Software errors and complexity: an empirical investigation0
Communications of the ACM
Identifying Error-Prone Software An Empirical Study
IEEE Transactions on Software Engineering
Analyzing Error-Prone System Structure
IEEE Transactions on Software Engineering
A Critique of Software Defect Prediction Models
IEEE Transactions on Software Engineering
A case study of open source software development: the Apache server
Proceedings of the 22nd international conference on Software engineering
The Confounding Effect of Class Size on the Validity of Object-Oriented Metrics
IEEE Transactions on Software Engineering
Software Metrics: A Rigorous and Practical Approach
Software Metrics: A Rigorous and Practical Approach
Elements of Software Science (Operating and programming systems series)
Elements of Software Science (Operating and programming systems series)
The Optimal Class Size for Object-Oriented Software
IEEE Transactions on Software Engineering
Two case studies of open source software development: Apache and Mozilla
ACM Transactions on Software Engineering and Methodology (TOSEM)
The Cathedral and the Bazaar
Empirical Software Engineering
Replicated Case Studies for Investigating Quality Factorsin Object-Oriented Designs
Empirical Software Engineering
Reexamining the Fault Density-Component Size Connection
IEEE Software
Does OO Sync with How We Think?
IEEE Software
Quantitative Analysis of Faults and Failures in a Complex Software System
IEEE Transactions on Software Engineering
Assessing the applicability of fault-proneness models across object-oriented software projects
IEEE Transactions on Software Engineering
M.H. Halstead's Software Science - a critical examination
ICSE '82 Proceedings of the 6th international conference on Software engineering
Some Misconceptions About Lines of Code
METRICS '97 Proceedings of the 4th International Symposium on Software Metrics
Replicated studies: building a body of knowledge about software reading techniques
Lecture notes on empirical software engineering
Quality assurance under the open source development model
Journal of Systems and Software
The Roi From Software Quality
Predicting the Location and Number of Faults in Large Software Systems
IEEE Transactions on Software Engineering
Regression Modeling Strategies
Regression Modeling Strategies
Modeling the Effect of Size on Defect Proneness for Open-Source Software
ICSE COMPANION '07 Companion to the proceedings of the 29th International Conference on Software Engineering
A Replicated Quantitative Analysis of Fault Distributions in Complex Software Systems
IEEE Transactions on Software Engineering
Quantitative Estimates of Debugging Requirements
IEEE Transactions on Software Engineering
Number of Faults per Line of Code
IEEE Transactions on Software Engineering
Theory of relative defect proneness
Empirical Software Engineering
A Replicated Survey of IT Software Project Failures
IEEE Software
An Investigation into the Functional Form of the Size-Defect Relationship for Software Modules
IEEE Transactions on Software Engineering
IEEE Transactions on Neural Networks
An in-depth study of the potentially confounding effect of class size in fault prediction
ACM Transactions on Software Engineering and Methodology (TOSEM)
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Recent studies on open-source software (OSS) products report that smaller modules are proportionally more defect prone compared to larger ones. This phenomenon, referred to as the Theory of Relative Defect Proneness (RDP), challenges the traditional QA approaches that give a higher priority to larger modules, and it attracts growing interest from closed-source software (CSS) practitioners. In this paper, we report the findings of a study where we tested the theory of RDP using ten CSS products. The results clearly confirm the theory of RDP. We also demonstrate the useful practical implications of this theory in terms of defect-detection effectiveness. Therefore, this study does not only make research contributions by rigorously testing a scientific theory for a different category of software products, but also provides useful insights and evidence to practitioners for revising their existing QA practices.