Software quality analysis with the use of computational intelligence

• Published in: Information and software technology Vol. 45; no. 7; pp. 405 - 417
• Main Authors: Reformat, Marek, Pedrycz, Witold, Pizzi, Nicolino J
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.05.2003; Elsevier Science Ltd
• Subjects: Alliances; Artificial intelligence; Classification; Computational intelligence; Data visualization; Decision trees; Knowledge extraction; Object oriented programming; Quality assurance; Self-organizing maps; Software; Software measures; Software quality; Software quality assessment; Studies; Visualization; Software quality assessment; Software measures; Self-organizing maps; Data visualization; Classification; Decision trees; Knowledge extraction; Computational intelligence
• ISSN: 0950-5849; 1873-6025
• DOI: 10.1016/S0950-5849(03)00012-0

Quality of individual objects composing a software system is one of the important factors that determine quality of this system. Quality of objects, on the other hand, can be related to a number of attributes, such as extensibility, reusability, clarity and efficiency. These attributes do not have representations suitable for automatic processing. There is a need to find a way to support quality related activities using data gathered during quality assurance processes, which involve humans. This paper proposes an approach, which can be used to support quality assessment of individual objects. The approach exploits techniques of Computational Intelligence that are treated as a consortium of granular computing, neural networks and evolutionary techniques. In particular, self-organizing maps and evolutionary-based developed decision trees are used to gain a better insight into the software data and to support a process of classification of software objects. Genetic classifiers—a novel algorithmic framework—serve as ‘filters’ for software objects. These classifiers are built on data representing subjective evaluation of software objects done by humans. 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 analyze an object-oriented visualization-based software system for biomedical data analysis.