MaLTeSQuE 2022 Workshop Summary

Author:

Cordy Maxime1,Xie Xiaofei2,Xu Bowen2,Stamatia Bibi3

Affiliation:

1. University of Luxembourg, Luxembourg

2. University Singapore, Singapore

3. University of Western Macedonia, Greece

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.

Publisher

Association for Computing Machinery (ACM)

Subject

Pharmacology (medical),Complementary and alternative medicine,Pharmaceutical Science

Reference8 articles.

1. Proceedings of the 6th International Workshop on Machine Learning Techniques for Software Quality Evaluation

2. The promise and perils of using machine learning when engineering software (keynote paper)

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. 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. Neural language models for code quality identification

5. Are machine programming systems using right source-code measures to select code repositories?

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