Theory of relative defect proneness

In this study, we investigated the functional form of the size-defect relationship for software modules through replicated studies conducted on ten open-source products. We consistently observed a power-law relationship where defect proneness increases at a slower rate compared to size. Therefore, smaller modules are proportionally more defect prone. We externally validated the application of our results for two commercial systems. Given limited and fixed resources for code inspections, there would be an impressive improvement in the cost-effectiveness, as much as 341% in one of the systems, if a smallest-first strategy were preferred over a largest-first one. The consistent results obtained in this study led us to state a theory of relative defect proneness (RDP): In large-scale software systems, smaller modules will be proportionally more defect-prone compared to larger ones. We suggest that practitioners consider our results and give higher priority to smaller modules in their focused quality assurance efforts.

Divya MathewEmail:

---

A. Güneş Koru   received a B.S. degree in Computer Engineering from Ege University, İzmir, Turkey in 1996, an M.S. degree in Computer Engineering from Dokuz Eylül University, İzmir, Turkey in 1998, an M.S. degree in Software Engineering from Southern Methodist University (SMU), Dallas, TX in 2002, and a Ph.D. degree in Computer Science from SMU in 2004. He is an assistant professor in the Department of Information Systems at University of Maryland, Baltimore County (UMBC). His research interests include software quality, measurement, maintenance, and evolution, open source software, bioinformatics, and healthcare informatics. Khaled El Emam   is an Associate Professor at the University of Ottawa, Faculty of Medicine and the School of Information Technology and Engineering. He is a Canada Research Chair in Electronic Health Information at the University of Ottawa. Previously Khaled was a Senior Research Officer at the National Research Council of Canada, and prior to that he was head of the Quantitative Methods Group at the Fraunhofer Institute in Kaiserslautern, Germany. In 2003 and 2004, he was ranked as the top systems and software engineering scholar worldwide by the Journal of Systems and Software based on his research on measurement and quality evaluation and improvement, and ranked second in 2002 and 2005. He holds a Ph.D. from the Department of Electrical and Electronics, King’s College, at the University of London (UK). His labs web site is: . Dongsong Zhang   is an Associate Professor in the Department of Information Systems at University of Maryland, Baltimore County. He received his Ph.D. in Management Information Systems from the University of Arizona. His current research interests include context-aware mobile computing, computer-mediated collaboration and communication, knowledge management, and open source software. Dr. Zhang’s work has been published or will appear in journals such as Communications of the ACM (CACM), Journal of Management Information Systems (JMIS), IEEE Transactions on Knowledge and Data Engineering (TKDE), IEEE Transactions on Multimedia, IEEE Transactions on Systems, Man, and Cybernetics, IEEE Transactions on Professional Communication, among others. He has received research grants and awards from NIH, Google Inc., and Chinese Academy of Sciences. He also serves as senior editor or editorial board member of a number of journals. Hongfang Liu   is currently an Assistant Professor in Department of Biostatistics, Bioinformatics, and Biomathematics (DBBB) of Georgetown University. She has been working in the field of Biomedical Informatics for more than 10 years. Her expertise in clinical informatics includes clinical information system, controlled medical vocabulary, and medical language processing. Her expertise in bioinformatics includes microarray data analysis, biomedical entity nomenclature, molecular biology database curation, ontology, and biological text mining. She received a B.S. degree in Applied Mathematics and Statistics from University of Science and Technology of China in 1994, a M.S. degree in Computer Science from Fordham University in 1998, a PhD degree in computer science at the Graduate School of City University of New York in 2002. Divya Mathew   received the BTech degree in computer science and engineering from Cochin University of Science and Technology in 2005 and the MS degree in information systems from the University of Maryland, Baltimore County in 2008. Her research interests include software engineering and privacy preserving data mining techniques.