Derivation and validation of software metrics
Derivation and validation of software metrics
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
The Confounding Effect of Class Size on the Validity of Object-Oriented Metrics
IEEE Transactions on Software Engineering
Journal of Systems and Software
The Effects of Fault Counting Methods on Fault Model Quality
COMPSAC '04 Proceedings of the 28th Annual International Computer Software and Applications Conference - Volume 01
Identifying and characterizing change-prone classes in two large-scale open-source products
Journal of Systems and Software
Towards a generic model for software quality prediction
Proceedings of the 6th international workshop on Software quality
Comparing negative binomial and recursive partitioning models for fault prediction
Proceedings of the 4th international workshop on Predictor models in software engineering
Information Sciences: an International Journal
Review: A systematic review of software fault prediction studies
Expert Systems with Applications: An International Journal
Data mining source code for locating software bugs: A case study in telecommunication industry
Expert Systems with Applications: An International Journal
Fair and balanced?: bias in bug-fix datasets
Proceedings of the the 7th joint meeting of the European software engineering conference and the ACM SIGSOFT symposium on The foundations of software engineering
Evaluating the Impact of UML Modeling on Software Quality: An Industrial Case Study
MODELS '09 Proceedings of the 12th International Conference on Model Driven Engineering Languages and Systems
Comparing the effectiveness of several modeling methods for fault prediction
Empirical Software Engineering
Review: Software fault prediction: A literature review and current trends
Expert Systems with Applications: An International Journal
Software fault prediction with object-oriented metrics based artificial immune recognition system
PROFES'07 Proceedings of the 8th international conference on Product-Focused Software Process Improvement
Software defect prediction using Bayesian networks
Empirical Software Engineering
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We used several machine learning algorithms to predict the defective modules in five NASA products, namely, CM1, JM1, KC1, KC2, and PC1. A set of static measures were employed as predictor variables. While doing so, we observed that a large portion of the modules were small, as measured by lines of code (LOC). When we experimented on the data subsets created by partitioning according to module size, we obtained higher prediction performance for the subsets that include larger modules. We also performed defect prediction using class-level data for KC1 rather than the method-level data. In this case, the use of class-level data resulted in improved prediction performance compared to using method-level data. These findings suggest that quality assurance activities can be guided even better if defect prediction is performed by using data that belong to larger modules.