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Effects of Negative Testing on TDD: An Industrial Experiment
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. (IS (Embedded Systems))ORCID iD: 0000-0001-8009-9052
Infosys Ltd., India.
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. (IS (Embedded Systems))ORCID iD: 0000-0001-5269-3900
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. (IS (Embedded Systems))ORCID iD: 0000-0002-5032-2310
2013 (English)In: Agile Processes in Software Engineering and Extreme Programming: 14th International Conference, XP 2013, Vienna, Austria, June 3-7, 2013. Proceedings, Springer , 2013, 91-105 p.Chapter in book (Refereed)
Abstract [en]

In our recent academic experiments, an existence of positive test bias, that is lack of negative test cases, was identified when a test driven development approach was used. At the same time, when defect detecting ability of individual test cases was calculated, it was noted that the probability of a negative test case to detect a defect was substantially higher than that of a positive test case. The goal of this study is to investigate the existence of positive test bias in test driven development within an industrial context, and measure defect detecting ability of both positive and negative test cases. An industrial experiment was conducted at Infosys Ltd. India, whose employees voluntarily signed up to participate in the study and were randomly assigned to groups utilizing test driven development, test driven development with negative testing, and test last development. Source code and test cases created by each participant during the study were collected and analysed. The collected data indicate a statistically significant difference between the number of positive and negative test cases created by industrial participants, confirming the existence of positive test bias. The difference in defect detecting ability of positive and negative test cases is also statistically significant. As a result, similarly to our previous academic study, 29% of all test cases were negative, contributing by revealing as much as 71% of all the defects found by all test cases. With this industrial experiment, we confirmed the existence of a positive test bias in an industrial context, as well as significantly higher defect detecting ability of negative test cases.

Place, publisher, year, edition, pages
Springer , 2013. 91-105 p.
Series
Lecture Notes in Business Information Processing, ISSN 1865-1348 ; 149
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:mdh:diva-21337DOI: 10.1007/978-3-642-38314-4_7ISI: 000345323500007ISBN: 978-3-642-38313-7 (print)ISBN: 978-3-642-38314-4 (print)OAI: oai:DiVA.org:mdh-21337DiVA: diva2:650010
Available from: 2013-09-19 Created: 2013-09-11 Last updated: 2015-11-04Bibliographically approved

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Causevic, AdnanPunnekkat, SasikumarSundmark, Daniel
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