Using structural and textual information to capture feature coupling in object-oriented software
Empirical Software Engineering
How much information do software metrics contain?
Proceedings of the 3rd ACM SIGPLAN workshop on Evaluation and usability of programming languages and tools
Studying software evolution using topic models
Science of Computer Programming
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The paper presents two novel conceptual metrics for measuring coupling and cohesion in software systems. Our first metric, Conceptual Coupling between Object classes (CCBO), is based on the well-known CBO coupling metric, while the other metric, Conceptual Lack of Cohesion on Methods (CLCOM5), is based on the LCOM5 cohesion metric. One advantage of the proposed conceptual metrics is that they can be computed in a simpler (and in many cases, programming language independent) way as compared to some of the structural metrics. We empirically studied CCBO and CLCOM5 for predicting fault-proneness of classes in a large open source system and compared these metrics with a host of existing structural and conceptual metrics for the same task. As the result, we found that the proposed conceptual metrics, when used in conjunction, can predict bugs nearly as precisely as the 58 structural metrics available in the Columbus source code quality framework and can be effectively combined with these metrics to improve bug prediction.