Acquiring soft and project skills during their studies is of paramount importance for computer science students to integrate large development teams after graduating. Project-oriented learning offers interesting opportunities for teachers to tutor students, and allows them to acquire and train those skills in addition to the core topics of the course. However, since most existing curricula require courses to be as independent as possible (for organizational reasons for instance), some topics are covered in different courses in slightly different ways. This repetition is interesting for understanding difficult notions appropriately, but may also hamper students' understanding when closely related concepts are embedded in different ways. We report here on our teaching approach: we propose a series of projects that share a common theme, in order to (i) provide a transversal understanding of common notions seen in separate courses, and (ii) introduce soft and project skills in a progressive way, enabling students to iteratively experience and learn skills that are necessary for professional life. We report on the results of interviews conducted with the students and extract valuable lessons for reproducing this approach in different curricula.@en

This study presents the initial step towards a thorough analysis of the difficulty to reproduce a crash using searchbased crash reproduction. Traditionally, code size and complexity are considered representative indicators of the difficulty for search-based approaches, like search-based unit test generation, to generate tests. However, unlike unit test generation, crash reproduction does not seek to cover a set of behaviors but instead to generate one or more tests exercising a specific behavior reproducing a given crash. In this context, there is no guarantee that the indicators used for unit testing are still valid for crash reproduction. In this study, we seek to identify such indicators by considering various code metrics, code smells, and change metrics. We report our effort to collect those metrics for JCRASHPACK, a state-of-the-art crash reproduction benchmark, and an initial assessment by considering metrics individually. Our results show that although JCRASHPACK is larger than benchmarks used in previous studies, additional crashes should be added to improve its diversity and representativeness, and that no individual metric can be used to characterize the difficulty to reproduce a crash. @en

In the past years, everyday life has been profoundly transformed by the development and widespread of digital technologies. General, as well as specialized audiences, have to face an ever-increasing amount of knowledge and learn new abilities. This first edition of the EASEAI workshop tried to address that challenge by looking at software engineering, education, and artificial intelligence research fields to explore how they can be combined. Specifically, we brought together researchers, teachers, and practitioners who use advanced software engineering tools and artificial intelligence techniques in education. And researchers and teachers in education science who address the problem of improving the awareness regarding digital technologies through a transgenerational and transdisciplinary range of students.@en

Welcome to the second edition of the International Workshop on Education through Advanced Software Engineering and Artificial Intelligence (EASEAI 2020) to be held virtually, November 9, 2020, co-located with ESEC/FSE 2020. In the past years, with the development and widespread of digital technologies, everyday life has been profoundly transformed. The general public, as well as specialized audiences, have to face an ever-increasing amount of knowledge and learn new abilities. The EASEAI workshop addresses that challenge by looking at software engineering, education, and artificial intelligence research fields to explore how they can be combined. Specifically, this workshop brings together researchers, teachers, and practitioners who use advanced software engineering tools and artificial intelligence techniques in the education field and through a transgenerational and transdisciplinary range of students to discuss the current state of the art and practices, and establish new future directions. In total, EASEAI 2020 received 13 submissions, out of which 5 papers were accepted after a thorough reviewprocess. Three members of the program committee reviewed each submission. We also received two presentation abstracts, both selected for presentation during the workshop. We sincerely thank the program committee members, authors, and participants who will make EASEAI an exciting and successful event!@en