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Spectrum-Based Fault Localization in Software Product Lines.pdf (360.1Kb)
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Izenburua
Spectrum-based fault localization in software product lines
Egilea
Arrieta, Aitor
Etxeberria, Leire
Markiegi, Urtzi
Sagardui, Goiuria
Egilea (beste erakunde batekoa)
Segura, Sergio
Ikerketa taldea
Ingeniería del software y sistemas
Bertsioa
Postprinta
Eskubideak
© 2018 Elsevier B. V.
Sarbidea
Sarbide bahitua
URI
https://hdl.handle.net/20.500.11984/1133
Argitaratzailearen bertsioa
https://doi.org/10.1016/j.infsof.2018.03.008
Non argitaratua
Information and Software Technology  Vol. 100. Pp. 18-31. August,
Lehenengo orria
18
Azken orria
31
Argitaratzailea
Elsevier B. V.
Gako-hitzak
Software product lines
Sprectum-based fault localization
Feature models
Debugging
Laburpena
Context: Software Product Line (SPL) testing is challenging mainly due to the potentially huge number of products under test. Most of the research on this field focuses on making testing affordable ... [+]
Context: Software Product Line (SPL) testing is challenging mainly due to the potentially huge number of products under test. Most of the research on this field focuses on making testing affordable by selecting a representative subset of products to be tested. However, once the tests are executed and some failures revealed, debugging is a cumbersome and time consuming task due to difficulty to localize and isolate the faulty features in the SPL. Objective: This paper presents a debugging approach for the localization of bugs in SPLs. Method: The proposed approach works in two steps. First, the features of the SPL are ranked according to their suspiciousness (i.e., likelihood of being faulty) using spectrum-based localization techniques. Then, a novel fault isolation approach is used to generate valid products of minimum size containing the most suspicious features, helping to isolate the cause of failures. Results: For the evaluation of our approach, we compared ten suspiciousness techniques on nine SPLs of different sizes. The results reveal that three of the techniques (Tarantula, Kulcynski2 and Ample2) stand out over the rest, showing a stable performance with different types of faults and product suite sizes. By using these metrics, faults were localized by examining between 0.1% and 14.4% of the feature sets. Conclusion: Our results show that the proposed approach is effective at locating bugs in SPLs, serving as a helpful complement for the numerous approaches for testing SPLs [-]
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