Using Efficient Machine-Learning Models to Assess Two Important Quality Factors: Maintainability and Reusability

Building efficient machine-learning assessment models is an important achievement of empirical software engineering research. Their integration in automated decision-making systems is one of the objectives of this work. It aims at empirically verify the relationships between some software internal artifacts and two quality attributes: maintainability and reusability. 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 and "glass-box" models that are very complementary.