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Improving continuous integration with similarity-based test case selection
Chalmers, Sweden; University of Gothenburg, Sweden.
Linköping University, Department of Computer and Information Science, Software and Systems. Linköping University, Faculty of Science & Engineering. (PELAB - Program Environment Laboratory)
Linköping University, Department of Computer and Information Science, Software and Systems. Linköping University, Faculty of Science & Engineering. (PELAB - Programming Environment Laboratory)
Linköping University, Department of Computer and Information Science, Software and Systems. Linköping University, Faculty of Science & Engineering. (PELAB - Programming Environment Laboratory)
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2018 (English)In: Proceedings of the 13th International Workshop on Automation of Software Test, New York: ACM Digital Library, 2018, p. 39-45Conference paper, Published paper (Refereed)
Abstract [en]

Automated testing is an essential component of Continuous Integration (CI) and Delivery (CD), such as scheduling automated test sessions on overnight builds. That allows stakeholders to execute entire test suites and achieve exhaustive test coverage, since running all tests is often infeasible during work hours, i.e., in parallel to development activities. On the other hand, developers also need test feedback from CI servers when pushing changes, even if not all test cases are executed. In this paper we evaluate similarity-based test case selection (SBTCS) on integration-level tests executed on continuous integration pipelines of two companies. We select test cases that maximise diversity of test coverage and reduce feedback time to developers. Our results confirm existing evidence that SBTCS is a strong candidate for test optimisation, by reducing feedback time (up to 92% faster in our case studies) while achieving full test coverage using only information from test artefacts themselves.

Place, publisher, year, edition, pages
New York: ACM Digital Library, 2018. p. 39-45
Series
International Workshop on Automation of Software Test, ISSN 2377-8628
Keywords [en]
Similarity based test case selection, Continuous integration, Automated testing
National Category
Software Engineering
Identifiers
URN: urn:nbn:se:liu:diva-152002DOI: 10.1145/3194733.3194744ISI: 000458922700009ISBN: 978-1-4503-5743-2 (electronic)OAI: oai:DiVA.org:liu-152002DiVA, id: diva2:1255676
Conference
AST'18 2018 ACM/IEEE 13th International Workshop on Automation of Software Test
Note

Funding agencies: Chalmers Software Center7 [30]

Available from: 2018-10-14 Created: 2018-10-14 Last updated: 2022-08-23
In thesis
1. Contributions to Improving Feedback and Trust in Automated Testing and Continuous Integration and Delivery
Open this publication in new window or tab >>Contributions to Improving Feedback and Trust in Automated Testing and Continuous Integration and Delivery
2022 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

An integrated release version (also known as a release candidate in software engineering) is produced by merging, building, and testing code on a regular basis as part of the Continuous Integration and Continuous Delivery (CI/CD) practices. Several benefits, including improved software quality and shorter release cycles, have been claimed for CI/CD. On the other hand, recent research has uncovered a plethora of problems and bad practices related to CI/CD adoption, necessitating some optimization. Some of the problems addressed in this work include the ability to respond to practitioners’ questions and obtain quick and trustworthy feedback in CI/CD. To be more specific, our effort concentrated on: 1) identifying the information needs of software practitioners engaged in CI/CD; 2) adopting test optimization approaches to obtain faster feedback that are realistic for use in CI/CD environments without introducing excessive technical requirements; 3) identifying perceived causes and automated root cause analysis of test flakiness, thereby providing developers with guidance on how to resolve test flakiness; and 4) identifying challenges in addressing information needs, providing faster and more trustworthy feedback. 

The findings of the research reported in this thesis are based on data from three single-case studies and three multiple-case studies. The research uses quantitative and qualitative data collected via interviews, site visits, and workshops. To perform our analyses, we used data from firms producing embedded software as well as open-source repositories. The following are major research and practical contributions. 

  • Information Needs: The initial contribution to research is a list of information needs in CI/CD. This list contains 27 frequently asked questions on continuous integration and continuous delivery by software practitioners. The identified information needs have been classified as related to testing, code & commit, confidence, bug, and artifacts. We investigated how companies deal with information needs, what tools they use to deal with them, and who is interested in them. We concluded that there is a discrepancy between the identified needs and the techniques employed to meet them. Since some information needs cannot be met by current tools, manual inspections are required, which adds time to the process. Information about code & commit, confidence level, and testing is the most frequently sought for and most important information. 
  • Evaluation of Diversity Based Techniques/Tool: The contribution is to conduct a detailed examination of diversity-based techniques using industry test cases to determine if there is a difference between diversity functions in selecting integrationlevel automated test. Additionally, how diversity-based testing compares to other optimization techniques used in industry in terms of fault detection rates, feature coverage, and execution time. This enables us to observe how coverage changes when we run fewer test cases. We concluded that some of the techniques can eliminate up to 85% of test cases (provided by the case company) while still covering all distinct features/requirements. The techniques are developed and made available as an open-source tool for further research and application. 
  • Test Flakiness Detection, Prediction & Automated Root Cause Analysis: We identified 19 factors that professionals perceive affect test flakiness. These perceived factors are divided into four categories: test code, system under test, CI/test infrastructure, and organizational. We concluded that some of the perceived factors of test flakiness in closed-source development are directly related to non-determinism, whereas other perceived factors concern different aspects e.g., lack of good properties of a test case (i.e., small, simple and robust), deviations from the established  processes, etc. To see if the developers’ perceptions were in line with what they had labelled as flaky or not, we examined the test artifacts that were readily available. We verified that two of the identified perceived factors (i.e., test case size and simplicity) are indeed indicative of test flakiness. Furthermore, we proposed a light weight technique named trace-back coverage to detect flaky tests. Trace-back coverage was combined with other factors such as test smells indicating test flakiness, flakiness frequency and test case size to investigate the effect on revealing test flakiness. When all factors are taken into consideration, the precision of flaky test detection is increased from 57% (using single factor) to 86% (combination of different factors). 
Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2022. p. 219
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 2247
National Category
Computer Sciences
Identifiers
urn:nbn:se:liu:diva-187765 (URN)10.3384/9789179294236 (DOI)9789179294229 (ISBN)9789179294236 (ISBN)
Public defence
2022-10-06, Ada Lovelace, B-huset, Campus Valla, Linköping, 13:15
Opponent
Supervisors
Note

Funding:Linköping University and Software Center, project 18 (Data Visualization in CI/CD) and project 30 (Aspects of Automated Testing).

Available from: 2022-08-23 Created: 2022-08-23 Last updated: 2022-09-22Bibliographically approved

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