Object-oriented class maintainability prediction using internal quality attributes

Context: Class maintainability is the likelihood that a class can be easily modified. Before releasing an object-oriented software system, it is impossible to know with certainty when, where, how, and how often a class will be modified. At that stage, this likelihood can be estimated using the internal quality attributes of a class, which include cohesion, coupling, and size. To reduce the future class maintenance efforts and cost, developers are encouraged to carefully test and well document low maintainability classes before releasing the object-oriented system. Objective: We empirically study the relationship between internal class quality attributes (size, cohesion, and coupling) and an external quality attribute (class maintainability). Using statistical techniques, we also construct models based on the selected internal attributes to predict class maintainability. Method: We consider classes of three open-source systems. For each class, we account for two actual maintainability indicators, the number of revised lines of code and the number of revisions in which the class was involved. Using 19 internal quality measures, we empirically explore the impact of size, cohesion, and coupling on class maintainability. We also empirically investigate the abilities of the measures, considered both individually and combined, to estimate class maintainability. Statistically based prediction models are constructed and validated. Results: Our results demonstrate that classes with better qualities (i.e., higher cohesion values and lower size and coupling values) have better maintainability (i.e., are more likely to be easily modified) than those of worse qualities. Most of the considered measures are shown to be predictors of the considered maintainability indicators to some degree. The abilities of the considered internal quality measures to predict class maintainability are improved when the measures are combined using optimized multivariate statistical models. Conclusion: The prediction models can help software engineers locate classes with low maintainability. These classes must be carefully tested and well documented.