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Software Fault Prediction using Computational Intelligence Techniques: A Survey
Objectives: Software fault prediction is a vital activity in software development to make software more efficient, economic and produce quality software. This is a fruitful approach to decreases the testing efforts (cost and time) of the software testing. Methods: Existence of faults not only reduces the standard of the software, but also increases its development cost and time. In this paper, the detailed survey has done on software fault prediction for categorize software modules as faulty or non-faulty. The goal of this paper is to find the techniques that has used in software fault prediction. The computational intelligence techniques like soft computing, data mining and machine learning based software fault prediction approaches have included. The classification accuracy rate of different methods which is used for fault prediction has explored. Finding: From the survey, it is found that the fuzzy logic and McCabe based metrics are better way to handle the software fault prediction. The McCabe metrics has independent from the programming language. It performs good result for different size of the software. Application: This survey will helpful to select the model which have high accuracy rate for early phase software testing to provide the quality software. The survey also helps to reduce the cost and time of software testing.
Fuzzy Logic, Software Testing, Software Fault Prediction, Software Fault Detection, Soft Computing
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