2026-04-21T00:20:44Zhttps://ebiltegia.mondragon.edu/oai/request

oai:ebiltegia.mondragon.edu:20.500.11984/11332024-03-04T17:08:32Zcom_20.500.11984_473col_20.500.11984_478

Spectrum-based fault localization in software product lines Arrieta, Aitor Etxeberria, Leire Markiegi, Urtzi Sagardui, Goiuria Segura, Sergio Software product lines Sprectum-based fault localization Feature models Debugging 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 2019-01-16T11:24:49Z 2019-01-16T11:24:49Z 2018 http://purl.org/coar/resource_type/c_6501 0950-5849 https://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=147761 https://hdl.handle.net/20.500.11984/1133 eng © 2018 Elsevier B. V. Elsevier B. V.