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A comprehensive study of pseudo-tested methods
Inria Rennes Bretagne Atlantique, Campus Beaulieu 263 Ave Gen Leclerc, F-35042 Rennes, France..
Inria Lille Nord Europe, Parc Sci Haute Borne 40,Ave Halley Bat A Pk Plaza, F-59650 Villeneuve Dascq, France..
KTH, School of Electrical Engineering and Computer Science (EECS), Theoretical Computer Science, TCS.ORCID iD: 0000-0003-3505-3383
KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
2019 (English)In: Journal of Empirical Software Engineering, ISSN 1382-3256, E-ISSN 1573-7616, Vol. 24, no 3, p. 1195-1225Article in journal (Refereed) Published
Abstract [en]

Pseudo-tested methods are defined as follows: they are covered by the test suite, yet no test case fails when the method body is removed, i.e., when all the effects of this method are suppressed. This intriguing concept was coined in 2016, by Niedermayr and colleagues, who showed that such methods are systematically present, even in well-tested projects with high statement coverage. This work presents a novel analysis of pseudo-tested methods. First, we run a replication of Niedermayr's study with 28K+ methods, enhancing its external validity thanks to the use of new tools and new study subjects. Second, we perform a systematic characterization of these methods, both quantitatively and qualitatively with an extensive manual analysis of 101 pseudo-tested methods. The first part of the study confirms Niedermayr's results: pseudo-tested methods exist in all our subjects. Our in-depth characterization of pseudo-tested methods leads to two key insights: pseudo-tested methods are significantly less tested than the other methods; yet, for most of them, the developers would not pay the testing price to fix this situation. This calls for future work on targeted test generation to specify those pseudo-tested methods without spending developer time.

Place, publisher, year, edition, pages
Springer, 2019. Vol. 24, no 3, p. 1195-1225
Keywords [en]
Software testing, Software developers, Pseudo-tested methods, Test quality, Program analysis
National Category
Software Engineering
Identifiers
URN: urn:nbn:se:kth:diva-255215DOI: 10.1007/s10664-018-9653-2ISI: 000472043800005Scopus ID: 2-s2.0-85053658099OAI: oai:DiVA.org:kth-255215DiVA, id: diva2:1348179
Funder
Wallenberg AI, Autonomous Systems and Software Program (WASP)
Note

QC 20190903

Available from: 2019-09-03 Created: 2019-09-03 Last updated: 2019-09-13Bibliographically approved

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Monperrus, MartinBaudry, Benoit

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