International conference on software engineering and knowledge engineering: Session chair

The Thirtieth International Conference on Software Engineering and Knowledge Engineering (SEKE 2018) will be held at the Hotel Pullman, San Francisco Bay, USA, from July 1 to July 3, 2018. SEKE2018 will also be dedicated in memory of Professor Lofti Zadeh, a great scholar, pioneer and leader in fuzzy sets theory and soft computing. The conference aims at bringing together experts in software engineering and knowledge engineering to discuss on relevant results in either software engineering or knowledge engineering or both. Special emphasis will be put on the transference of methods between both domains. The theme this year is soft computing in software engineering & knowledge engineering. Submission of papers and demos are both welcome

Web applications testing techniques: a systematic mapping study

Due to the importance of Web application testing techniques for detecting faults and assessing quality attributes, many research papers were published in this field. For this reason, it became essential to analyse, classify and summarize the research in the field. The main goal of this research is to provide a classification or categorization of Web applications testing techniques or approaches to help researchers and practitioners to understand the current state-of-the-art in this field and find it easier to focus their research on the areas that had received less attention. To achieve this goal, this research conducted a systematic mapping study on 98 research papers in the field of Web applications testing published between 2008 and 2021. This mapping study resulted in a classification schema that categorizes the papers in this field into: model-based testing category, security testing category, and other types of testing categories. In model-based testing of Web applications, research papers were classified according to the model used for test data generation, while the research papers in the field of Web applications security testing were classified according to the targeted vulnerability. The results showed that the most commonly used Web applications testing techniques in literature are model-based testing and security testing. Besides, the most commonly used models in model-based testing are finite-state machines. The most targeted vulnerability in security testing is SQL injection. Test automation is the most targeted testing goal in both model-based and security testing. For other Web applications testing techniques, the main goals of testing were test automation, test coverage, and assessing security quality attributes

Empirical Assessment of Generating Adversarial Configurations for Software Product Lines

International audienceSoftware product line (SPL) engineering allows the derivation of products tailored to stakeholders' needs through the setting of a large number of configuration options. Unfortunately, options and their interactions create a huge configuration space which is either intractable or too costly to explore exhaustively. Instead of covering all products, machine learning (ML) approximates the set of acceptable products (e.g., successful builds, passing tests) out of a training set (a sample of configurations). However, ML techniques can make prediction errors yielding non-acceptable products wasting time, energy and other resources. We apply adversarial machine learning techniques to the world of SPLs and craft new configurations faking to be acceptable configurations but that are not and vice-versa. It allows to diagnose prediction errors and take appropriate actions. We develop two adversarial configuration generators on top of state-of-the-art attack algorithms and capable of synthesizing configurations that are both adversarial and conform to logical constraints. We empirically assess our generators within two case studies: an industrial video synthesizer (MOTIV) and an industry-strength, open-source Web-appconfigurator (JHipster). For the two cases, our attacks yield (up to) a 100% misclassification rate without sacrificing the logical validity of adversarial configurations. This work lays the foundations of a quality assurance framework for ML-based SPLs