Effective prediction of the fault-proneness plays a very important role in the analysis of software quality and balance of software cost, and it also is an important problem of software engineering. Importance of software quality is increasing leading to development of new sophisticated techniques, which can be used in constructing models for predicting quality attributes. Software quality prediction thus aims to evaluate software quality level periodically and to indicate software quality problems early. In this paper, we propose a novel technique to predict software quality by adopting Ant Colony Optimization (ACO) of software modules based on complexity metrics. Because only limited information of software complexity metrics is available in early software life cycle, ordinary software quality models cannot make good predictions generally. It consequently proposes an ACO-NM software model, whose characteristic is appropriate for early software quality predictions when only a small number of sample data are available. Therefore, proposed model was very useful in predicting software quality and classing the fault-proneness.

Software Quality, Computational Intelligence, Prediction, ACO, Quality Assurance.