Software

A Study on Early Prediction of Fault Proneness in Software Modules Using Genetic Algorithm

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Executive Summary

Fault-proneness of a software module is the probability that the module contains faults. To predict fault-proneness of modules different techniques have been proposed which includes statistical methods, machine learning techniques, neural network techniques and clustering techniques. The aim of proposed paper is to explore whether metrics available in the early lifecycle (i.e. requirement metrics), metrics available in the late lifecycle (i.e. code metrics) and metrics available in the early lifecycle (i.e. requirement metrics) combined with metrics available in the late lifecycle (i.e. code metrics) can be used to identify fault prone modules using Genetic Algorithm technique. This approach has been tested with real time defect C programming language datasets of NASA software projects.

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