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Generic and industrial scale many-criteria regression test selection
Mid Sweden University, Faculty of Science, Technology and Media, Department of Communication, Quality Management, and Information Systems (2023-).ORCID iD: 0000-0001-9372-3416
2023 (English)In: Journal of Systems and Software, ISSN 0164-1212, E-ISSN 1873-1228, Vol. 205, article id 111802Article in journal (Refereed) Published
Abstract [en]

While several test case selection algorithms (heuristic and optimal) and formulations (linear and non-linear) have been proposed, no multi-criteria framework enables Pareto search — the state-of-the-art approach of doing multi-criteria optimization. Therefore, we introduce the highly parallelizable, openly available Many-Criteria Test-Optimization Algorithm (MC-TOA) framework that combines heuristic Pareto search and optimality gap knowledge per criterion. MC-TOA is largely agnostic to the criteria formulations and can incorporate many criteria where existing approaches offer limited scope (single or few objectives/constraints), lack flexibility in the expression and assurance of constraints, or run into problem complexity issues. For two large-scale systems with up to six criteria and thousands of system test cases, MC-TOA not only produces, over the board, superior Pareto fronts in terms of HVI score compared to the state-of-the-art many-objective heuristic baseline, it also does that within minutes of runtime for worst-case executions, i.e., assuming that a regression affects the entire test-suite. MC-TOA depends on convex solvers. We find that the evaluated open-source solvers are slower but suffice for smaller systems, while being less robust for larger systems. Linear formulations execute faster and obtain near-optimal results, which led to faster and better overall convergence of MC-TOA compared to integer formulations. Editor's note: Open Science material was validated by the Journal of Systems and Software Open Science Board. 

Place, publisher, year, edition, pages
Elsevier, 2023. Vol. 205, article id 111802
Keywords [en]
Industrial-scale optimization, Regression testing, Software testing, Test case selection
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:miun:diva-49086DOI: 10.1016/j.jss.2023.111802ISI: 001050843400001Scopus ID: 2-s2.0-85166227149OAI: oai:DiVA.org:miun-49086DiVA, id: diva2:1788949
Available from: 2023-08-17 Created: 2023-08-17 Last updated: 2023-09-01Bibliographically approved

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Dobslaw, Felix

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