Elements of information theory
Elements of information theory
Estimating software fault content before coding
ICSE '92 Proceedings of the 14th international conference on Software engineering
Prediction of Software Reliability Using Connectionist Models
IEEE Transactions on Software Engineering
A practical Bayesian framework for backpropagation networks
Neural Computation
Assessing Software Designs Using Capture-Recapture Methods
IEEE Transactions on Software Engineering - Special issue on software reliability
Evaluating predictive quality models derived from software measures: lessons learned
Journal of Systems and Software
IEEE Transactions on Software Engineering
Defect content estimations from review data
Proceedings of the 20th international conference on Software 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
ICSE '01 Proceedings of the 23rd International Conference on Software Engineering
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Software Inspection
Empirical interval estimates for the defect content after an inspection
Proceedings of the 24th International Conference on Software Engineering
Empirical Software Engineering
Applying Machine Learning to Solve an Estimation Problem in Software Inspections
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Bayesian Learning and Evolutionary Parameter Optimization
KI '01 Proceedings of the Joint German/Austrian Conference on AI: Advances in Artificial Intelligence
A Comparison and Integration of Capture-Recapture Models and the Detection Profile Method
ISSRE '98 Proceedings of the The Ninth International Symposium on Software Reliability Engineering
QSIC '04 Proceedings of the Quality Software, Fourth International Conference
A New Challenge for Applying Time Series Metrics Data to Software Quality Estimation
Software Quality Control
Software Defect Association Mining and Defect Correction Effort Prediction
IEEE Transactions on Software Engineering
Analysis of Naive Bayes' assumptions on software fault data: An empirical study
Data & Knowledge Engineering
A defect prediction method for software versioning
Software Quality Control
An industrial case study of classifier ensembles for locating software defects
Software Quality Control
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Abstract--We view the problem of estimating the defect content of a document after an inspection as a machine learning problem: The goal is to learn from empirical data the relationship between certain observable features of an inspection (such as the total number of different defects detected) and the number of defects actually contained in the document. We show that some features can carry significant nonlinear information about the defect content. Therefore, we use a nonlinear regression technique, neural networks, to solve the learning problem. To select the best among all neural networks trained on a given data set, one usually reserves part of the data set for later cross-validation; in contrast, we use a technique which leaves the full data set for training. This is an advantage when the data set is small. We validate our approach on a known empirical inspection data set. For that benchmark, our novel approach clearly outperforms both linear regression and the current standard methods in software engineering for estimating the defect content, such as capture-recapture. The validation also shows that our machine learning approach can be successful even when the empirical inspection data set is small.