DOI:10.20894/IJBI.
Periodicity: Bi Annual.
Impact Factor:
SJIF:5.065
Submission:Any Time
Publisher: IIR Groups
Language: English
Review Process:
Double Blinded
Fault propensity of software is the prospect that the component contains faults. To forecast fault proneness of modules different techniques have been proposed which includes statistical methods, machine learning techniques, etc. Machine learning techniques can be used to analyze data from different perspectives and enable developers to retrieve useful information. Machine learning techniques are proven to be useful in terms of software bug prediction. This is leading to increase in development of machine learning methods for exploring data sets, which can be used in constructing models for predicting quality attributes such as fault proneness. This research work analyzed various fault prediction techniques and proposed a new algorithm GMFPA (Genetic Metric Fault Prediction Algorithm) to explore the fault prone modules by using metrics. The GMFPA is used to identify fault with highest gain value by forming the hypothesis set.
