Quentin Perez

Romain Lefeuvre, Maïwenn Le Goasteller, Jessie Galasso et al.

Empirical software engineering research often depends on datasets of code repository artifacts, where sampling strategies are employed to enable large-scale analyses. The design and evaluation of these strategies are critical, as they directly influence the generalizability of research findings. However, sampling remains an underestimated aspect in software engineering research: we identify two main challenges related to (1) the design and representativeness of sampling approaches, and (2) the ability to reason about the implications of sampling decisions on generalizability. To address these challenges, we propose a Domain-Specific Language (DSL) to explicitly describe complex sampling strategies through composable sampling operators. This formalism supports both the specification and the reasoning about the generalizability of results based on the applied sampling strategies. We implement the DSL as a Python-based fluent API, and demonstrate how it facilitates representativeness reasoning using statistical indicators extracted from sampling workflows. We validate our approach through a case study of MSR papers involving code repository sampling. Our results show that the DSL can model the sampling strategies reported in recent literature.

Quentin Perez, Pierre-Antoine Jean, Christelle Urtado et al.

Nowadays, development teams often rely on tools such as Jira or Bugzilla to manage backlogs of issues to be solved to develop or maintain software. Although they relate to many different concerns (e.g., bug fixing, new feature development, architecture refactoring), few means are proposed to identify and classify these different kinds of issues, except for non mandatory labels that can be manually associated to them. This may lead to a lack of issue classification or to issue misclassification that may impact automatic issue management (planning, assignment) or issue-derived metrics. Automatic issue classification thus is a relevant topic for assisting backlog management. This paper proposes a binary classification solution for discriminating bug from non bug issues. This solution combines natural language processing (TF-IDF) and classification (multi-layer perceptron) techniques, selected after comparing commonly used solutions to classify issues. Moreover, hyper-parameters of the neural network are optimized using a genetic algorithm. The obtained results, as compared to existing works on a commonly used benchmark, show significant improvements on the F1 measure for all datasets.