A Predictive Approach to Estimate Software Defects Density Using Weighted Artificial Neural Networks for the Given Software Metrics
Context Applications of software have penetrated into every part of human life and have become significant in versatile areas. A case of negligence could cost a great loss of economy or even to life (Pentium FDIV bug "Statistical Analysis of Floating Point Flaw: Intel White Paper" (PDF) (2004) [1] The Helminthiasis of the Internet [2] and hence the reliability of the functions of an application is of extreme importance. A software error could be a deviation from the process flow, improper requirement definitions, or ambiguity in defining the constraints (IEEE Standard Glossary of Software Engineering Terminology (1990) [3] To avoid the lateral effects, the applications should be evaluated beforehand for all defects in all phases of the software cycle. Objective This paper emphasizes on the prediction of software defect density using weighted neural networks and comparison with the fuzzy logic based approach. Method Each design phase is divided into the neural network node. Software metrics are chosen for each stage and fed into the nodes with a weight associated to it. Neural network system are associated with input layers, SW DDC layer (stage wise defect density calculation layer) and C DDC (consolidated defect density calculation layer) Results The accuracy of the predicted defects is very close the actual defects. Validation results show this method is very accurate than the current methods like fuzzy implementation. Conclusion Weighted artificial networks is used to predict the software defect density and is been verified on different real time data sets ranging from very low project sizes to the very high project size.