Data Mining Static Code Attributes to Learn Defect Predictors
IEEE Transactions on Software Engineering
Implications of ceiling effects in defect predictors
Proceedings of the 4th international workshop on Predictor models in software engineering
A defect prediction method for software versioning
Software Quality Control
An FIS for early detection of defect prone modules
ICIC'09 Proceedings of the Intelligent computing 5th international conference on Emerging intelligent computing technology and applications
Urban point-of-interest recommendation by mining user check-in behaviors
Proceedings of the ACM SIGKDD International Workshop on Urban Computing
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Traditional methods of generating quality code indicators (e.g. linear regression, decision tree induction) can be demonstrated to be inappropriate for IV&V purposes. IV&V is a unique aspect of the software lifecycle, and different methods are necessary to produce quick and accurate results. If quality code indicators could be produced on a per-project basis, then IV&V could proceed in a more straight-forward fashion, saving time and money. This article presents one case study on just such a project, showing that by using the proper metrics and machine learning algorithms, quality indicators can be found as early as 3 monthsinto the IV&V process.