Software reliability: measurement, prediction, application
Software reliability: measurement, prediction, application
C4.5: programs for machine learning
C4.5: programs for machine learning
Testing: principles and practice
ACM Computing Surveys (CSUR)
A Critique of Software Defect Prediction Models
IEEE Transactions on Software Engineering
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Data mining: concepts and techniques
Data mining: concepts and techniques
Machine Learning
Software Cost Estimation with Cocomo II with Cdrom
Software Cost Estimation with Cocomo II with Cdrom
Rapid Testing
An Empirical Method for Selecting Software Reliability Growth Models
Empirical Software Engineering
Quantitative Analysis of Development Defects to Guide Testing: A Case Study
Software Quality Control
Quantitative Analysis of Faults and Failures in a Complex Software System
IEEE Transactions on Software Engineering
ECML '95 Proceedings of the 8th European Conference on Machine Learning
Modelling the Fault Correction Process
ISSRE '01 Proceedings of the 12th International Symposium on Software Reliability Engineering
Software Endgames: Eliminating Defects, Controlling Change, And The Countdown To On-time Delivery
Software Endgames: Eliminating Defects, Controlling Change, And The Countdown To On-time Delivery
Using AUC and Accuracy in Evaluating Learning Algorithms
IEEE Transactions on Knowledge and Data Engineering
Empirical Assessment of Machine Learning based Software Defect Prediction Techniques
WORDS '05 Proceedings of the 10th IEEE International Workshop on Object-Oriented Real-Time Dependable Systems
Queuing Models for Field Defect Resolution Process
ISSRE '06 Proceedings of the 17th International Symposium on Software Reliability Engineering
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Neural Networks: A Comprehensive Foundation (3rd Edition)
Neural Networks: A Comprehensive Foundation (3rd Edition)
IEEE Transactions on Software Engineering
A multiplicative model of software defect repair times
Empirical Software Engineering
A systematic literature review of software quality cost research
Journal of Systems and Software
Is lines of code a good measure of effort in effort-aware models?
Information and Software Technology
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The ability to predict the time required to repair software defects is important for both software quality management and maintenance. Estimated repair times can be used to improve the reliability and time-to-market of software under development. This paper presents an empirical approach to predicting defect repair times by constructing models that use well-established machine learning algorithms and defect data from past software defect reports. We describe, as a case study, the analysis of defect reports collected during the development of a large medical software system. Our predictive models give accuracies as high as 93.44%, despite the limitations of the available data. We present the proposed methodology along with detailed experimental results, which include comparisons with other analytical modeling approaches.