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Improving Rule Set Based Software Quality Prediction: A Genetic Algorithm-based Approach Salah Bouktif, Dept. of Computer Science and Op. Res., University of Montreal, Canada Danielle Azar and Doina Precup, School of Computer Science, McGill University, Montreal, Canada Houari Sahraoui and Balázs Kégl, Dept. of Computer Science and Op. Res., University of Montreal, Canada

TOOLS USA 2003 PROCEEDINGS PDF Version

Abstract

The object-oriented (OO) paradigm has now reached maturity. OO software products are becoming more complex which makes their evolution effort and time consuming. In this respect, it has become important to develop tools that allow assessing the stability of OO software (i.e., the ease with which a software item can evolve while preserving its design). In general, predicting the quality of OO software is a complex task. Although many predictive models are proposed in the literature, we remain far from having reliable tools that can be applied to real industrial systems. The main obstacle for building reliable predictive tools for real industrial systems is the lackof representative samples. Unlike other domains where such samples can be drawn from available large repositories of data, in OO software the lack of such repositories makes it hard to generalize, to validate and to reuse existing models. Since universal models do not exist, selecting an appropriate quality model is a difficult, non-trivial decision for a company. In this paper, we propose two general approaches to solve this problem. They consist of combining/adapting a set of existing models. The process is driven by the context of the target company. These approaches are applied to OO software stability prediction.

About the authors

		Salah Bouktif is a Ph.D. student at the department of computer science and operational research of the University of Montreal. His research interests relate to the software quality, metrics and software prediction models. Salah can be reached at bouktifs@iro.umontreal.ca.
		Danielle Azar (dazar@cs.mcgill.ca) is a Ph.D. candidate in the School of Computer Science at McGill University in Montreal, Canada. Her main areas of interest include genetic algorithms and their application in software engineering, particularly in the optimization of software quality estimation models.
		Houari Sahraoui received a Ph.D. in Computer Science from Pierre Marie Curie University, Paris in 1995. He is currently an associate professor at the department of computer science and operational research, University of Montreal where he is leading the software engineering group. His research interests include object-oriented software quality and software reverse and re-engineering. He can be reached at sahraouh@iro.umontreal.ca.
		Balázs Kéglreceived the Ph.D. degree in computer science (with honors) from Concordia University, Montreal, Canada, in 1999. His is currently with the Department of Computer Science and Operational Research at the University of Montreal as an assistant professor. His research interests include statistical pattern recognition, machine learning, and image processing. He can be reached at kegl@iro.umontreal.ca.
		Doina Precup received her PhD in Computer Science from the University of Massachusetts, Amherst,in 2000. In July 2000 she joined the School of Computer Science at McGill University. Doina Precup’s research interests lie mainly in the field of machine learning. She is especially interested in the learning problems that face a decision-maker interacting with a complex, uncertain environment. She can be reached at dprecup@cs.mcgill.ca.

Cite this article as follows: Salah Bouktif et al.: "Improving Rule Set Based Software Quality Prediction: A Genetic Algorithm-based Approach", in Journal of Object Technology, vol. 3, no. 4, April 2004,Special issue: TOOLS USA 2003, pp. 227-241. http://www.jot.fm/issues/issue_2004_04/article13