A Survey on Regression Testing Using Nature-Inspired Approaches

Bajaj, Anu; Sangwan, Om Prakash

doi:10.1109/ccaa.2018.8777692

Cited by 18 publications

(8 citation statements)

References 32 publications

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“…Reinforcement Learning [14], [20], [73], [78], [86], [96]- [98], [107], [38]- [41], [59], [62], [63], [66] 19 4 Regression [24], [54], [64], [65], [67], [99], [131] 7 5…”

Section: Discussionmentioning

confidence: 99%

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Trend Application of Machine Learning in Test Case Prioritization: A Review on Techniques

et al. 2021

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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.

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“…Reinforcement Learning [14], [20], [73], [78], [86], [96]- [98], [107], [38]- [41], [59], [62], [63], [66] 19 4 Regression [24], [54], [64], [65], [67], [99], [131] 7 5…”

Section: Discussionmentioning

confidence: 99%

“…As search based TCP approach has been quite popular in recent years [1], [20], [21], the application of AI in TCP was then suggested to be assessed in a comprehensive context. Since AI quite big to be cover in single review study, only ML techniques taxonomy in TCP will be covered.…”

Section: B What Is the Research Trend Of Machine Learning In Tcp? (Rq11)mentioning

confidence: 99%

Trend Application of Machine Learning in Test Case Prioritization: A Review on Techniques

et al. 2021

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“…Henceforth, the researchers started looking into nature, how nature maintains the optimum balance, and now they are trying to find optimal orderings with the help of nature-inspired algorithms. The researchers have used various Nature-Inspired algorithms in the TCP domain [5]. Inspired by the evolution process, John Holland [6] proposed a genetic algorithm (GA), which became a well-tested meta-heuristic technique.…”

Section: Test Case Prioritization (Tcp) Helps In Sorting Only Thosementioning

confidence: 99%

“…Sometimes there are test cases that are not required at a particular version but can be useful in later versions of a software [11]. In other words, prioritization is safer than permanent removal [5], i.e., TCP is a secure, reliable, and cost-effective method for regression testing. Therefore, researchers focus is more on TCP than the test suite minimization or test case selection [1].…”

Section: A Test Case Prioritizationmentioning

confidence: 99%

“…While the progression of research, the preliminary literature review showed that to the best of our information, no SLR had been published so far on the topic of TCP using GA. Considering the increasing use of GA in TCP [5], we believe that it is high time to consolidate and synthesize existing research evidence from the relevant studies. Moreover, we have also classified primary studies into seven classes, which assists in formulating the basis for answering the research questions.…”

Section: ) Need For Slrmentioning

confidence: 99%

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A Systematic Literature Review of Test Case Prioritization Using Genetic Algorithms

Bajaj

Sangwan

2019

IEEE Access

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Regression testing is the essential process of software maintenance and evolution phase of the software development life cycle for assuring the quality and reliability of updated software. Test case prioritization is the technique of regression testing to reduce the time and effort required for regression testing. Search-based algorithms are used to enhance the efficiency and effectiveness of the method. Among these search-based optimization algorithms, genetic algorithms are becoming more popular among researchers since the last decade. In this paper, we are doing a systematic literature review, i.e., a secondary study of test case prioritization using genetic algorithms. The objective of this review is to examine and classify the current state of use of the genetic algorithm in test case prioritization. In other words, to give a base for the advancement of test case prioritization research using genetic algorithms. With the use of the systematic literature review protocol, we selected the most relevant studies (20 out of 384) from the appropriate repositories by using a set of search keywords, inclusion/exclusion criteria and the quality assessment of studies. The data extraction and synthesis process and the taxonomic classification are used to answer the research questions. We also performed a rigorous analysis of the techniques by comparing them on research methodology, the prioritization method, dataset specification, test suite size, types of genetic algorithms used, performance metrics, and the validation criteria. The whole process took four months for comprehensive analysis and classification of primary studies. We observed that the parameter settings, the type of operators, the probabilistic rate of operators, and fitness function design have a significant impact on the quality of the solutions obtained. This systematic literature review yields that genetic algorithms have great potential in solving test case prioritization problems, and the area is open for further improvements. Future researchers can fill the research gaps by following the suggestions given in the review. From this review, we found that the use of the appropriate approach can make a genetic algorithm based test case prioritization one of the effective methods in regression testing.INDEX TERMS Genetic algorithm, NSGA-II, regression testing, systematic review, test case prioritization.

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A novel test case prioritization approach for black‐box testing based on K‐medoids clustering

Chen

Cai

et al. 2023

J Software Evolu Process

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Regression testing is an essential and expensive process in software testing. However, there may be insufficient resources for the execution of all test cases during regression testing. Test case prioritization (TCP) techniques improve the efficiency of regression testing by adjusting the test case execution sequence. Traditional TCP techniques usually rely on the historical execution information of the software under test for more efficient results. String distance‐based TCP (SD‐TCP) avoids these limitations; it uses only the textual difference information of the test cases themselves for prioritization. However, the time overhead on the sorting process of this method is not ideal, and the extreme test case inputs have an impact on the stability of the method. To address these problems, we propose a novel test case prioritization strategy, it first classifies the test cases more finely using the K‐medoids algorithm and then transforms the set into subsequences and improves the early diversity by greedy sorting within clusters. Finally, the test cases are selected through a polling strategy to compose the execution sequence. Extensive experimental results demonstrate that the proposed approach outperforms SD‐TCP in better time efficiency on test case prioritization; it also has a higher average percentage of fault detected (APFD) value than random prioritization (RP) and SD‐TCP.

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A Survey on Regression Testing Using Nature-Inspired Approaches

Cited by 18 publications

References 32 publications

Trend Application of Machine Learning in Test Case Prioritization: A Review on Techniques

Trend Application of Machine Learning in Test Case Prioritization: A Review on Techniques

A Systematic Literature Review of Test Case Prioritization Using Genetic Algorithms

A novel test case prioritization approach for black‐box testing based on K‐medoids clustering

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