AI emerging trend in QA Sanjeev Kumar Jha

AI emerging trend in QA Sanjeev Kumar Jha, Senior Consultant Amit Paspunattu, Manager Capgemini Technology Services 1

Abstract Artificial intelligence (AI) is improving QA efficiencies beyond the reach of traditional practices. AI algorithms learn from test assets to provide intelligent insights like application stability, failure patterns, defect hotspots, failure prediction, etc. These insights will help QA anticipate, automate, and amplify decision-making capabilities, thereby building quality early in the project lifecycle. This paper focuses on providing the insight value of AI in Software testing that is currently emerging trend in QA. Research was carried out to find scope of AI concepts in software testing in upcoming trends to ensure customer reliability and satisfaction. 2

Agenda Introduction of Artificial Intelligence(AI) AI concepts that enhance testing process AI Makes QA Smart Benefit Case Study Conclusion 3

Introduction of Artificial Intelligence (AI) • Artificial Intelligence is a way of making a computer, a computercontrolled robot, or a software think intelligently, in the similar manner the intelligent humans think. • Approaches include statistical methods, computational intelligence, and traditional symbolic • AI has been dominated in various fields such as Gaming, Natural Language Processing, Expert Systems, Vision Systems, Speech Recognition, Handwriting Recognition, and Medical Diagnosis & Intelligent Robots 4

AI concepts that enhance testing process Traditional QA Process: Enhanced QA Process by using AI concepts: 5

Algorithms: • Algorithms are used for calculation, data processing, and automated reasoning • Algorithms have been useful in identifying how patterns emerge in nature, other correlations generated by algorithms have been more suspect • Few of the concepts K Mean Cluster, Dendogram, Association Rules, Apriori algorithm, correlation, Hypothesis Analysis, Network Analysis, Cluster Coefficient, Linear Regression, Logistic Regression, Auto correlation, Correlogram, Decision Tree, Random forest used in developing the smart system 6

AI makes QA Smart: We can develop an end-to-end ecosystem that explores, evolves and makes decisions based on cognitive and analytics capability from our own testing system. This will develop the smart assets which can self-monitor, selfcorrect and evolve based on associated environmental factors The smart asset can be a smart test case, smart test environment, smart test data or smart test strategy. There will be smart integrations between components by using set of rules of engagement between assets. The smart test case can define the required environment and the data set required for execution. Similarly, the context could define the type or quantity of testing 7

Benefits Increased customer satisfaction Improved quality – Prediction, prevention, and automation using self-learning algorithms Faster time to market – Significant reduction in efforts with complete end to end test coverage Cognitively – Scientific approach for defect localization, aiding early feedback with unattended execution Traceability – Missing test coverage against requirement as well as, identifying dead test cases for modified or redundant requirement Security – The Driving Force AI concepts can be used in scope of performance & security testing of application. 8

Benefits Skilled resources. Resources need to be skilled in the AI concepts & processes to enhance the testing activities to be more effective. Increased productivity and client retention Testing is key factor if done right; provide a good user experience that enriches a brand leads to more users, and ultimately more growth. Accuracy & Quality AI is changing the software testing industry in enhancing accuracy & quality of the Application. 9

Case Study: Development Testing: AI concept is used in performing development testing in TFS Check-in/Check-out process. ØImpact analysis with all related methods define in the code where changes made ØIdentify coverage and risk associated with code changes. ØIdentify and run any tests that represent the methods that are impacted. 10

Conclusion ØThis paper focuses on providing the insight value of AI in Software testing that is currently emerging trend in QA. ØArtificial intelligence (AI) is improving QA efficiencies beyond the reach of traditional practices. AI algorithms learn from test assets to provide intelligent insights like application stability, failure patterns, defect hotspots, failure prediction, etc. ØThese insights will help QA anticipate, automate, and amplify decision-making capabilities, thereby building quality early in the project lifecycle. 11

References & Appendix http: //comp. utm. my/wp-content/uploads/2013/04/Intelligent-and -Automated-Software-Testing-Methods-Classification. pdf http: //in. bgu. ac. il/en/engn/ise/QT/Documents/Artificial%20 Intell igence%20 Techniques%20 to%20 Improve%20 Software%20 Test ing-PPT. pdf http: //usir. salford. ac. uk/2208/2/meziane_chapter_meziane_boo k. pdf http: //www 0. cs. ucl. ac. uk/staff/mharman/raise 12. pdf http: //research. ijcaonline. org/volume 90/number 19/pxc 3894637. pdf 12

Author Biography Sanjeev Kumar Jha Senior Consultant Email id: - Sanjeev. b. jha@Capgemini. com Co Author detail: Amit Kumar Paspunattu Manager Email id: - amitkumar. paspunattu@Capgemini. com Capgemini Office Address : -IT Park 1, 115 / 32&35 | Nanakram Guda | Gachibowli | Hyderabad - 500032 | INDIA 13

Thank You!!! 14