Application of machine learning on software quality assurance and testing: A chronological survey

Hongkai Chen, Mohammad Hossain

Research output: Contribution to journalConference articlepeer-review


Ensuring the quality is essential for a successful Software System. Software systems need to be tested in every stage of the Software Development Life Cycle (SDLC) irrespective of the type of software being developed. If a software bug remains undetected in the early phase of the SDLC, it becomes harder to fix it at a later stage and becomes very costly. The application of machine learning in Software Quality Assurance and Testing can help testers in the testing process, including the early detection and prediction of a software bug. However, employing machine learning techniques brings new challenges to testing and quality assurance. Machine Learning (ML) uses Artificial Intelligence (AI) techniques that focus on a given dataset to find any trend present in the data. It has been observed that some software testing activities can, in fact, be represented as a learning problem. Thus, ML can be used as an efficient tool to automate softwaretesting activities, especially when the software system becomes very complex. This survey aims to study and summarize the application of machine learning on software quality assurance and testing in a chronological manner by selecting from articles published in the last twenty-six years or so.

Original languageEnglish (US)
Pages (from-to)42-52
Number of pages11
JournalEPiC Series in Computing
StatePublished - 2022
Event37th International Conference on Computers and Their Applications, CATA 2022 - Virtual, Online
Duration: Mar 21 2022Mar 23 2022

Bibliographical note

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