Accenture Global Solutions Limited

Massachusetts Institute of Technology

Sunder Ranganathan Nochilur

Mahesh Venkataraman

Mallika Fernandes

Kishore P Durg

Jothi Gouthaman

Chinmaya Ranjan Jena

Venugopal S Shenoy

Srinatha Sreedhara Mulugund

Radhika Golden

Kulkarni Girish

Jeffrey Steven Wilkinson

Gopi Krishna Durbhaka

Chinmay Kumar Panda

Ramchand R Bhambhani

Akash Murumkar

Prashant Gurunathan

Swagatika Pati

Michael A Ljung

Mohan Sekhar

Sivasankar Ramalingam

Prabir GhoushalKumar

Matthias Rasking

Girish Dattatreya Kulkarni

Lalitha Yengaldas

Kalyan Kumar Veeramachaneni

Neha Patki

Jeffrey S Wilkinson

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for test cycle optimization using contextual association mapping. In one aspect, a method includes obtaining an artifact that includes a collection of reference items, where each reference item includes a sequence of words, generating candidate tags from each of the reference items based on the sequences of words in the reference items, selecting a subset of the candidate tags as context tags based on an amount that the candidate tags appear in the reference items, obtaining a sample item that includes a sequence of words, identifying a subset of the context tags in the sequence of words in the sample item, and classifying a subset of the reference items as contextually similar to the sample item based the context tags that were identified.

Test cycle optimization using contextual association mapping

The disclosed system and method for software testing provide a strategy for testing new software functionalities in a sequence that detects defects according to a testing strategy designed to accomplish a predetermined objective. Providing a pre-testing stage generates test execution results for test cases designed to find defects related to new functionalities. Using machine learning clustering techniques to identify which test cases are similar to one another facilitates organizing test cases in a sequence appropriate for accomplishing a predetermined objective.

Software test case sequencing

A system and method that improves the efficiency and accuracy of template selection process by applying machine learning to perform natural language processing (NLP) to automatically interpret the intent of the test scenarios and to match the intent of the test scenarios to appropriate test template(s). The system and method can use keyphrases and part-of-speech (POS) tokens to accurately capture the intent of test scenarios and templates. The system and method can additionally use machine learning to perform NLP to identify information from the test scenario(s) that relates to fields in templates to automatically fill in fields in the selected test template(s). In situations where the processing of the test scenario(s) does not provide all of the information necessary to fill every field of a template, the system and method can use machine learning to perform NLP to automatically create and execute a search statement to find the missing information related to empty fields in an application database.

Intelligent software testing

Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for performing autonomous self-healing of test scripts. One example process includes the actions of recording data that reflects user interactions with multiple user interface objects of a first user interface, and, for each interaction, generating a natural language statement that reflects an intent of the interaction with an associated user interface object. The process also includes storing the natural language statements in a test script, autonomously self-healing the test script for execution on a second user interface, and executing the autonomously self-healed test script on a second user interface.

Autonomous self-healing test automation

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for a touchless testing platform employed to, for example, create automated testing scripts, sequence test cases, and implement defect solutions. In one aspect, a method includes receiving a log file and testing results generated from a code base for an application; processing the log file through a pattern-mining algorithm to determine a usage pattern of code modules within the code base; clustering defects from the testing results based on a respective functionality of the application reported within each of the defects; generating testing prioritizations for test cases for the application by assigning weightages to the test cases based on the clusters of defects and the usage pattern of the code modules within the code base; sequencing a set of the test cases based on the test prioritizations; and transmitting the sequence to a test execution engine.

Test prioritization and dynamic test case sequencing

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for test cycle optimization using contextual association mapping. In one aspect, a method includes obtaining an artefact that includes a collection of reference items, where each reference item includes a sequence of words, generating candidate tags from each of the reference items based on the sequences of words in the reference items, selecting a subset of the candidate tags as context tags based on an amount that the candidate tags appear in the reference items, obtaining a sample item that includes a sequence of words, identifying a subset of the context tags in the sequence of words in the sample item, and classifying a subset of the reference items as contextually similar to the sample item based the context tags that were identified.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for automated software testing are disclosed. In one aspect, a method includes the actions of accessing plurality of test cases that each includes a plurality of test steps. The actions further include identifying a first group of modules that each include at least two sequential test steps. The actions further include comparing each module in the first group. The actions further include identifying a second group that each include the same first sequential test steps. The actions further include identifying a third group that each include the same first sequential test steps and the same second sequential test steps. The actions further include generating a fourth group. The actions further include selecting a subset of the fourth group. The actions further include updating the plurality of test cases by modularizing the test steps of the fourth group.

Automated software testing

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for a touchless testing platform employed to, for example, create automated testing scripts, sequence test cases, and implement determine defect solutions. In one aspect, a method includes the actions of receiving requirements documentation for an application through a user interface (UI), analyzing the requirements documentation to extract terminologies based on an entity term corpus; categorizing the extracted terminologies based on a corpus of known terms; generating a semantic graph from standardized statements constructed from the categorized extracted terminologies; generating a process flow map for the application by identifying processes of the application and a respective relationship between each process from the semantic graph; generating a test scenario map of test scenarios for the application from the process flow map and the semantic graph; and providing the test scenario map to a tester through the UI.

Test scenario and knowledge graph extractor

A method and system for fault analysis and prediction in an enterprise environment is described. In one embodiment, a method includes obtaining data from a plurality of sources in the enterprise environment. The plurality of sources includes at least one or more systems, users, or applications. The obtained data is associated with identifiers that include a theme selected from a set of themes and one or more keywords that are specific to each theme. The method includes generating a workflow for a user based on a session identifier and/or timestamps associated with activity by the user. The workflow identifies a time-based sequence of interactions by the user with the at least one or more systems or applications in the enterprise environment. The method also includes determining at least one fault identification or fault prediction based on the workflow and identifiers associated with the obtained data that corresponds to the workflow.

Fault analysis and prediction using empirical architecture analytics

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for a touchless testing platform employed to, for example, create automated testing scripts, sequence test cases, and implement determine defect solutions. In one aspect, a method includes the actions of receiving a log file that includes log records generated from a code base; processing the log file through a pattern mining algorithm to determine a usage pattern; generating a graphical representation based on an analysis of the usage pattern; processing the graphical representation through a machine learning algorithm to select a set of test cases from a plurality of test cases for the code base and to assign a priority value to each of the selected test cases; sequencing the set of test cases based on the priority values; and transmitting the sequenced set of test cases to a test execution engine.

Touchless testing platform

A relational database is transformed so as to obfuscate secure and/or private aspects of data contained in the database, while preserving salient elements of the data to facilitate data analysis. A restructured database is generatively modeled, and the model is sampled to create synthetic data that maintains sufficiently similar (or the same) mathematical properties and relations as the original data stored in the database. In one example, various statistics at the intersection of related database tables are determined by modeling data using an iterative multivariate approach. Synthetic data may be sampled from any part of the modeled database, wherein the synthesized data is 'realistic' in that it statistically mimics the original data in the database. The generation of such synthetic data allows publication of bulk data freely and on-demand (e.g., for data analysis purposes), without the risk of security/privacy breaches.

Growing community of inventors

Bengaluru, India

Sunder Ranganathan Nochilur