Module-order modeling using an evolutionary multi-objective optimization approach

The problem of quality assurance is important for software systems. The extent to which software reliability improvements can be achieved is often dictated by the amount of resources available for the same. A prediction for risk-based rankings of software modules can assist in the cost-effective delegation of the limited resources. A module-order model (MOM) is used to gauge the performance of the predicted rankings. Depending on the software system under consideration, multiple software quality objectives may be desired for a MOM; e.g., the desired rankings may be such that if 20% of modules were targeted for reliability enhancements then 80% of the faults would be detected. In addition, it may also be desired that if 50% of modules were targeted then 100% of the faults would be detected. Existing works related to MOM(s) have used an underlying prediction model to obtain the rankings, implying that only the average, relative, or mean square errors are minimized. Such an approach does not provide an insight into the behavior of a MOM, the performance of which focusses on how many faults are accounted for by the given percentage of modules enhanced. We propose a methodology for building MOM (s) by implementing a multiobjective optimization with genetic programming. It facilitates the simultaneous optimization of multiple performance objectives for a MOM. Other prediction techniques, e.g., multiple linear regression and neural networks, cannot achieve multiobjective optimization for MOM(s). A case study of a high-assurance telecommunications software system is presented. The observed results show a new promise in the modeling of goal-oriented software quality estimation models.