The inherent uncertainty of software gives a vague and imprecise solution when it is solved by human judgment. As the project expands, the issues of missing data values, outlier detection, feature subset selection and prediction of faultiness behaviour should be addressed. The feature selection process may lead to the production of high-dimensional data sets that may contribute to many irrelevant or redundant features. In this paper, we focussed on the optimal feature subset selection and fault prediction at the early stage of a project. We propose the novel approach of grey relational analysis (GRA) from grey system theory by optimizing the grey relational grade function using biogeography optimization referred to as B-GRA. The proposed algorithm gives resilience to users to select features for both continuous and categorical attributes. The issues such as feature subset selection, heterogeneity of data sets, outlier analysis and fault prediction are addressed, and then, B-GRA and GRA approaches on five publically available data sets are evaluated using statistical and machine learning techniques. Experimental results show significant results indicating that the proposed methodology can be used for the prediction of faults and produce conceivable results when compared with the GRA feature selection approach.
