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Informative Article | Computer Technology | India | Volume 7 Issue 3, March 2018 | Popularity: 5.9 / 10

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Optimizing Automated Software Testing with Machine Learning Techniques

Satish Kathiriya, Rajath Karangara, Narayana Challla

Abstract: This paper investigates the application of machine learning (ML) techniques to enhance the efficiency of software testing automation systems. Recognizing the escalating complexities and the critical need for quality assurance in software development, our study focuses on leveraging ML algorithms to refine the testing process. The methodology encompasses a comparative analysis of conventional testing methods against our ML - integrated approach, measuring performance through accuracy, execution speed, and resource utilization metrics. Our findings reveal a notable enhancement in testing efficiency, with the ML model proficiently identifying and rectifying software anomalies. This advancement signifies a pivotal shift towards more intelligent, adaptable, and efficient testing mechanisms in software development. The research underscores the transformative potential of ML in software testing, proposing a new paradigm for future explorations in this domain. The implications extend beyond immediate testing improvements, providing a foundational approach for continuous advancement in software quality assurance.

Keywords: Software testing, Manual Testing, Automation Testing, Machine learning

Edition: Volume 7 Issue 3, March 2018

Pages: 1960 - 1964

DOI: https://www.doi.org/10.21275/SR24304113021

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