Software Defect Association Mining and Defect Correction Effort Prediction

Much current software defect prediction work concentrates on the number of defects remaining in software system. In this paper, we present association rule mining based methods to predict defect associations and defect-correction effort. This is to help developers detect software defects and assist project managers in allocating testing resources more effectively. We applied the proposed methods to the SEL defect data consisting of more than 200 projects over more than 15 years. The results show that for the defect association prediction, the accuracy is very high and the false negative rate is very low. Likewise for the defect-correction effort prediction, the accuracy for both defect isolation effort prediction and defect correction effort prediction are also high. We compared the defect-correction effort prediction method with other types of methods: PART, C4.5, and Na¨ıve Bayes and show that accuracy has been improved by at least 23%. We also evaluated the impact of support and confidence levels on prediction accuracy, false negative rate, false positive rate, and the number of rules. We found that higher support and confidence levels may not result in higher prediction accuracy, and a sufficient number of rules is a precondition for high prediction accuracy

Proceedings of the Twenty-Third Annual Software Engineering Workshop

The Twenty-third Annual Software Engineering Workshop (SEW) provided 20 presentations designed to further the goals of the Software Engineering Laboratory (SEL) of the NASA-GSFC. The presentations were selected on their creativity. The sessions which were held on 2-3 of December 1998, centered on the SEL, Experimentation, Inspections, Fault Prediction, Verification and Validation, and Embedded Systems and Safety-Critical Systems

A Review of Software Inspections

For two decades, software inspections have proven effective for detecting defects in software. We have reviewed the different ways software inspections are done, created a taxonomy of inspection methods, and examined claims about the cost-effectiveness of different methods. We detect a disturbing pattern in the evaluation of inspection methods. Although there is universal agreement on the effectiveness of software inspection, their economics are uncertain. Our examination of several empirical studies leads us to conclude that the benefits of inspections are often overstated and the costs (especially for large software developments) are understated. Furthermore, some of the most influential studies establishing these costs and benefits are 20 years old now, which leads us to question their relevance to today's software development processes. Extensive work is needed to determine exactly how, why, and when software inspections work, and whether some defect detection techniques might be more cost-effective than others. In this article we ask some questions about measuring effectiveness of software inspections and determining how much they really cost when their effect on the rest of the development process is considered. Finding answers to these questions will enable us to improve the efficiency of software development. (Also cross-referenced as UMIACS-TR-95-104