A SYSTEMATIC REVIEW OF THE EMPIRICAL VALIDATION OF OBJECT-ORIENTED METRICS TOWARDS FAULT-PRONENESS PREDICTION.

Object-oriented (00) approaches of software development promised better maintainable and reusable systems, but the complexity resulting from its features usually introduce some faults that are difficult to detect or anticipate during software change process. Thus, the earlier they are detected, found and fixed, the lesser the maintenance costs. Several 0 0 metrics have been proposed for assessing the quality of 0 0 design and code and several empirical studies have been undertaken to validate the impact of 0 0 metrics on fault proneness (FP). The question now is which metrics are iiseful in measuring the FP of 0 0 classes? Gonsequently, we investigate the existing empirical validation of GK + SLOG metrics based on their state of significance, validation and usefulness. We used systematic hterature review (SLR) methodology over a number of relevant article sources, and our results show the existence of 29 relevant empirical studies. Further analysis indicates that coupling, complexity and size measures have strong impact on FP of OO classes. Based on the results, we therefore conclude that these metrics can be used as good predictors for building quality fault models when that could assist in focusing resources on high risk components that are liable to cause system failures, when only GK + SLOG metrics are used. [ABSTRACT FROM AUTHOR]