Component reuse is widely considered vital for obtaining significant improvement in development productivity. However, as an organization adopts a reuse-oriented development process, the nature of the problems in development is likely to change. In this article, we use a measurement-based approach to better understand and evaluate an evolving reuse process. More specifically, we study the effects of reuse across seven projects in narrow domain from a single development organization. An analysis of the errors that occur in new and reused components across all phases of system development provides insight into the factors influencing the reuse process. We found significant differences between errors associated with new and various types of reused components in terms of the types of errors committed. In addition, we identified differences when errors are introduced and the effect that the errors have on the development process.

The software industry has made extensive use of commercial software tools such as compilers and editors in development environments of computer-based systems for several decades. However, in recent years an emerging trend is the extensive use of commercial off-the-shelf (COTS) software products as a major part of delivered software systems. It is generally recognized that this trend introduces a significant change to the development of computer-based systems. The thrust of this paper is that this trend also introduces a significant change to the software maintenance process. The paper addresses some of the issues involved in the maintenance of COTS-intensive software systems, and the reasons why the COTS factor constitutes a significant change from traditional software maintenance processes. Finally, some lessons learned are offered as suggestions for addressing the issues in the maintenance of COTS-intensive systems. © 1997 John Wiley & Sons, Ltd.

In order to plan, control, and evaluate the software development process, one needs to collect and analyze data in a meaningful way. Classical techniques for such analysis are not always well suited to software engineering data. A pattern recognition approach for analyzing software engineering data, called optimized set reduction (OSR), that addresses many of the problems associated with the usual approaches is described. Methods are discussed for using the technique for prediction, risk management, and quality evaluation. Experimental results are provided to demonstrate the effectiveness of the technique for the particular application of software cost estimation.>