Abstract
AbstractSoftware fault prediction techniques are helpful in developing dependable software. In this paper, we proposed a novel framework that integrates testing and prediction process for unit testing prediction. Because high fault prone metrical data are much scattered and multi-centers can represent the whole dataset better, we used artificial immune network (aiNet) algorithm to extract and simplify data from the modules that have been tested, then generated multi-centers for each network by Hierarchical Clustering. The proposed framework acquires information along with the testing process timely and adjusts the network generated by aiNet algorithm dynamically. Experimental results show that higher accuracy can be obtained by using the proposed framework.
Highlights
Software fault Prediction technology is very important for software testing because it can effectively guide the software testing and improve software quality
We propose a novel prediction method to deal with those two problems by introducing the artificial immune network algorithm[5] into the software fault estimation framework
We proposed a novel software fault prediction framework mainly used for module or unit testing
Summary
Software fault Prediction technology is very important for software testing because it can effectively guide the software testing and improve software quality. The problem arisen is that, the previous system or project has much difference with the developing system, such as the complexity, skill level of software engineers, management level and so on. We propose a novel prediction method to deal with those two problems by introducing the artificial immune network (aiNet) algorithm[5] into the software fault estimation framework. The prediction results can be obtained in the very early stage of software life cycle, because our framework can start prediction with little prior data and the aiNet algorithm can adjust its network dynamically with the data obtained increasing. The experiment results show that higher accuracy can be obtained by using the proposed framework.
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