Abstract

Effectiveness and clarity of software objects, their adherence to coding standards and programming habits of programmers are important features of overall quality of software systems. This paper proposes an approach towards a quantitative software quality assessment with respect to extensibility, reusability, clarity and efficiency. It exploits techniques of Computational Intelligence (CI) that are treated as a consortium of granular computing, neural networks and evolutionary techniques. In particular, we take advantage of self-organizing maps to gain a better insight into the data, and study genetic decision trees -- a novel algorithmic framework to carry out classification of software objects with respect to their quality. Genetic classifiers serve as a "quality filter" for software objects. Using these classifiers, a system manager can predict quality of software objects and identify low quality objects for review and possible revision. The approach is applied to an object-oriented visualization-based software system for biomedical data analysis.

Citations