Instance-Based Learning Algorithms
Machine Learning
Machine Learning
Handbook of software reliability engineering
Handbook of software reliability engineering
A Critique of Software Defect Prediction Models
IEEE Transactions on Software Engineering
A Comprehensive Evaluation of Capture-Recapture Models for Estimating Software Defect Content
IEEE Transactions on Software Engineering
Software Verification and Validation for Practitioners and Managers, Second Edition
Software Verification and Validation for Practitioners and Managers, Second Edition
On Building Prediction Systems for Software Engineers
Empirical Software Engineering
The particle swarm optimization algorithm: convergence analysis and parameter selection
Information Processing Letters
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
An Investigation of Analysis Techniques for Software Datasets
METRICS '99 Proceedings of the 6th International Symposium on Software Metrics
Artificial Immune Recognition System (AIRS): An Immune-Inspired Supervised Learning Algorithm
Genetic Programming and Evolvable Machines
An Empirical Study of Software Reuse vs. Defect-Density and Stability
Proceedings of the 26th International Conference on Software Engineering
Static analysis tools as early indicators of pre-release defect density
Proceedings of the 27th international conference on Software 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
Empirical Validation of Object-Oriented Metrics on Open Source Software for Fault Prediction
IEEE Transactions on Software Engineering
What Do We Know about Defect Detection Methods?
IEEE Software
Rotation Forest: A New Classifier Ensemble Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
A literature survey of the quality economics of defect-detection techniques
Proceedings of the 2006 ACM/IEEE international symposium on Empirical software engineering
Data Mining
Journal of Systems and Software
The Current State and Future of Search Based Software Engineering
FOSE '07 2007 Future of Software Engineering
Predicting weekly defect inflow in large software projects based on project planning and test status
Information and Software Technology
The influence of organizational structure on software quality: an empirical case study
Proceedings of the 30th international conference on Software engineering
Quality-Evaluation Models and Measurements
IEEE Software
IEEE Transactions on Software Engineering
A Comparative Evaluation of Using Genetic Programming for Predicting Fault Count Data
ICSEA '08 Proceedings of the 2008 The Third International Conference on Software Engineering Advances
Review: A systematic review of software fault prediction studies
Expert Systems with Applications: An International Journal
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Journal of Systems and Software
PSO based neural network for time series forecasting
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Comparing the effectiveness of several modeling methods for fault prediction
Empirical Software Engineering
A Field Guide to Genetic Programming
A Field Guide to Genetic Programming
The relationship between search based software engineering and predictive modeling
Proceedings of the 6th International Conference on Predictive Models in Software Engineering
Unsupervised learning for expert-based software quality estimation
HASE'04 Proceedings of the Eighth IEEE international conference on High assurance systems engineering
Search-based Prediction of Fault-slip-through in Large Software Projects
SSBSE '10 Proceedings of the 2nd International Symposium on Search Based Software Engineering
Evolutionary Optimization of Software Quality Modeling with Multiple Repositories
IEEE Transactions on Software Engineering
Using Faults-Slip-Through Metric as a Predictor of Fault-Proneness
APSEC '10 Proceedings of the 2010 Asia Pacific Software Engineering Conference
Basics of Software Engineering Experimentation
Basics of Software Engineering Experimentation
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A large percentage of the cost of rework can be avoided by finding more faults earlier in a software test process. Therefore, determination of which software test phases to focus improvement work on has considerable industrial interest. We evaluate a number of prediction techniques for predicting the number of faults slipping through to unit, function, integration, and system test phases of a large industrial project. The objective is to quantify improvement potential in different test phases by striving toward finding the faults in the right phase. The results show that a range of techniques are found to be useful in predicting the number of faults slipping through to the four test phases; however, the group of search-based techniques (genetic programming, gene expression programming, artificial immune recognition system, and particle swarm optimization---based artificial neural network) consistently give better predictions, having a representation at all of the test phases. Human predictions are consistently better at two of the four test phases. We conclude that the human predictions regarding the number of faults slipping through to various test phases can be well supported by the use of search-based techniques. A combination of human and an automated search mechanism (such as any of the search-based techniques) has the potential to provide improved prediction results.