Introducing dependency, exception and encapsulation metrics for object-oriented defect prediction: Complementarity with CK/MOOD metrics through empirical machine learning and statistical validation

TL;DR

A set of ten novel object-oriented metrics designed to enrich existing datasets and enhance prediction performance are proposed, demonstrating that the proposed metrics capture complementary aspects of software quality not fully represented by traditional metrics, leading to more accurate and robust defect prediction models.