Software reuse has the potential to improve the productivity and quality of software significantly. Systematic reuse is a promising means to reduce the development time and cost of software by leveraging existing reusable software artifacts. There are many factors, both technical and nontechnical, which influence the successful reuse in software development. Identifying as well as adopting these factors in a company-wide reuse practice is essential for project success. Towards this end, data mining is an established approach to extract relevant information in order to aid decision making. In this paper, we have proposed an approach to identify key factors for systematic reuse in software projects using data mining. We have used popular data mining algorithms to discover the most important factors which predict the success and failure of software reuse projects.

In most engineering disciplines, systems are designed by using existing components. Software engineering recognizes this technique to achieve better software more quickly at a lower cost. Software reuse may take different forms, from ad hoc to systematic approaches. It may span from small building blocks such as function or class to larger components and subsystems. It not only involves code but also various artifacts in a software project (Kruger, 1992). Systematic software reuse has been recognized as the key strategy for increasing the productivity and improving quality, possibly with a higher payoff than process improvement or process automation in software industry (Bohem, 1993). However, successful implementation of software reuse is practically difficult. This can be attributed to several factors, both technical and nontechnical, relating to a software project as well as the organization.

There have been adequate research efforts to address the technical aspects of software reuse. This may be in terms of finding development methodologies to create reusable software components, creating repositories, developing tools for search and retrieval of reusable artifacts. As technical knowhow in software industry becomes matured, nontechnical factors such as organization, process, business drivers and human involvement appeared to be equally important for successful software reuse. Factual evidence to support the impact of these factors is still scarce or contradictory (Frakes and Isoda, 1994). Further, the key factors which influence the success or failure of a reuse program are difficult to find. In this work, we used data mining technique to find key factors that predict the success or failure of reuse program in a software project.

The rest of the paper is organized as follows: Section 2 presents the methodological approach proposed in the paper. Section 3 describes briefly the dataset used and the preprocessing undertaken. Section 4 presents the design of the experiment. The results are discussed in Section 5, finally followed by the conclusion.

Information Technology Journal, A Data Mining Approach, Identify Key Factors for Systematic Reuse, Software reuse, Reuse factors, Data mining, Feature selection, Reuse prediction.