Program evolution: processes of software change
Program evolution: processes of software change
A Pattern Recognition Approach for Software Engineering Data Analysis
IEEE Transactions on Software Engineering - Special issue on software measurement principles, techniques, and environments
A Critique of Software Defect Prediction Models
IEEE Transactions on Software Engineering
The distribution of faults in a large industrial software system
ISSTA '02 Proceedings of the 2002 ACM SIGSOFT international symposium on Software testing and analysis
Uncertain Classification of Fault-Prone Software Modules
Empirical Software Engineering
Reexamining the Fault Density-Component Size Connection
IEEE Software
Mining the Maintenance History of a Legacy Software System
ICSM '03 Proceedings of the International Conference on Software Maintenance
Populating a Release History Database from Version Control and Bug Tracking Systems
ICSM '03 Proceedings of the International Conference on Software Maintenance
CVS Release History Data for Detecting Logical Couplings
IWPSE '03 Proceedings of the 6th International Workshop on Principles of Software Evolution
Developing Fault Predictors for Evolving Software Systems
METRICS '03 Proceedings of the 9th International Symposium on Software Metrics
Use of relative code churn measures to predict system defect density
Proceedings of the 27th international conference on Software engineering
EvoLens: Lens-View Visualizations of Evolution Data
IWPSE '05 Proceedings of the Eighth International Workshop on Principles of Software Evolution
Predicting defect densities in source code files with decision tree learners
Proceedings of the 2006 international workshop on Mining software repositories
Predicting component failures at design time
Proceedings of the 2006 ACM/IEEE international symposium on Empirical software engineering
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Comparing bug finding tools with reviews and tests
TestCom'05 Proceedings of the 17th IFIP TC6/WG 6.1 international conference on Testing of Communicating Systems
Proceedings of the 30th international conference on Software engineering
Analysis of the reliability of a subset of change metrics for defect prediction
Proceedings of the Second ACM-IEEE international symposium on Empirical software engineering and measurement
Can developer-module networks predict failures?
Proceedings of the 16th ACM SIGSOFT International Symposium on Foundations of software engineering
Empirical Evaluation of Hunk Metrics as Bug Predictors
IWSM '09 /Mensura '09 Proceedings of the International Conferences on Software Process and Product Measurement
Usage of multiple prediction models based on defect categories
Proceedings of the 6th International Conference on Predictive Models in Software Engineering
Ownership, experience and defects: a fine-grained study of authorship
Proceedings of the 33rd International Conference on Software Engineering
Topic-based defect prediction (NIER track)
Proceedings of the 33rd International Conference on Software Engineering
A topic-based approach for narrowing the search space of buggy files from a bug report
ASE '11 Proceedings of the 2011 26th IEEE/ACM International Conference on Automated Software Engineering
Faster issue resolution with higher technical quality of software
Software Quality Control
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We use 63 features extracted from sources such as versioning and issue tracking systems to predict defects in short time frames of two months. Our multivariate approach covers aspects of software projects such as size, team structure, process orientation, complexity of existing solution, difficulty of problem, coupling aspects, time constrains, and testing data. We investigate the predictability of several severities of defects in software projects. Are defects with high severity difficult to predict? Are prediction models for defects that are discovered by internal staff similar to models for defects reported from the field? We present both an exact numerical prediction of future defect numbers based on regression models as well as a classification of software components as defect-prone based on the C4.5 decision tree. We create models to accurately predict short-term defects in a study of 5 applications composed of more than 8.000 classes and 700.000 lines of code. The model quality is assessed based on 10-fold cross validation.