In this repo:
The Qualitas Corpus is a curated collection of software systems intended to be used for empirical studies of code artefacts.
JaConTeBe: A Benchmark Suite of Real-World Java Concurrency Bugs (T).
Do Automatically Generated Unit Tests Find Real Faults? An Empirical Study of Effectiveness and Challenges
Combining Deep Learning with Information Retrieval to Localize Buggy Files for Bug Reports
Combining Deep Learning with Information Retrieval to Localize Buggy Files for Bug Reports
Combining Deep Learning with Information Retrieval to Localize Buggy Files for Bug Reports
Combining Deep Learning with Information Retrieval to Localize Buggy Files for Bug Reports
Combining Deep Learning with Information Retrieval to Localize Buggy Files for Bug Reports
Combining Deep Learning with Information Retrieval to Localize Buggy Files for Bug Reports
Enhancing candidate link generation for requirements tracing: The cluster hypothesis revisited
When and why your code starts to smell bad
The Eclipse and Mozilla defect tracking dataset: A genuine dataset for mining bug information
Dealing with Noise in Defect Prediction
Defect Prediction between Software Versions with Active Learning and Dimensionality Reduction
Software process evaluation A machine learning approach
Towards Identifying Software Project Clusters with Regard to Defect Prediction
Towards Identifying Software Project Clusters with Regard to Defect Prediction
Towards Identifying Software Project Clusters with Regard to Defect Prediction
Towards Identifying Software Project Clusters with Regard to Defect Prediction
Towards Identifying Software Project Clusters with Regard to Defect Prediction
Towards Identifying Software Project Clusters with Regard to Defect Prediction
Towards Identifying Software Project Clusters with Regard to Defect Prediction
Towards Identifying Software Project Clusters with Regard to Defect Prediction
Towards Identifying Software Project Clusters with Regard to Defect Prediction
Towards Identifying Software Project Clusters with Regard to Defect Prediction
Towards Identifying Software Project Clusters with Regard to Defect Prediction
Towards Identifying Software Project Clusters with Regard to Defect Prediction
Towards Identifying Software Project Clusters with Regard to Defect Prediction
Towards identifying software project clusters with regard to defect prediction
Towards Identifying Software Project Clusters with Regard to Defect Prediction
Towards Identifying Software Project Clusters with Regard to Defect Prediction
Towards Identifying Software Project Clusters with Regard to Defect Prediction
Towards Identifying Software Project Clusters with Regard to Defect Prediction
Towards identifying software project clusters with regard to defect prediction
Towards Identifying Software Project Clusters with Regard to Defect Prediction
Towards Identifying Software Project Clusters with Regard to Defect Prediction
Towards Identifying Software Project Clusters with Regard to Defect Prediction
Towards Identifying Software Project Clusters with Regard to Defect Prediction
Towards Identifying Software Project Clusters with Regard to Defect Prediction
Towards Identifying Software Project Clusters with Regard to Defect Prediction
Towards Identifying Software Project Clusters with Regard to Defect Prediction
Towards Identifying Software Project Clusters with Regard to Defect Prediction
Towards Identifying Software Project Clusters with Regard to Defect Prediction
Towards Identifying Software Project Clusters with Regard to Defect Prediction
camel OO defect data
berek OO defect data
Towards Identifying Software Project Clusters with Regard to Defect Prediction
Towards Identifying Software Project Clusters with Regard to Defect Prediction
Notes on ar1, ar3, ar4, ar5, ar6
Notes on mw1
pc5 defect data
pc4 defect data
pc3 defect data
pc2 defect data
pc1 defect data
mc2 data
mc1 data
kc3 defect data
kc2 defect data
real-time predictive ground system
data collection and processing