A decision tree logic based recommendation system to select software fault prediction techniques
Identifying a reliable fault prediction technique is the key requirement for building effective fault prediction model. It has been found that the performance of fault prediction techniques is highly dependent on the characteristics of the fault dataset. To mitigate this issue, researchers have evaluated and compared a plethora of fault prediction techniques by varying the context in terms of domain information, characteristics of input data, complexity, etc. However, the lack of an accepted benchmark makes it difficult to select fault prediction technique for a particular context of prediction. In this paper, we present a recommendation system that facilitates the selection of appropriate technique(s) to build fault prediction model. First, we have reviewed the literature to elicit the various characteristics of the fault dataset and the appropriateness of the machine learning and statistical techniques for the identified characteristics. Subsequently, we have formalized our findings and built a recommendation system that helps in the selection of fault prediction techniques. We performed an initial appraisal of our presented system and found that proposed recommendation system provides useful hints in the selection of the fault prediction techniques.
Computing