I would like to express my deep gratitude toward my advisor, Professor Luiz Albini, for all the guidance he offered throughout the research that led to this dissertation. His frequent ideas and suggestions were what inspired me to pursue knowledge and be encouraged to find solutions to problems that are far from simple. Furthermore, I would like to thank Professor Eduardo Todt, who has helped me in ways beyond this dissertation, and my fellow students, Eric, Nelson and Ivan, who always brought interesting conversations. I would also like to thank my friends, in particular Taiane, Cainã, Douglas, Lucas and Giancarlo for bringing joy to my life for many years. Perhaps most importantly, I would like to thank my parents, Edison and Lizmari, who supported me throughout the whole process and always reminded me that I was on a path worth pursuing, as well as my grandmother Glacial and everyone in my family. Palavras-chave: redes veiculares, gerenciamento de confiança, identificação de nós maliciosos.
Context: Software regression testing refers to rerunning test cases after the system under test is modified, ascertaining that the changes have not (re-)introduced failures. Not all researchers’ approaches consider applicability and scalability concerns, and not many have produced an impact in practice. Objective: One goal is to investigate industrial relevance and applicability of proposed approaches. Another is providing a live review, open to continuous updates by the community. Method: A systematic review of regression testing studies that are clearly motivated by or validated against industrial relevance and applicability is conducted. It is complemented by follow-up surveys with authors of the selected papers and 23 practitioners. Results: A set of 79 primary studies published between 2016-2022 is collected and classified according to approaches and metrics. Aspects relative to their relevance and impact are discussed, also based on their authors’ feedback. All the data are made available from the live repository that accompanies the study. Conclusions: While widely motivated by industrial relevance and applicability, not many approaches are evaluated in industrial or large-scale open-source systems, and even fewer approaches have been adopted in practice. Some challenges hindering the implementation of relevant approaches are synthesized, also based on the practitioners’ feedback.
Test case selection (TCS) and test case prioritization (TCP) techniques can reduce time to detect the first test failure. Although these techniques have been extensively studied in combination and isolation, they have not been compared one against the other. In this paper, we perform an empirical study directly comparing TCS and TCP approaches, represented by the tools Ekstazi and FAST, respectively. Furthermore, we develop the first combination, named Fastazi, of file-based TCS and similarity-based TCP and evaluate its benefit and cost against each individual technique. We performed our experiments using 12 Java-based open-source projects. Our results show that, in the median case, the combined approach detects the first failure nearly two times faster than either Ekstazi alone (with random test ordering) or FAST alone (without TCS). Statistical analysis shows that the effectiveness of Fastazi is higher than that of Ekstazi, which in turn is higher than that of FAST. On the other hand, FAST adds the least overhead to testing time, while the difference between the additional time needed by Ekstazi and Fastazi is negligible. Fastazi can also improve failure detection in scenarios where the time available for testing is restricted. CCS CONCEPTS• Software and its engineering → Software testing and debugging.
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