Analysis of Faults in an N-Version Software Experiment
IEEE Transactions on Software Engineering
Regression modelling of software quality: empirical investigation
Journal of Electronic Materials
Formal methods: state of the art and future directions
ACM Computing Surveys (CSUR) - Special ACM 50th-anniversary issue: strategic directions in computing research
A Critique of Software Defect Prediction Models
IEEE Transactions on Software Engineering
Proceedings of the Conference on The Future of Software Engineering
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Machine Learning
Introduction to Bayesian Networks
Introduction to Bayesian Networks
Data Mining Techniques: For Marketing, Sales, and Customer Support
Data Mining Techniques: For Marketing, Sales, and Customer Support
Empirical Software Engineering
A Comparative Study of Ordering and Classification of Fault-ProneSoftware Modules
Empirical Software Engineering
Seven More Myths of Formal Methods
IEEE Software
Quantitative Analysis of Faults and Failures in a Complex Software System
IEEE Transactions on Software Engineering
Change Impact Identification in Object Oriented Software Maintenance
ICSM '94 Proceedings of the International Conference on Software Maintenance
Requirement-Based Automated Black-Box Test Generation
COMPSAC '01 Proceedings of the 25th International Computer Software and Applications Conference on Invigorating Software Development
Automated support for classifying software failure reports
Proceedings of the 25th International Conference on Software Engineering
SEW '02 Proceedings of the 27th Annual NASA Goddard Software Engineering Workshop (SEW-27'02)
Learning Early Lifecycle IV&V Quality Indicators
METRICS '03 Proceedings of the 9th International Symposium on Software Metrics
Using Machine Learning for Estimating the Defect Content After an Inspection
IEEE Transactions on Software Engineering
Finding Latent Code Errors via Machine Learning over Program Executions
Proceedings of the 26th International Conference on Software Engineering
Introduction to Machine Learning (Adaptive Computation and Machine Learning)
Introduction to Machine Learning (Adaptive Computation and Machine Learning)
Use of relative code churn measures to predict system defect density
Proceedings of the 27th 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
Software Assurance by Bounded Exhaustive Testing
IEEE Transactions on Software Engineering
Predicting the Location and Number of Faults in Large Software Systems
IEEE Transactions on Software Engineering
Scenario-Based Assessment of Nonfunctional Requirements
IEEE Transactions on Software Engineering
A Comprehensive Model for Software Rejuvenation
IEEE Transactions on Dependable and Secure Computing
Ranking Significance of Software Components Based on Use Relations
IEEE Transactions on Software Engineering
A Probabilistic Model for Predicting Software Development Effort
IEEE Transactions on Software Engineering
Building Defect Prediction Models in Practice
IEEE Software
Providing Test Quality Feedback Using Static Source Code and Automatic Test Suite Metrics
ISSRE '05 Proceedings of the 16th IEEE International Symposium on Software Reliability Engineering
Software Defect Association Mining and Defect Correction Effort Prediction
IEEE Transactions on Software Engineering
Software Defect Identification Using Machine Learning Techniques
EUROMICRO '06 Proceedings of the 32nd EUROMICRO Conference on Software Engineering and Advanced Applications
Data Mining Static Code Attributes to Learn Defect Predictors
IEEE Transactions on Software Engineering
What went wrong: explaining counterexamples
SPIN'03 Proceedings of the 10th international conference on Model checking software
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New methodologies and tools have gradually made the life cycle for software development more human-independent. Much of the research in this field focuses on defect reduction, defect identification and defect prediction. Defect prediction is a relatively new research area that involves using various methods from artificial intelligence to data mining. Identifying and locating defects in software projects is a difficult task. Measuring software in a continuous and disciplined manner provides many advantages such as the accurate estimation of project costs and schedules as well as improving product and process qualities. This study aims to propose a model to predict the number of defects in the new version of a software product with respect to the previous stable version. The new version may contain changes related to a new feature or a modification in the algorithm or bug fixes. Our proposed model aims to predict the new defects introduced into the new version by analyzing the types of changes in an objective and formal manner as well as considering the lines of code (LOC) change. Defect predictors are helpful tools for both project managers and developers. Accurate predictors may help reducing test times and guide developers towards implementing higher quality codes. Our proposed model can aid software engineers in determining the stability of software before it goes on production. Furthermore, such a model may provide useful insight for understanding the effects of a feature, bug fix or change in the process of defect detection.