Accenture Global Solutions Limited

Mallika Fernandes

Mahesh Venkataraman

Kishore P Durg

Jothi Gouthaman

Sunder Ranganathan Nochilur

Venugopal S Shenoy

Chinmaya Ranjan Jena

Srinatha Sreedhara Mulugund

Kulkarni Girish

Sivasankar Ramalingam

Matthias Rasking

Sunder Nochilur Ranganathan

Radhika Golden

Gopi Krishna Durbhaka

Jeffrey Steven Wilkinson

Swagatika Pati

Michael A Ljung

Ramchand R Bhambhani

Chinmay Kumar Panda

Mahesh Venkata Raman

Akash Murumkar

Prashant Gurunathan

Bhaskar Ghosh

Rohit Shrikant Patwardhan

Rahul Ghanashyam Joglekar

Arati Deo

Teresa Escrig

Jeffrey S Wilkinson

Bachu Mohan

Pandiaraj Balaravisekar

Murali Krishna Rao Koppaka

An automated, dynamic system and method of testing infrastructure-as-code (IaC). The system is configured to validate infrastructure provisioned in multi-cloud environments and is able to accommodate any cloud provider. Implementation of such as system can eliminate manual errors, as well as enable early detection of errors (i.e., before production deployment), thus empowering early 'go live'. Furthermore, the proposed embodiments are configured to integrate with already existing devOps pipelines for rapid test execution and can run as many times as needed with minimal configuration, allowing for creation and execution of complex scenarios to test low-level validation upon cloud infrastructure setup.

System and method for cloud infrastructure test automation

An autonomous system and method of comprehensively testing serverless applications. The system is configured to automatically generate test scripts and test data based on deployed and modified function code and configuration files. The system is configured to work in multi-cloud environments and is able to accommodate any cloud provider. Implementation of such a system can eliminate manual errors. Furthermore, the proposed embodiments are configured to integrate with already existing devOps pipelines for rapid test execution, and can continue automated testing in real-time as modifications to the app are made.

System and method for serverless application testing

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

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 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 device identifies training data and scoring data for a model, and removes bias from the training data to generate unbiased training data. The device trains the model with the unbiased training data to generate trained models, and processes the trained models, with the scoring data, to generate scores for the trained models. The device selects a trained model, from the trained models, based on model metrics and the scores, and processes a training sample, with the trained model, to generate first results, wherein the training sample is created based on the unbiased training data and production data. The device processes a production sample, with the trained model, to generate second results, wherein the production sample is created based on the production data and the training sample. The device provides the trained model for use in a production environment based on the first results and the second results.

Training, validating, and monitoring artificial intelligence and machine learning models

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

Growing community of inventors

Bengaluru, India

Mallika Fernandes