g., via the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA) can execute computer readable program instructions to individualize the electronic circuitry by utilizing state information in the computer readable program instructions to implement aspects of the present invention.

[0038] Aspects of the present invention are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions.

[0039] These computer-readable program instructions may be provided to a processor of a computer or other programmable data processing apparatus to produce a machine, such that the instructions, which execute on the processor of the computer or other programmable data processing apparatus, create means for performing the functions / acts specified in one or more blocks of the flowcharts and / or block diagrams. These computer-readable program instructions may also be stored on a computer-readable storage medium capable of directing a computer, programmable data processing apparatus, or other device, or combination thereof, to function in a particular manner, such that the computer-readable storage medium on which the instructions are stored comprises an article of manufacture containing instructions that implement aspects of the functions / acts specified in one or more blocks of the flowcharts and / or block diagrams.

[0040] The computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to create a computer-implemented process and cause the computer, other programmable apparatus, or other device to perform a series of operational steps, such that the instructions, which execute on the computer, other programmable apparatus, or other device, perform the functions / acts specified in one or more blocks of the flow charts and / or block diagrams.

[0041] The flow charts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flow chart or block diagram may represent a module, segment, or portion of instructions, including one or more executable instructions for implementing specified logical functions. In some alternative implementations, the functions shown in the blocks may occur out of the order shown in the figures. For example, two blocks shown in succession may actually be performed as a single step, or may be executed concurrently, substantially concurrently, partially, or fully in a time-overlapping manner, or the blocks may sometimes be executed in reverse order, depending on the functionality involved. It should also be noted that each block of the block diagrams and / or flow chart diagrams, and combinations of blocks in the block diagrams and / or flow chart diagrams, may be implemented by special-purpose hardware-based systems that perform the specified functions or acts or execute a combination of special-purpose hardware and computer instructions.

[0042] It should be noted that any of the methods described herein may include the additional step of providing a system including separate software modules embodied in a computer-readable storage medium, where the modules may include, for example, any or all of the components described in detail herein. In this case, the method steps may be performed using separate software modules and / or sub-modules of such a system executed on hardware processor 502. Furthermore, a computer program product may include a computer-readable storage medium having code adapted to be implemented to perform at least one method step described herein, including providing a system with separate software modules.

[0043] In any event, it should be understood that the components illustrated herein may be implemented in various forms of hardware, software, or combinations thereof, such as an application specific integrated circuit (ASIC), functional circuitry, a suitably programmed digital computer with associated memory, etc. Given the teachings of the present invention provided herein, one skilled in the art will be able to contemplate other implementations of the components of the present invention.

[0044] Additionally, it should be understood at the outset that implementation of the teachings described herein is not limited to any particular computing environment. Rather, embodiments of the present invention can be implemented in conjunction with any type of computing environment now known or later developed.

[0045] For example, cloud computing is a service delivery model that enables convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, network bandwidth, servers, processing, memory, storage, applications, virtual machines, and services) that can be rapidly provisioned and released with minimal management effort or interaction with the service provider. This cloud model can include at least five characteristics, at least three service models, and at least four deployment models.

[0046] Its features are as follows:

[0047] On-demand self-service: Cloud consumers can unilaterally provision computing capacity, such as server time and network storage, automatically as needed without requiring human interaction with the service provider.

[0048] Broad network access: Functionality is available over the network and accessed through standard mechanisms that facilitate use by heterogeneous thin-client or thick-client platforms (e.g., mobile phones, laptops, and PDAs).

[0049] Resource Pool: A provider's computing resources are pooled to serve multiple consumers using a multi-tenant model, with different physical and virtual resources dynamically allocated and reallocated according to demand. There is a sense of location independence in that consumers generally have no control or information about the exact location of the resources provided, although location may be specified at a high level of abstraction (e.g., country, state, or data center).

[0050] Rapid Scalability: Capabilities can be provisioned quickly and scalably, in some cases automatically, quickly scaled out, and quickly released and quickly scaled in. To the consumer, the capabilities available for provisioning often appear unlimited, and any amount can be purchased at any time.

[0051] Service Metering: Cloud systems automatically control and optimize resource usage by leveraging metering capabilities at several levels of abstraction appropriate to the type of service (e.g., storage, processing, bandwidth, and active user accounts). Resource usage can be monitored, controlled, and reported, providing transparency to both providers and consumers of the services used.

[0052] The service model is as follows:

[0053] Software as a Service (SaaS): The functionality offered to the consumer is the use of a provider's applications running on a cloud infrastructure. The applications are accessible from a variety of client devices through thin-client interfaces such as web browsers (e.g., web-based email). The consumer does not manage or control the underlying cloud infrastructure, including the network, servers, operating systems, storage, or even individual application functions, except for limited user-specific application configuration settings.

[0054] Platform as a Service (PaaS): The capability offered to the consumer is the deployment of consumer-created or acquired applications, written using programming languages ​​and tools supported by the provider, onto a cloud infrastructure. The consumer does not manage or control the underlying cloud infrastructure, including the network, servers, operating systems, or storage, but does have control over the deployed applications and, in some cases, the application hosting environment configuration.

[0055] Infrastructure as a Service (IaaS): The capability offered to consumers is the provisioning of processing, storage, network, and other basic computing resources on which the consumer can deploy and run any software, which may include operating systems and applications. The consumer does not manage or control the underlying cloud infrastructure, but does have control over the operating system, storage, deployed applications, and possibly limited control over select networking components (e.g., host firewalls).

[0056] The deployment model is as follows:

[0057] Private Cloud: Cloud infrastructure is operated exclusively for an organization, can be managed by that organization or a third party, and can exist on-premise or off-premise.

[0058] Community Cloud: Cloud infrastructure is shared by several organizations to support a specific community with a shared mission (e.g., mission, security requirements, policies, and compliance concerns). It may be managed by the organization or a third party and can exist on-premises or off-premises.

[0059] Public Cloud: Cloud infrastructure is made available to the general public or large industry organizations and is owned by an organization that sells cloud services.

[0060] Hybrid Cloud: A cloud infrastructure is a combination of two or more clouds (private, community, or public) that remain a unique entity, but are joined by standardized or proprietary technologies that allow data and application portability (e.g., cloud bursting for load balancing between clouds).

[0061] Cloud computing environments are service-oriented, focusing on statelessness, low coupling, modularity, and semantic interoperability. At the heart of cloud computing is an infrastructure that includes a network of interconnected nodes.

[0062] Referring now to FIG. 6, an exemplary cloud computing environment 50 is depicted. As shown, the cloud computing environment 50 includes one or more cloud computing nodes 10 that can communicate with local computing devices used by cloud consumers, such as, for example, a personal digital assistant (PDA) or mobile phone 54A, a desktop computer 54B, a laptop computer 54C, or an automotive computer system 54N, or any combination thereof. The nodes 10 can communicate with each other. They can be physically or virtually grouped in one or more networks (not shown), such as a private, community, public, or hybrid cloud, or any combination thereof, as described herein. This enables the cloud computing environment 50 to provide infrastructure, platform, and / or software as a service, eliminating the need for cloud consumers to maintain resources on their local computing devices. It will be understood that the types of computing devices 54A-N shown in FIG. 6 are intended to be exemplary only, and that the computing node 10 and cloud computing environment 50 can communicate with any type of computerized device over any type of network and / or network-addressable connection (e.g., using a web browser).

[0063] Referring now to Figure 7, a set of functional abstraction layers provided by cloud computing environment 50 (Figure 6) is shown. It should be understood in advance that the components, layers, and functions shown in Figure 7 are intended to be merely exemplary, and embodiments of the present invention are not limited thereto. As depicted, the following layers and corresponding functions are provided:

[0064] The hardware and software layer 60 includes hardware and software components. Examples of hardware components include mainframes 61, RISC (reduced instruction set computer) architecture-based servers 62, servers 63, blade servers 64, storage devices 65, and networks and networking components 66. In some embodiments, software components include network application server software 67 and database software 68.

[0065] The virtualization layer 70 provides an abstraction layer at which the following examples of virtual entities may be provided: virtual servers 71, virtual storage 72, virtual networks including virtual private networks 73, virtual applications and operating systems 74, and virtual clients 75. In one example, the management layer 80 may provide the functions described below: Resource provisioning 81 provides dynamic procurement of computing resources and other resources utilized to perform tasks within the cloud computing environment. Metering and billing 82 provides cost tracking as resources are utilized within the cloud computing environment and charging or billing for the consumption of these resources.

[0066] In one example, these resources may include application software licenses. Security provides identity verification for cloud consumers and tasks, as well as protection for data and other resources. A user portal 83 provides consumers and system administrators with access to the cloud computing environment. Service level management 84 provides allocation and management of cloud computing resources so that required service levels are met. Service level agreement (SLA) planning and execution 85 provides advance agreement on cloud computing resources in anticipation of future demand and procurement of cloud computing resources according to SLAs.

[0067] The workload layer 90 provides examples of functionality for which a cloud computing environment may be utilized. Examples of workloads and functions that may be provided from this layer include mapping and navigation 91, software development and lifecycle management 92, virtual classroom instruction delivery 93, data analytics processing 94, transaction processing 95, and automated dataset generation 96 in accordance with one or more embodiments of the present invention.

[0068] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used herein, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly dictates otherwise. It will be further understood that the terms "comprise" and / or "comprising," when used herein, specify the presence of stated features, steps, operations, elements, or components or combinations thereof, but do not exclude the presence or addition of other features, steps, operations, elements, components, or groups or combinations thereof.

[0069] At least one embodiment of the present invention may provide beneficial effects such as automatically generating a data set, for example, by processing a federated forum using artificial intelligence techniques.

[0070] While descriptions of various embodiments of the present invention have been presented for illustrative purposes, they are not intended to be exhaustive or limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope of the described embodiments. The terminology used herein has been chosen to best explain the principles of the embodiments, practical applications, or technical improvements over technologies found in the market, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. 1. A computer-implemented method comprising: acquiring conversational data from one or more federated forum sources; classifying at least a portion of the acquired conversational data into one or more categories based at least in part on one or more designated applications using a first set of one or more artificial intelligence techniques; extracting information from at least a portion of the classified conversational data regarding one or more test case related issues; validating at least a portion of the extracted information by analyzing a portion of the conversational data belonging to a plurality of entities and relating to the extracted information using a second set of one or more artificial intelligence techniques; using the verified information to generate one or more data sets related to at least one of the one or more test case-related issues for at least one of the one or more specified applications; and performing at least one automated action based at least in part on the one or more generated data sets; and Including, the method being performed by at least one computing device; 10. The computer-implemented method, wherein performing the at least one automated action includes automatically performing a feature test for at least one of the one or more specified applications using at least one of the one or more generated datasets.

2. 2. The computer-implemented method of claim 1, wherein using the first set of one or more artificial intelligence techniques comprises using at least one recurrent neural network to map the at least a portion of the acquired conversational data to one or more application tasks associated with the one or more categories.

3. 3. The computer-implemented method of claim 2, wherein using at least one recurrent neural network comprises using at least one long short-term memory (LSTM) network.

4. 2. The computer-implemented method of claim 1, wherein using the second set of one or more artificial intelligence techniques includes using at least one conditional random field-based model to process the portion of the conversational data in association with the at least some of the extracted information.

5. 2. The computer-implemented method of claim 1, wherein the one or more collaborative forum sources include at least one of one or more group chats, one or more open source communities, one or more message boards, and one or more workflow management tools.

6. The computer-implemented method of claim 1 , wherein the conversational data includes structured and unstructured data.

7. 2. The computer-implemented method of claim 1, wherein the one or more designated applications include at least one of one or more fault location applications, one or more incident similarity applications, one or more solution recommendation applications, and one or more anomaly detection applications.

8. 10. The computer-implemented method of claim 1, further comprising automatically updating the first set of one or more artificial intelligence techniques based at least in part on the one or more generated datasets.

9. 10. The computer-implemented method of claim 1, further comprising automatically updating the second set of one or more artificial intelligence techniques based at least in part on the one or more generated datasets.

10. The computer-implemented method of claim 1 , wherein software implementing the method is provided as a service in a cloud environment.

11. On the computer, acquiring conversational data from one or more federated forum sources; classifying at least a portion of the acquired conversational data into one or more categories based at least in part on one or more designated applications using a first set of one or more artificial intelligence techniques; extracting information from at least a portion of the classified conversational data regarding one or more test case related issues; validating at least a portion of the extracted information by analyzing a portion of the conversational data belonging to a plurality of entities and relating to the extracted information using a second set of one or more artificial intelligence techniques; using the verified information to generate one or more data sets related to at least one of the one or more test case-related issues for at least one of the one or more specified applications; and performing at least one automated action based at least in part on the one or more generated data sets; performing the at least one automated action includes automatically performing a feature test for at least one of the one or more specified applications using at least one of the one or more generated datasets.

12. 12. The computer program product of claim 11, wherein using the first set of one or more artificial intelligence techniques comprises using at least one recurrent neural network to map the at least a portion of the acquired conversational data to one or more application tasks associated with the one or more categories.

13. 13. The computer program product of claim 12, wherein using at least one recurrent neural network comprises using at least one LSTM network.

14. 12. The computer program product of claim 11, wherein using the second set of one or more artificial intelligence techniques comprises using at least one conditional random field-based model to process the portion of the conversational data in association with the at least some of the extracted information.

15. 12. The computer program product of claim 11, wherein the conversational data includes structured data and unstructured data.

16. 12. The computer program product of claim 11, further causing the computer to automatically update the first set of one or more artificial intelligence techniques based at least in part on the one or more generated datasets.

17. 12. The computer program product of claim 11, further causing the computer to automatically update the second set of one or more artificial intelligence techniques based at least in part on the one or more generated datasets.

18. 1. A system comprising: a memory configured to store program instructions; a processor operatively coupled to the memory, acquiring conversational data from one or more federated forum sources; classifying at least a portion of the acquired conversational data into one or more categories based at least in part on one or more designated applications using a first set of one or more artificial intelligence techniques; extracting information from at least a portion of the classified conversational data regarding one or more test case related issues; validating at least a portion of the extracted information by analyzing a portion of the conversational data belonging to a plurality of entities and relating to the extracted information using a second set of one or more artificial intelligence techniques; using the verified information to generate one or more data sets related to at least one of the one or more test case-related issues for at least one of the one or more specified applications; and performing at least one automated action based at least in part on the one or more generated data sets; and the processor executing the program instructions to perform wherein performing the at least one automated action includes automatically performing a feature test for at least one of the one or more specified applications using at least one of the one or more generated datasets.

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