Pub Date : 2023-07-28DOI: 10.1007/s11219-023-09646-0
Dusica Marijan
There is a growing body of research indicating the potential of machine learning to tackle complex software testing challenges. One such challenge pertains to continuous integration testing, which is highly time-constrained, and generates a large amount of data coming from iterative code commits and test runs. In such a setting, we can use plentiful test data for training machine learning predictors to identify test cases able to speed up the detection of regression bugs introduced during code integration. However, different machine learning models can have different fault prediction performance depending on the context and the parameters of continuous integration testing, for example, variable time budget available for continuous integration cycles, or the size of test execution history used for learning to prioritize failing test cases. Existing studies on test case prioritization rarely study both of these factors, which are essential for the continuous integration practice. In this study, we perform a comprehensive comparison of the fault prediction performance of machine learning approaches that have shown the best performance on test case prioritization tasks in the literature. We evaluate the accuracy of the classifiers in predicting fault-detecting tests for different values of the continuous integration time budget and with different lengths of test history used for training the classifiers. In evaluation, we use real-world and augmented industrial datasets from a continuous integration practice. The results show that different machine learning models have different performance for different size of test history used for model training and for different time budgets available for test case execution. Our results imply that machine learning approaches for test prioritization in continuous integration testing should be carefully configured to achieve optimal performance.
{"title":"Comparative study of machine learning test case prioritization for continuous integration testing","authors":"Dusica Marijan","doi":"10.1007/s11219-023-09646-0","DOIUrl":"https://doi.org/10.1007/s11219-023-09646-0","url":null,"abstract":"There is a growing body of research indicating the potential of machine learning to tackle complex software testing challenges. One such challenge pertains to continuous integration testing, which is highly time-constrained, and generates a large amount of data coming from iterative code commits and test runs. In such a setting, we can use plentiful test data for training machine learning predictors to identify test cases able to speed up the detection of regression bugs introduced during code integration. However, different machine learning models can have different fault prediction performance depending on the context and the parameters of continuous integration testing, for example, variable time budget available for continuous integration cycles, or the size of test execution history used for learning to prioritize failing test cases. Existing studies on test case prioritization rarely study both of these factors, which are essential for the continuous integration practice. In this study, we perform a comprehensive comparison of the fault prediction performance of machine learning approaches that have shown the best performance on test case prioritization tasks in the literature. We evaluate the accuracy of the classifiers in predicting fault-detecting tests for different values of the continuous integration time budget and with different lengths of test history used for training the classifiers. In evaluation, we use real-world and augmented industrial datasets from a continuous integration practice. The results show that different machine learning models have different performance for different size of test history used for model training and for different time budgets available for test case execution. Our results imply that machine learning approaches for test prioritization in continuous integration testing should be carefully configured to achieve optimal performance.","PeriodicalId":21827,"journal":{"name":"Software Quality Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135556996","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-23DOI: 10.1007/s11219-023-09644-2
Antonio Vallecillo, Ricardo Pérez-Castillo, Joost Visser
{"title":"Guest editorial: special issue on “IT quality challenges in a digital society”","authors":"Antonio Vallecillo, Ricardo Pérez-Castillo, Joost Visser","doi":"10.1007/s11219-023-09644-2","DOIUrl":"https://doi.org/10.1007/s11219-023-09644-2","url":null,"abstract":"","PeriodicalId":21827,"journal":{"name":"Software Quality Journal","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2023-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45060454","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-17DOI: 10.1007/s11219-023-09637-1
Morena Barboni, A. Morichetta, A. Polini, F. Casoni
{"title":"ReSuMo: a regression strategy and tool for mutation testing of solidity smart contracts","authors":"Morena Barboni, A. Morichetta, A. Polini, F. Casoni","doi":"10.1007/s11219-023-09637-1","DOIUrl":"https://doi.org/10.1007/s11219-023-09637-1","url":null,"abstract":"","PeriodicalId":21827,"journal":{"name":"Software Quality Journal","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2023-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43737624","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-17DOI: 10.1007/s11219-023-09641-5
Jing Chen, Dongjin Yu, Haiyang Hu
{"title":"Towards an understanding of memory leak patterns: an empirical study in Python","authors":"Jing Chen, Dongjin Yu, Haiyang Hu","doi":"10.1007/s11219-023-09641-5","DOIUrl":"https://doi.org/10.1007/s11219-023-09641-5","url":null,"abstract":"","PeriodicalId":21827,"journal":{"name":"Software Quality Journal","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2023-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46624085","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-09DOI: 10.1007/s11219-023-09629-1
Raymon van Dinter, C. Catal, G. Giray, B. Tekinerdogan
{"title":"Just-in-time defect prediction for mobile applications: using shallow or deep learning?","authors":"Raymon van Dinter, C. Catal, G. Giray, B. Tekinerdogan","doi":"10.1007/s11219-023-09629-1","DOIUrl":"https://doi.org/10.1007/s11219-023-09629-1","url":null,"abstract":"","PeriodicalId":21827,"journal":{"name":"Software Quality Journal","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2023-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47669582","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-05DOI: 10.1007/s11219-023-09642-4
Iqra Batool, T. Khan
{"title":"Software fault prediction using deep learning techniques","authors":"Iqra Batool, T. Khan","doi":"10.1007/s11219-023-09642-4","DOIUrl":"https://doi.org/10.1007/s11219-023-09642-4","url":null,"abstract":"","PeriodicalId":21827,"journal":{"name":"Software Quality Journal","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2023-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43212503","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-26DOI: 10.1007/s11219-023-09632-6
M. Serrano, L. E. Sánchez, Antonio Santos-Olmo, David García-Rosado, C. Blanco, Vita Santa Barletta, D. Caivano, E. Fernández-Medina
{"title":"Minimizing incident response time in real-world scenarios using quantum computing","authors":"M. Serrano, L. E. Sánchez, Antonio Santos-Olmo, David García-Rosado, C. Blanco, Vita Santa Barletta, D. Caivano, E. Fernández-Medina","doi":"10.1007/s11219-023-09632-6","DOIUrl":"https://doi.org/10.1007/s11219-023-09632-6","url":null,"abstract":"","PeriodicalId":21827,"journal":{"name":"Software Quality Journal","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46833711","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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