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ESPRET: A tool for execution time estimation of manual test cases
RISE - Research Institutes of Sweden, ICT, SICS. Mälardalen University, Sweden.ORCID iD: 0000-0002-8724-9049
Mälardalen University, Sweden.
RISE - Research Institutes of Sweden, ICT, SICS.ORCID iD: 0000-0002-1512-0844
RISE - Research Institutes of Sweden, ICT, SICS.
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2018 (English)In: Journal of Systems and Software, ISSN 0164-1212, E-ISSN 1873-1228, Vol. 146, p. 26-41Article in journal (Refereed) Published
Abstract [en]

Manual testing is still a predominant and an important approach for validation of computer systems, particularly in certain domains such as safety-critical systems. Knowing the execution time of test cases is important to perform test scheduling, prioritization and progress monitoring. In this work, we present, apply and evaluate ESPRET (EStimation and PRediction of Execution Time) as our tool for estimating and predicting the execution time of manual test cases based on their test specifications. Our approach works by extracting timing information for various steps in manual test specification. This information is then used to estimate the maximum time for test steps that have not previously been executed, but for which textual specifications exist. As part of our approach, natural language parsing of the specifications is performed to identify word combinations to check whether existing timing information on various test steps is already available or not. Since executing test cases on the several machines may take different time, we predict the actual execution time for test cases by a set of regression models. Finally, an empirical evaluation of the approach and tool has been performed on a railway use case at Bombardier Transportation (BT) in Sweden.

Place, publisher, year, edition, pages
2018. Vol. 146, p. 26-41
Keywords [en]
Execution time, Manual testing, Optimization, Regression analysis, Software testing, Test specification, Forecasting, Safety engineering, Safety testing, Specifications, Bombardier Transportation, Empirical evaluations, Estimation and predictions, Natural language parsing, Safety critical systems, Test specifications
National Category
Natural Sciences
Identifiers
URN: urn:nbn:se:ri:diva-35572DOI: 10.1016/j.jss.2018.09.003Scopus ID: 2-s2.0-85053193472OAI: oai:DiVA.org:ri-35572DiVA, id: diva2:1261235
Note

Funding details: 20130085; Funding details: 20160139; Funding details: MegaM@RT2, VINNOVA; Funding details: Bombardier; Funding text: ECSEL & VINNOVA (through projects MegaM@RT2 & TESTOMAT) and the Swedish Knowledge Foundation (through the projects TOCSYC (20130085) and TestMine (20160139)) have supported this work

Available from: 2018-11-06 Created: 2018-11-06 Last updated: 2018-11-06Bibliographically approved

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Tahvili, SaharSaadatmand, Mehrdad

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