Feature-oriented defect prediction

Author:

Strüder Stefan1,Mukelabai Mukelabai2,Strüber Daniel3,Berger Thorsten2

Affiliation:

1. University of Koblenz-Landau, Germany

2. University of Gothenburg, Sweden

3. Radboud University Nijmegen, Netherlands

Funder

Vetenskapsrådet

Publisher

ACM

Reference79 articles.

1. 2017. Encyclopedia of Machine Learning and Data Mining. Springer US. 2017. Encyclopedia of Machine Learning and Data Mining. Springer US.

2. Iago Abal Claus Brabrand and Andrzej Wasowski. 2014. 42 variability bugs in the linux kernel: a qualitative analysis. In ASE. 421--432. Iago Abal Claus Brabrand and Andrzej Wasowski. 2014. 42 variability bugs in the linux kernel: a qualitative analysis. In ASE. 421--432.

3. Ethem Alpaydin. 2010. Introduction to Machine Learning (second edition ed.). The MIT Press Cambridge Massachusetts. Ethem Alpaydin. 2010. Introduction to Machine Learning (second edition ed.). The MIT Press Cambridge Massachusetts.

4. Software Defect Prediction Using Supervised Machine Learning and Ensemble Techniques: A Comparative Study

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1. FeatRacer: Locating Features Through Assisted Traceability;IEEE Transactions on Software Engineering;2023-12

2. Determining the Most Significant Metadata Features to Indicate Defective Software Commits;2023 IEEE/ACIS 21st International Conference on Software Engineering Research, Management and Applications (SERA);2023-05-23

3. Detecting false-passing products and mitigating their impact on variability fault localization in software product lines;Information and Software Technology;2023-01

4. Asset Management in Machine Learning: State-of-research and State-of-practice;ACM Computing Surveys;2022-12-15

5. Why Don’t XAI Techniques Agree? Characterizing the Disagreements Between Post-hoc Explanations of Defect Predictions;2022 IEEE International Conference on Software Maintenance and Evolution (ICSME);2022-10

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