Machine-Learning Models for Software Quality: A Compromise between Performance and Intelligibility

Building powerful machine-learning assessment models is an important achievement of empirical software engineering research, but it is not the only one. Intelligibility of such models is also needed, especially, in a domain, software engineering, where exploration and knowledge capture is still a challenge. Several algorithms, belonging to various machine-learning approaches, are selected and run on software data collected from medium size applications. Some of these approaches produce models with very high quantitative performances, others give interpretable, intelligible, and "glass-box" models that are very complementary. We consider that the integration of both, in automated decision-making systems for assessing software product quality, is desirable to reach a compromise between performance and intelligibility.