Software reliability: measurement, prediction, application
Software reliability: measurement, prediction, application
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
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
Data mining: concepts and techniques
Data mining: concepts and techniques
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Art of Software Testing
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
Deriving a Fault Architecture to Guide Testing
Software Quality Control
Gauging Software Readiness with Defect Tracking
IEEE Software
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
Exploring Defect Data from Development and Customer Usage on Software Modules over Multiple Releases
ISSRE '98 Proceedings of the The Ninth International Symposium on Software Reliability Engineering
Modelling the Fault Correction Process
ISSRE '01 Proceedings of the 12th International Symposium on Software Reliability Engineering
Software Engineering: A Practitioner's Approach (McGraw-Hill Series in Computer Science)
Software Engineering: A Practitioner's Approach (McGraw-Hill Series in Computer Science)
In-process metrics for software testing
IBM Systems Journal
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
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
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
An analysis of Bayesian classifiers
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
Test strategies in distributed software development environments
Computers in Industry
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Achieving high quality software would be easier if effective software development practices were known and deployed in appropriate contexts. Because our theoretical knowledge of the underlying principles of software development is far from complete, empirical analysis of past experience in software projects is essential for acquiring useful software practices. As advances in software technology continue to facilitate automated tracking and data collection, more software data become available. Our research aims to develop methods to exploit such data for improving software development practices.This paper proposes an empirical approach, based on the analysis of defect data, that provides support for software testing management in two ways: (1) construction of a predictive model for defect repair times, and (2) a method for assessing testing quality across multiple releases. The approach employs data mining techniques including statistical methods and machine learning. To illustrate the proposed approach, we present a case study using the defect reports created during the development of three releases of a large medical software system, produced by a large well-established software company. We validate our proposed testing quality assessment using a statistical test at a significance level of 0.1. Despite the limitations of the available data, our predictive models give accuracies as high as 93%.