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SOrTES: A Supportive Tool for Stochastic Scheduling of Manual Integration Test Cases
RISE - Research Institutes of Sweden, ICT, SICS.ORCID iD: 0000-0002-8724-9049
RISE - Research Institutes of Sweden, ICT, SICS.
Mälardalen University, Sweden.
KTH Royal Institute of Technology, Sweden.
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2019 (English)In: IEEE Access, E-ISSN 2169-3536, Vol. 7, p. 12928-12946, article id 8616828Article in journal (Refereed) Published
Abstract [en]

The main goal of software testing is to detect as many hidden bugs as possible in the final software product before release. Generally, a software product is tested by executing a set of test cases, which can be performed manually or automatically. The number of test cases which are required to test a software product depends on several parameters such as the product type, size, and complexity. Executing all test cases with no particular order can lead to waste of time and resources. Test optimization can provide a partial solution for saving time and resources which can lead to the final software product being released earlier. In this regard, test case selection, prioritization, and scheduling can be considered as possible solutions for test optimization. Most of the companies do not provide direct support for ranking test cases on their own servers. In this paper, we introduce, apply, and evaluate sOrTES as our decision support system for manual integration of test scheduling. sOrTES is a Python-based supportive tool which schedules manual integration test cases which are written in a natural language text. The feasibility of sOrTES is studied by an empirical evaluation which has been performed on a railway use-case at Bombardier Transportation, Sweden. The empirical evaluation indicates that around 40 % of testing failure can be avoided by using the proposed execution schedules by sOrTES, which leads to an increase in the requirements coverage of up to 9.6%. 

Place, publisher, year, edition, pages
2019. Vol. 7, p. 12928-12946, article id 8616828
Keywords [en]
decision support systems, dependency, integration testing, manual testing, scheduler algorithm, Software testing, stochastic test scheduling, test optimization, Artificial intelligence, Integration, Program debugging, Scheduling, Stochastic systems, Testing, Bombardier Transportation, Empirical evaluations, Natural language text, Stochastic scheduling, Test scheduling
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Natural Sciences
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URN: urn:nbn:se:ri:diva-37921DOI: 10.1109/ACCESS.2019.2893209Scopus ID: 2-s2.0-85061302207OAI: oai:DiVA.org:ri-37921DiVA, id: diva2:1293919
Note

Funding details: European Research Consortium for Informatics and Mathematics, ERCIM; Funding text 1: This work was supported in part by ECSEL & VINNOVA through projects XIVT, TESTOMAT, and MegaM@RT2, in part by the Swedish Knowledge Foundation through projects TOCSYC and TESTMINE, and in part by ERCIM ‘‘Alain Bensoussan’’ Fellowship Programme.

Available from: 2019-03-05 Created: 2019-03-05 Last updated: 2019-03-05Bibliographically approved

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Tahvili, SaharBohlin, Markus

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