Identifying effective software metrics using genetic algorithms

Various software metrics may be used to quantify object-oriented source code characteristics in order to assess the quality of the software. This type of software quality assessment may be viewed as a problem of classification: given a set of objects with known features (software metrics) and group labels (quality rankings), design a classifier that can predict the quality rankings of new objects using only the software metrics. We have obtained a variety of software measures for a Java application used for biomedical data analysis. A system architect has ranked the quality of the objects as low, medium-low, medium or high with respect to maintainability. A commercial program was used to parse the source code identifying 16 metrics. A genetic algorithm (GA) was implemented to determine which subset of the various software metrics gave the best match to the quality ranking specified by the expert. By selecting the optimum metrics for determining object quality, GA-based feature selection offers an insight into which software characteristics developers should try to optimize.