An empirical study of source code complexity and source code modifications during testing and maintenance.

Since maintenance is the most expensive phase of the software life cycle, detecting most of the errors as early as possible in the software development effort can provide substantial savings. This study investigates the behavior of complexity metrics during testing and maintenance, and their relationship to modifications made to the software. Interface complexity causes most of the change activities during integration testing and maintenance, while size causes most of the changes during unit testing. Principal component analysis groups 16 complexity metrics into four domains. Changes in domain pattern are observed throughout the software life cycle. Using those domains as input, regression analysis shows that software complexity measures collected as early as the unit testing phase can identify and predict change prone modules. With a low rate of misclassification, discriminant analysis further confirms that complexity metrics provide a strong indication of the changes made to a module during testing and maintenance.