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Spectrum-based fault localization in software product lines

Artículo relevante publicado en 2018 en el ISTContext: Software Product Line (SPL) testing is challenging mainly due to the potentially huge number ofproducts under test. Most of the research on this field focuses on making testing affordable by selecting arepresentative 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 featuresin 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 theirsuspiciousness (i.e., likelihood of being faulty) using spectrum-based localization techniques. Then, a novel faultisolation 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 differentsizes. 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, faultswere 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 helpfulcomplement for the numerous approaches for testing SPLs.

Feature Modeling to deal with Variability in Business Process Perspectives

The construction of Business Process (BP) models entails big challenges, especially when BPs contain many variations. In addition, BPs can be seen from different perspectives, i.e., the behavioral (i.e., control-flow), the organizational (i.e., resources distribution), or the informational (data-flow) perspectives among others. Depending on the context where the BP is taken place, we may found variability in any of these perspectives. Different approaches to model variability in BP perspectives have already been proposed. However, these approaches are highly tight to the modeling language. In addition, they focus mainly on the behavioral perspective. To deal with variability in other BP perspectives in a more flexible manner, this work proposes an approach based on feature models. These models do not only allow enhancing expressiveness regarding BP variability, but also the maintenance and understanding of the resulting process model.