12.4 - Machine Learning-Driven Test Case Prioritization Approaches for Black-Box Software Testing

Lachmann, Remo

doi:10.5162/ettc2018/12.4

Cited by 30 publications

(14 citation statements)

References 22 publications

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“…[134], [46], [48]-[51] 2 Clustering [10], [12], [42], [43], [53], [57], [60], [61], [69], [71], [79], [85], [21], [89], [90], [93], [101]- [103], [111], [114], [117], [118], [25], [126], [129], [130], [132], [133], [27], [31]- [35] 35…”

Section: Discussionmentioning

confidence: 99%

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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Section: Discussionmentioning

confidence: 99%

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

et al. 2021

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“…Their experimental results imply that the SVM Rank technique improves the failure detection rate significantly compared to a random order. Lachmann et al [73] takes the work further and evaluates the efficiency of ANN, K-NN, Logistic Regression, and Ensemble Methods in their application to test case prioritization of black-box tests based on natural language artefacts. Their results indicate that logistic regression outperforms the other applied ML algorithms in terms of effectiveness.…”

Section: Last and Friedmanmentioning

confidence: 99%

Artificial Intelligence in Software Testing : Impact, Problems, Challenges and Prospect

Khaliq¹,

Farooq²,

Khan³

2022

Preprint

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Artificial Intelligence (AI) is making a significant impact in multiple areas like medical, military, industrial, domestic, law, arts as AI is capable to perform several roles such as managing smart factories, driving autonomous vehicles, creating accurate weather forecasts, detecting cancer and personal assistants, etc. Software testing is the process of putting the software to test for some abnormal behaviour of the software. Software testing is a tedious, laborious and most time-consuming process. Automation tools have been developed that help to automate some activities of the testing process to enhance quality and timely delivery. Over time with the inclusion of continuous integration and continuous delivery (CI/CD) pipeline, automation tools are becoming less effective. The testing community is turning to AI to fill the gap as AI is able to check the code for bugs and errors without any human intervention and in a much faster way than humans. In this study, we aim to recognize the impact of AI technologies on various software testing activities or facets in the STLC. Further, the study aims to recognize and explain some of the biggest challenges software testers face while applying AI to testing. The paper also proposes some key contributions of AI in the future to the domain of software testing.

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“…Spieker et al [19] experiment with adaptive TCP strategies based on online reinforcement learning. Lachman [21] compares four machine learning algorithms: software vector machine rank (SVM rank), neural networks, K-nearest neighbor (KNN) and logistic regression. Besides, the author also proposes an ensemble algorithm to combine the four others.…”

Section: Artificial Intelligencementioning

confidence: 99%

“…Another challenge is to know which algorithm performs better than others in the context considered. There is a comparison between methods in the literature [21] [22], but how to know which is more adapted to the automotive context of Renault Software Factory? Which AI algorithm is more adopted to the available data?…”

Section: B Artificial Intelligence and Test Selection (Rq2)mentioning

confidence: 99%

TSAI - Test Selection using Artificial Intelligence for the Support of Continuous Integration

Meyer

2021

2021 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW)

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The agile methodology has been increasingly deployed in the industry world, breaking the process into cycles of planning, executing, and evaluating. In the software development domain, an agile method named continuous integration is widely used to automatically integrate code changes from different developers into the same software. Then, each new build can be tested to make sure that the modifications did not interfere with the rest of the already verified code. Despite being very important, regression tests are usually the costliest part of a project. It is laborious to retest all tests of each new software version due to the time it takes to perform and often, before all tests are finished, a new software version is ready to be tested. To improve regression tests results, a selection can be done. By selecting the right tests at the right moment, the use of all test catalogs can be avoided to find faults in the software tested. The aim of this work is to develop a method to select tests to be executed for each version using artificial intelligence algorithms. Learning algorithms can find patterns and similarities between test cases to help knowing which one has a higher probability to expose a fault.

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12.4 - Machine Learning-Driven Test Case Prioritization Approaches for Black-Box Software Testing

Cited by 30 publications

References 22 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

Artificial Intelligence in Software Testing : Impact, Problems, Challenges and Prospect

TSAI - Test Selection using Artificial Intelligence for the Support of Continuous Integration

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