MaLTeSQuE 2022 Workshop Summary
Pages 95 - 96
Abstract
Welcome to the sixth edition of the workshop on Machine Learning Techniques for Software Quality Evaluation (MaLTeSQuE 2022), held in Singapore, November 18th, 2022, co-located with ESEC / FSE 2022 [1]. Six papers from all over the world were submitted, five of them were accepted. The program also featured two keynotes by Yuriy Brun on the promise and perils of using machine learning when engineering software and Mike Papadakis on the best practices in assessment of deep learning testing methods.
References
[1]
Maxime Cordy, Xiaofei Xie, Bowen Xu, and Bibi Stamatia, editors. Proceedings of the 6th International Workshop on Machine Learning Techniques for Software Quality Evaluation, MaLTeSQuE 2022, Singapore, Singapore, 18 November 2022. ACM, 2022.
[2]
Yuriy Brun. The promise and perils of using machine learning when engineering software (keynote paper). In Maxime Cordy, Xiaofei Xie, Bowen Xu, and Bibi Stamatia, editors, Proceedings of the 6th International Workshop on Machine Learning Techniques for Software Quality Evaluation, MaLTeSQuE 2022, Singapore, Singapore, 18 November 2022, pages 1--4. ACM, 2022.
[3]
Mike Papadakis. Best practices in (empirical) assessment of deep learning testing methods (keynote paper). In Maxime Cordy, Xiaofei Xie, Bowen Xu, and Bibi Stamatia, editors, Proceedings of the 6th International Workshop on Machine Learning Techniques for Software Quality Evaluation, MaLTeSQuE 2022, Singapore, Singapore, 18 November 2022, pages 1--4. ACM, 2022.
[4]
Srinivasan Sengamedu and Hangqi Zhao. Neural language models for code quality identification. In Maxime Cordy, Xiaofei Xie, Bowen Xu, and Bibi Stamatia, editors, Proceedings of the 6th International Workshop on Machine Learning Techniques for Software Quality Evaluation, MaLTeSQuE 2022, Singapore, Singapore, 18 November 2022, pages 5--10. ACM, 2022.
[5]
Niranjan Hasabnis. Are machine programming systems using right source-code measures to select code repositories? In Maxime Cordy, Xiaofei Xie, Bowen Xu, and Bibi Stamatia, editors, Proceedings of the 6th International Workshop on Machine Learning Techniques for Software Quality Evaluation, MaLTeSQuE 2022, Singapore, Singapore, 18 November 2022, pages 11--16. ACM, 2022.
[6]
Nikolaos Nikolaidis, Dimitrios Zisis, Apostolos Ampatzoglou, Nikolaos Mittas, and Alexander Chatzigeorgiou. Using machine learning to guide the application of software refactorings: a preliminary exploration. In Maxime Cordy, Xiaofei Xie, Bowen Xu, and Bibi Stamatia, editors, Proceedings of the 6th International Workshop on Machine Learning Techniques for Software Quality Evaluation, MaLTeSQuE 2022, Singapore, Singapore, 18 November 2022, pages 23--28. ACM, 2022.
[7]
Chao Liu, Qiaoluan Xie, Yong Li, Yang Xu, and Hyun-Deok Choi. Deepcrash: deep metric learning for crash bucketing based on stack trace. In Maxime Cordy, Xiaofei Xie, Bowen Xu, and Bibi Stamatia, editors, Proceedings of the 6th International Workshop on Machine Learning Techniques for Software Quality Evaluation, MaLTeSQuE 2022, Singapore, Singapore, 18 November 2022, pages 29--34. ACM, 2022.
[8]
Matthew Yit Hang Yeow, Chun Yong Chong, and Mei Kuan Lim. On the application of machine learning models to assess and predict software reusability. In Maxime Cordy, Xiaofei Xie, Bowen Xu, and Bibi Stamatia, editors, Proceedings of the 6th International Workshop on Machine Learning Techniques for Software Quality Evaluation, MaLTeSQuE 2022, Singapore, Singapore, 18 November 2022, pages 17--22. ACM, 2022.
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Association for Computing Machinery
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Publication History
Published: 17 January 2023
Published in SIGSOFT Volume 48, Issue 1
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