C4.5: programs for machine learning
C4.5: programs for machine learning
Modeling the effort to correct faults
Selected papers of the sixth annual Oregon workshop on Software metrics
Estimating Software Project Effort Using Analogies
IEEE Transactions on Software Engineering
Technical Note: Naive Bayes for Regression
Machine Learning
Generating Accurate Rule Sets Without Global Optimization
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Basic Block Distribution Analysis to Find Periodic Behavior and Simulation Points in Applications
Proceedings of the 2001 International Conference on Parallel Architectures and Compilation Techniques
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Prediction Models for Software Fault Correction Effort
CSMR '01 Proceedings of the Fifth European Conference on Software Maintenance and Reengineering
A Simulation Study of the Model Evaluation Criterion MMRE
IEEE Transactions on Software Engineering
Software effort estimation by analogy and "regression toward the mean"
Journal of Systems and Software - Special issue: Best papers on Software Engineering from the SEKE'01 Conference
Estimation of Software Defects Fix Effort Using Neural Networks
COMPSAC '04 Proceedings of the 28th Annual International Computer Software and Applications Conference - Workshops and Fast Abstracts - Volume 02
Lucene in Action (In Action series)
Lucene in Action (In Action series)
Software Defect Association Mining and Defect Correction Effort Prediction
IEEE Transactions on Software Engineering
How Long Will It Take to Fix This Bug?
MSR '07 Proceedings of the Fourth International Workshop on Mining Software Repositories
Effort prediction model using similarity for embedded software development
ICCSA'06 Proceedings of the 2006 international conference on Computational Science and Its Applications - Volume Part II
Financial distress prediction based on similarity weighted voting CBR
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
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This article tackles the problem of predicting effort (in person-hours) required to fix a software defect posted on an Issue Tracking System. The proposed method is inspired by the Nearest Neighbour Approach presented by the pioneering work of Weiss et al. (2007) [1]. We propose four enhancements to Weiss et al. (2007) [1]: Data Enrichment, Majority Voting, Adaptive Threshold and Binary Clustering. Data Enrichment infuses additional issue information into the similarity-scoring procedure, aiming to increase the accuracy of similarity scores. Majority Voting exploits the fact that many of the similar historical issues have repeating effort values, which are close to the actual. Adaptive Threshold automatically adjusts the similarity threshold to ensure that we obtain only the most similar matches. We use Binary Clustering if the similarity scores are very low, which might result in misleading predictions. This uses common properties of issues to form clusters (independent of the similarity scores) which are then used to produce the predictions. Numerical results are presented showing a noticeable improvement over the method proposed in Weiss et al. (2007) [1].