The Accuracy of Early Fault Prediction in Modified Code

Software systems are normally developed in a number of releases. Each release usually modifies existing code. In this study we show that such modified code can be an important source of faults. Since faults are considered major cost drivers of software projects, the ability to identify fault-prone classes before they are implemented would give a chance to apply some preventive measures, which could bring significant savings on project costs. To achieve that, however, the prediction model available early in the development process would have to be accurate. In this study we compare the accuracy of fault prediction models available before and after the system is implemented. We find that fault prediction models that are available after the system is implemented are about 34% more accurate compared to models available before the system is implemented. We discover that the higher accuracy of the prediction models available after the system is implemented is caused by the metric that describes the size of the class modification. This metric is a code metric that is available only after the system is developed. As further work, we suggest defining design metrics that describe the characteristics of modifications and evaluating their applicability to predict faults in modified code.