IEEE Transactions on Software Engineering (TSE)

International Journal Papers x 19

1. Do Experts Agree About Smelly Infrastructure?
   Authors - Sogol Masoumzadeh, Nuno Saavedra, Rungroj Maipradit, Lili Wei, João F. Ferreira, Dániel Varró, Shane McIntosh
   Venue - IEEE Transactions on Software Engineering, pp. To appear, 2025
   Preprint - PDF
   Related Tags - TSE 2025 anti-patterns
2. Mitigating the Uncertainty and Imprecision of Log-Based Code Coverage Without Requiring Additional Logging Statements
   Authors - Xiaoyan Xu, Filipe R. Cogo, Shane McIntosh
   Venue - IEEE Transactions on Software Engineering, pp. To appear, 2024
   Preprint - PDF
   Related Tags - TSE 2024 software logging
3. Characterizing the Prevalence, Distribution, and Duration of Stale Reviewer Recommendations
   Authors - Farshad Kazemi, Maxime Lamothe, Shane McIntosh
   Venue - IEEE Transactions on Software Engineering, pp. To appear, 2024
   Preprint - PDF
   Related Tags - TSE 2024 code review knowledge loss
4. Revisiting the Performance of Deep Learning-Based Vulnerability Detection on Realistic Datasets
   Authors - Partha Chakraborty, Krishna Kanth Arumugam, Mahmoud Alfadel, Meiyappan Nagappan, Shane McIntosh
   Venue - IEEE Transactions on Software Engineering, pp. To appear, 2024
   Preprint - PDF
   Related Tags - TSE 2024 software quality defect prediction
5. Characterizing Timeout Builds in Continuous Integration
   Authors - Nimmi Rashinika Weeraddana, Mahmoud Alfadel, Shane McIntosh
   Venue - IEEE Transactions on Software Engineering, Vol. 50, No. 6, pp. 1450–1463, 2024
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   Related Tags - TSE 2024 continuous integration resource waste
6. Code Cloning in Smart Contracts on the Ethereum Platform: An Extended Replication Study
   Authors - Faizan Khan, Istvan David, Daniel Varro, Shane McIntosh
   Venue - IEEE Transactions on Software Engineering, Vol. 49, No. 4, pp. 2006–2019, 2023
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   Related Tags - TSE 2023 software quality anti-patterns
7. Characterizing and Mitigating Self-Admitted Technical Debt in Build Systems
   Authors - Tao Xiao, Dong Wang, Shane McIntosh, Hideaki Hata, Raula Gaikovina Kula, Takashi Ishio, Kenichi Matsumoto
   Venue - IEEE Transactions on Software Engineering, Vol. 48, No. 10, pp. 4214–4228, 2022
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   Related Tags - TSE 2022 build systems software quality anti-patterns
8. The Ghost Commit Problem When Identifying Fix-Inducing Changes: An Empirical Study of Apache Projects
   Authors - Christophe Rezk, Yasutaka Kamei, Shane McIntosh
   Venue - IEEE Transactions on Software Engineering, Vol. 48, No. 9, pp. 3297–3309, 2022
   Preprint - PDF
   Related Tags - TSE 2022 software quality defect prediction
9. An Empirical Study of Type-Related Defects in Python Projects
   Authors - Faizan Khan, Boqi Chen, Daniel Varro, Shane McIntosh
   Venue - IEEE Transactions on Software Engineering, Vol. 48, No. 8, pp. 3145–3158, 2022
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   Related Tags - TSE 2022 software quality anti-patterns
10. Accelerating Continuous Integration by Caching Environments and Inferring Dependencies
    Authors - Keheliya Gallaba, John Ewart, Yves Junqueira, Shane McIntosh
    Venue - IEEE Transactions on Software Engineering, Vol. 48, No. 8, pp. 2040–2052, 2022
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    Related Tags - TSE 2022 continuous integration build performance
11. Quantifying, Characterizing, and Mitigating Flakily Covered Program Elements
    Authors - Shivashree Vysali, Shane McIntosh, Bram Adams
    Venue - IEEE Transactions on Software Engineering, Vol. 48, No. 3, pp. 1018–1029, 2022
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    Related Tags - TSE 2022 software quality flaky tests
12. Code Reviews with Divergent Review Scores: An Empirical Study of the OpenStack and Qt Communities
    Authors - Toshiki Hirao, Shane McIntosh, Akinori Ihara, Kenichi Matsumoto
    Venue - IEEE Transactions on Software Engineering, Vol. 48, No. 1, pp. 69–81, 2022
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    Related Tags - TSE 2022 code review integration
13. Use and Misuse of Continuous Integration Features: An Empirical Study of Projects that (mis)use Travis CI
    Authors - Keheliya Gallaba, Shane McIntosh
    Venue - IEEE Transactions on Software Engineering, Vol. 46, No. 1, pp. 33–50, 2020
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    Related Tags - TSE 2020 continuous integration anti-patterns software evolution
14. The Impact of Automated Parameter Optimization on Defect Prediction Models
    Authors - Chakkrit Tantithamthavorn, Shane McIntosh, Ahmed E. Hassan, Kenichi Matsumoto
    Venue - Transactions on Software Engineering, Vol. 45, No. 7, pp. 683–711, 2019
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    Related Tags - TSE 2019 software quality defect prediction
15. Are Fix-Inducing Changes a Moving Target? A Longitudinal Case Study of Just-In-Time Defect Prediction
    Authors - Shane McIntosh, Yasutaka Kamei
    Venue - IEEE Transactions on Software Engineering, Vol. 44, No. 5, pp. 412–428, 2018
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    Related Tags - TSE 2018 software quality defect prediction
16. A Framework for Evaluating the Results of the SZZ Approach for Identifying Bug-Introducing Changes
    Authors - Daniel Alencar da Costa, Shane McIntosh, Weiyi Shang, Uirá Kulesza, Roberta Coelho, Ahmed E. Hassan
    Venue - IEEE Transactions on Software Engineering, Vol. 43, No. 7, pp. 641–657, 2017
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    Related Tags - TSE 2017 software quality defect prediction
17. The Use of Summation to Aggregate Software Metrics Hinders the Performance of Defect Prediction Models
    Authors - Feng Zhang, Ahmed E. Hassan, Shane McIntosh, Ying Zou
    Venue - IEEE Transactions on Software Engineering, Vol. 43, No. 5, pp. 476-491, 2017
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    Related Tags - TSE 2017 software quality defect prediction
18. An Empirical Comparison of Model Validation Techniques for Defect Prediction Models
    Authors - Chakkrit Tantithamthavorn, Shane McIntosh, Ahmed E. Hassan, Kenichi Matsumoto
    Venue - IEEE Transactions on Software Engineering, Vol. 41, No. 1, pp. 1-18, 2017
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    Related Tags - TSE 2017 software quality defect prediction
19. Comments on "Researcher Bias: The Use of Machine Learning in Software Defect Prediction"
    Authors - Chakkrit Tantithamthavorn, Shane McIntosh, Ahmed E. Hassan, Kenichi Matsumoto
    Venue - IEEE Transactions on Software Engineering, Vol. 42, No. 11, pp. 1092-1094, 2016
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    Related Tags - TSE 2016 software quality defect prediction