An FIS for early detection of defect prone modules
ICIC'09 Proceedings of the Intelligent computing 5th international conference on Emerging intelligent computing technology and applications
Review: Software fault prediction: A literature review and current trends
Expert Systems with Applications: An International Journal
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For the management of a software development project, a software quality prediction model is very helpful since it can provide the management with useful information needed for decision-makings. Many software quality prediction models and techniques have been proposed and studied in the literature. Nevertheless, the complicated situations of software development process call for a more flexible model that can cater for all factors that have impact on the quality of the target software, including the characteristics of the software product, the characteristics of the development process and the operation conditions. In this paper, a software quality prediction model based on a fuzzy neural network is presented. The proposed model is a hybrid model of Artificial Neural Network (ANN) and Fuzzy Logic (FL), which exploits the advantages of ANN and FL while eliminating their limitations. The model exhibits some favorable features such as being able to deal with objective data collected in the software development process as well as knowledge/experiences obtained from experts or from similar projects, which is the main information that is available in the early phases of a software development process. Using this model, early prediction of software quality becomes feasible and the management can have knowledge of the quality of target software product as early as possible, which helps to identify design errors and avoid expensive rework. Experimental results show that for problems of small-to-medium scale, the proposed model can be easily trained and thus can be of practical use.