To Generate Rule for Software Defect Prediction on Quantitative and Qualitative Factors Using Artificial Neural Network

Date Added: Dec 2011
Format: PDF

Fault-proneness of a software module is the probability that the module contains faults. A correlation exists between the fault-proneness of the software and the measurable attributes of the code (i.e., the static metrics) and of the testing (i.e., the dynamic metrics). Static code metrics such as Halstead complexity, Cyclomatic complexity, McCabe's complexity measure are inefficient to measure quality, The use of single features of software to predict faults is uninformative. Therefore, Artifical Neural Network is used for software defect prediction. An Artificial Neural Network (ANN) is an information-processing paradigm that is inspired by the way a biological nervous system in human brain works.