MaLTeSQuE 2021 Workshop Summary-Reference-Cited by-同舟云学术

MaLTeSQuE 2021 Workshop Summary

Published:2022-01-25 Issue:1 Volume:47 Page:15-17
ISSN:0163-5948
Container-title:ACM SIGSOFT Software Engineering Notes
language:en
Short-container-title:SIGSOFT Softw. Eng. Notes

Author:

Feitosa Daniel¹,Catolino Gemma²,Lenarduzzi Valentina³,Ampatzoglou Apostolos⁴

Affiliation:

1. University of Groningen, Groningen, Netherlands

2. Tilburg University, Tilburg, Netherlands

3. LUT University, Lahti, Finland

4. University of Macedonia, Thessaloniki, Greece

Abstract

The 5th edition of the Machine Learning Techniques for Software Quality Evaluation (MaLTeSQuE 2021) workshop was held virtually (originally in Athens, Greece) on August 23rd, 2021, colocated with the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE 2021). MaLTeSQuE received a total of ten submissions from all over the world, from which six papers and three abstracts were included in the program. The program also featured a keynote by Tim Menzies on the future of machine learning techniques for software quality. This report summarizes the event and insights stemmed from the keynote and presentations in the three sessions of the workshop.

Publisher

Association for Computing Machinery (ACM)

Subject

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

Link

https://dl.acm.org/doi/pdf/10.1145/3502771.3502777

Reference7 articles.

1. A. Ampatzoglou , D. Feitosa , G. Catolino , and V. Lenarduzzi , editors . Proceedings of the 5th International Workshop on Machine Learning Techniques for Software Quality Evolution, MaLTESQuE@ESEC/FSE 2021 , Athens, Greece , August 23, 2021 . ACM. A. Ampatzoglou, D. Feitosa, G. Catolino, and V. Lenarduzzi, editors. Proceedings of the 5th International Workshop on Machine Learning Techniques for Software Quality Evolution, MaLTESQuE@ESEC/FSE 2021, Athens, Greece, August 23, 2021. ACM.

2. Comparing within- and cross-project machine learning algorithms for code smell detection

3. VaryMinions: leveraging RNNs to identify variants in event logs

4. Metrics selection for load monitoring of service-oriented system

5. Building a bot for automatic expert retrieval on discord