MaLTeSQuE 2022 – Proceedings

6th International Workshop on Machine Learning Techniques for Software Quality Evaluation (MaLTeSQuE 2022)

Frontmatter

Keynote

The Promise and Perils of Using Machine Learning When Engineering Software (Keynote Paper)
Yuriy Brun
(University of Massachusetts at Amherst, USA)

Publisher's Version

Machine Learning for Code Assessment

Neural Language Models for Code Quality Identification
Srinivasan Sengamedu and Hangqi Zhao
(Amazon, USA; Twitter, USA)

Publisher's Version

Are Machine Programming Systems using Right Source-Code Measures to Select Code Repositories?
Niranjan Hasabnis
(Intel Corporation, USA)

Publisher's Version

Machine Learning for Software Processes

On the Application of Machine Learning Models to Assess and Predict Software Reusability
Matthew Yit Hang Yeow, Chun Yong Chong, and Mei Kuan Lim
(Monash University, Malaysia)

Publisher's Version Published Artifact Artifacts Available

Using Machine Learning to Guide the Application of Software Refactorings: A Preliminary Exploration
Nikolaos Nikolaidis, Dimitrios Zisis, Apostolos Ampatzoglou, Nikolaos Mittas, and Alexander Chatzigeorgiou
(University of Macedonia, Greece; Accenture, Greece; International Hellenic University, Greece)

Publisher's Version

DeepCrash: Deep Metric Learning for Crash Bucketing Based on Stack Trace
Liu Chao, Xie Qiaoluan, Li Yong, Xu Yang, and Choi Hyun-Deok
(SAP Labs, China; SAP Labs, South Korea)

Publisher's Version

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