Enhancing Software Testing using Machine Learning Techniques

SW Testing with Machine Learning

1. Enhancing Software Testing Using Machine Learning Techniques Ganeshkiran Nayak Mtech , CSE Staff Test Engineer, Western Digital

2. Bio Ganeshkiran Nayak MTech CSE graduate at RNSIT College, 2010-12 Batch Staff Test Engineer, Western Digital 11+ years of experience in Storage, Server and Team Management working on both Automation and Manual Testing. As a ‘Staff Test Engineer’, a pioneer in creating Technical Documentation, Test Plans, Test cases, Test Executio n, Defect Tracking and Reporting. Hand-on knowledge of Desktop Apps, Mobile Apps and WebApp which covers Windows & Mac OS, Android & IOS, Chrome, Edge & Mozilla browsers.

3. Abstract Proposal of the Research aims to investigate and propose the integration of machine learning (ML) techniques into software testing practices to enhance their efficiency, accuracy and effectiveness. L everage ML algorithms to Automate test case generation Defect prediction Optimization of testing methods – Regression/ Sanity/ Smoke/ Unit.

4. Literature Remo Lachman proposed an approach to prioritize system-level testing using supervised machine learning called as SVM rank which is used to learn ranked classification model. Upulee kanewala used ML to forecast the metamorphic relation. It uses the decision tree and support vector machine learning algorithm for classification.

5. Research Objectives The traditional/ conventional process of software testing couldn’t efficiently identify defect-prone codebase or optimise test coverage. Thus, ML offers leverage to historical data, patterns, and predictive analytics to improve test case generation, fault detection, and overall quality assurance. Integrating ML into the STLC resolves several challenges, including but not limited to: Test case generation Defect prediction Test priority and optimisation Real-time/ Geo adaptation

6. Proposal Design an ML model to assess the components of STLC into Data collection and Pre-processing Feature Engineering Model Selection and Development Testcase Generation and Optimization Real-time Adaptation and Continuous Learning Validation and Evaluation Integration with Testing workflow

7. Data Model using ML Data Collection Training Dataset Applying ML Develop Model Model Evaluation Model Implementation Test Data set Predict Output

8. References Role of Machine Learning in Software Testing Vedpal ; Naresh Chauhan 2021 5th International Conference on Information Systems and Computer Networks (ISCON) Enhancing Software Testing with Machine Learning Techniques V. Akila ; A . Vasuki ; J.Anita Christaline ; R . Sathiya ; Priti Rishi ; A.Shirly Edward 2023 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS)