Abstract
Software quality can be assured by passing the process of software testing. However, software testing process involve many phases which lead to more resources and time consumption. To reduce these downsides, one of the approaches is to adopt test case prioritization (TCP) where numerous works has indicated that TCP do improve the overall software testing performance. TCP does have several kinds of techniques which have their own strengths and weaknesses. As for this review paper, the main objective of this paper is to examine deeper on machine learning (ML) techniques based on research questions created. The research method for this paper was designed in parallel with the research questions. Consequently, 110 primary studies were selected where, 58 were journal articles, 50 were conference papers and 2 considered as others articles. For overall result, it can be said that ML techniques in TCP has trending in recent years yet some improvements are certainly welcomed. There are multiple ML techniques available, in which each technique has specified potential values, advantages, and limitation. It is notable that ML techniques has been considerably discussed in TCP approach for software testing.
Highlights
Software engineering is not just about programming and software development
The paper explored 115 studies, but only 56 studies discussed agentbased software testing, which is partially related to our review study, as this paper focuses on machine learning (ML) in Test case prioritization (TCP) software testing
As this paper come to the end, the purpose of this review paper has been achieved by answering all the research questions designated
Summary
Software engineering is not just about programming and software development. Software engineering itself is an implementation of engineering procedures in the development of any software in a systematic way [1]. Within the software development process, software testing consumes a long time for execution and can be the most expensive phase [2]. In the work of Yoo and Harman [6], various regression test approaches were examined to supplement the importance of the accumulated test suite in regression testing Those studies were classified into three domains: minimization, selection, and prioritization. There have been no reviews focusing on ML techniques within the TCP approach itself, as ML has been trending in almost all other domains. The review found that the search-based TCP using ML techniques showed the most improvement in TCP regression in several recent studies
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