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Predicting and Estimating Execution Time of Manual Test Cases - A Case Study in Railway Domain
Mälardalen University, School of Innovation, Design and Engineering.
2017 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Testing plays a vital role in the software development life cycle by verifying and validating the software's quality. Since software testing is considered as an expensive activity and due to thelimitations of budget and resources, it is necessary to know the execution time of the test cases for an efficient planning of test-related activities such as test scheduling, prioritizing test cases and monitoring the test progress. In this thesis, an approach is proposed to predict and estimate the execution time of manual test cases written in English natural language. The method uses test specifications and historical data that are available from previously executed test cases. Our approach works by obtaining timing information from each and every step of previously executed test cases. The collected data is used to estimate the execution time for non-executed test cases by mapping them using text from their test specifications. Using natural language processing, texts are extracted from the test specification document and mapped with the obtained timing information. After estimating the time from this mapping, a linear regression analysis is used to predict the execution time of non-executed test cases. A case study has been conducted in Bombardier Transportation (BT) where the proposed method is implemented and the results are validated. The obtained results show that the predicted execution time of studied test cases are close to their actual execution time.

Place, publisher, year, edition, pages
2017. , 34 p.
Keyword [en]
Software Testing, Prediction, Estimation, Manual testing, Execution time, NLP, Linear Regression, Test Specification, Optimization, Priortization.
National Category
Software Engineering
Identifiers
URN: urn:nbn:se:mdh:diva-35929OAI: oai:DiVA.org:mdh-35929DiVA: diva2:1114982
External cooperation
Research Institute of Sweden RISE ICT / SICS Västerås
Subject / course
Computer Science
Presentation
2017-06-01, Lambda, Mälardalen University, Västerås, 09:55 (English)
Supervisors
Examiners
Available from: 2017-08-11 Created: 2017-06-26 Last updated: 2017-08-11Bibliographically approved

Open Access in DiVA

Master Thesis Report(1743 kB)67 downloads
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Type fulltextMimetype application/pdf

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Ameerjan, Sharvathul Hasan
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CiteExportLink to record
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  • apa
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