Cited By
View all- Kotti ZGalanopoulou RSpinellis D(2022)Machine Learning for Software Engineering: A Tertiary StudyACM Computing Surveys10.1145/357290555:12(1-39)Online publication date: 30-Nov-2022
MALTESQUE 2019 Workshop Summary
Authors: 
Francesca Arcelli Fontana, Gilles Perrouin, Apostolos Ampatzoglou, Mathieu Archer, Bartosz Walter, Maxime Cordy, Fabio Palomba, 
Xavier DevroeyAuthors Info & Claims
Francesca Arcelli Fontana, Gilles Perrouin, Apostolos Ampatzoglou, Mathieu Archer, Bartosz Walter, Maxime Cordy, Fabio Palomba, Xavier DevroeyAuthors Info & ClaimsPages 34 - 35
Abstract
Welcome to the third edition of the workshop on Machine Learn- ing Techniques for Software Quality Evaluation (MaLTeSQuE 2019), held in Tallinn, Estonia, August 27th, 2019, co-located with ESEC / FSE 2019. This year MALTESQUE merged with the MASES (Machine Learning and Software Engineering in Symbiosis) work- shop, co-located with the ASE 2018 conference. Ten papers from all over the world were submitted, seven of them were accepted. The program also featured a keynote by Lionel Briand on the use of machine learning to improve software testing.
References
[1]
Markus Borg, Oscar Svensson, Kristian Berg, and Daniel Hansson. SZZ unleashed: an open implementation of the SZZ algorithm - featuring example usage in a study of just-in-time bug prediction for the jenkins project. In Fontana et al. [3], pages 7{12.
[2]
Harald Foidl and Michael Felderer. Risk-based data validation in machine learning-based software systems. In Fontana et al. [3], pages 13{18.
[3]
Francesca Arcelli Fontana, Bartosz Walter, Apostolos Ampatzoglou, Fabio Palomba, Gilles Perrouin, Mathieu Acher, Maxime Cordy, and Xavier Devroey, editors. Proceedings of the 3rd ACM SIGSOFT International Workshop on Machine Learning Techniques for Software Quality Evaluation, MaLTeSQuE@ESEC/SIGSOFT FSE 2019, Tallinn, Estonia, August 27, 2019. ACM, 2019.
[4]
Valentina Lenarduzzi, Antonio Martini, Davide Taibi, and Damian Andrew Tamburri. Towards surgically-precise technical debt estimation: early results and research roadmap. In Fontana et al. [3], pages 37{42.
[5]
Aravind Nair, Karl Meinke, and Sigrid Eldh. Leveraging mutants for automatic prediction of metamorphic relations using machine learning. In Fontana et al. [3], pages 1{6.
[6]
Fabiano Pecorelli, Dario Di Nucci, Coen De Roover, and Andrea De Lucia. On the role of data balancing for machine learning-based code smell detection. In Fontana et al. [3], pages 19{24.
[7]
Gilles Perrouin, Mathieu Acher, Maxime Cordy, and Xavier Devroey, editors. Proceedings of the 1st International Workshop on Machine Learning and Software Engineering in Symbiosis, MASES@ASE 2018, Montpellier, France, September 3, 2018. ACM, 2018.
[8]
Md. Abdur Rahman, Md. Ariful Haque, Md. Nurul Ahad Tawhid, and Md. Saeed Siddik. Classifying non-functional requirements using RNN variants for quality software development. In Fontana et al. [3], pages 25{30.
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Nickolay Viuginov and Andrey Filchenkov. A machine learning based automatic folding of dynamically typed languages. In Fontana et al. [3], pages 31{36.
- MALTESQUE 2019 Workshop Summary
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Published: 22 January 2020
Published in SIGSOFT Volume 45, Issue 1
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View all- Kotti ZGalanopoulou RSpinellis D(2022)Machine Learning for Software Engineering: A Tertiary StudyACM Computing Surveys10.1145/357290555:12(1-39)Online publication date: 30-Nov-2022
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