An Artificial Immune System Approach for Fault Prediction in Object-Oriented Software

The features of real-time dependable systems are availability, reliability, safety and security. In the near future, real-time systems will be able to adapt themselves according to the specific requirements and real-time dependability assessment technique will be able to classify modules as faulty or fault-free. Software fault prediction models help us in order to develop dependable software and they are commonly applied prior to system testing. In this study, we examine Chidamber-Kemerer (CK) metrics and some method-level metrics for our model which is based on artificial immune recognition system (AIRS) algorithm. The dataset is a part of NASA Metrics Data Program and class-level metrics are from PROMISE repository. Instead of validating individual metrics, our mission is to improve the prediction performance of our model. The experiments indicate that the combination of CK and the lines of code metrics provide the best prediction results for our fault prediction model. The consequence of this study suggests that class-level data should be used rather than method-level data to construct relatively better fault prediction models. Furthermore, this model can constitute a part of real-time dependability assessment technique for the future.