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)


@InProceedings{MaLTeSQuE22p1, author = {Yuriy Brun}, title = {The Promise and Perils of Using Machine Learning When Engineering Software (Keynote Paper)}, booktitle = {Proc.\ MaLTeSQuE}, publisher = {ACM}, pages = {1-0}, doi = {10.1145/3549034.3570200}, year = {2022}, } Publisher's Version

Machine Learning for Code Assessment

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


@InProceedings{MaLTeSQuE22p7, author = {Srinivasan Sengamedu and Hangqi Zhao}, title = {Neural Language Models for Code Quality Identification}, booktitle = {Proc.\ MaLTeSQuE}, publisher = {ACM}, pages = {7-6}, doi = {10.1145/3549034.3561175}, year = {2022}, } Publisher's Version

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


@InProceedings{MaLTeSQuE22p13, author = {Niranjan Hasabnis}, title = {Are Machine Programming Systems using Right Source-Code Measures to Select Code Repositories?}, booktitle = {Proc.\ MaLTeSQuE}, publisher = {ACM}, pages = {13-12}, doi = {10.1145/3549034.3561176}, year = {2022}, } 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)


@InProceedings{MaLTeSQuE22p19, author = {Matthew Yit Hang Yeow and Chun Yong Chong and Mei Kuan Lim}, title = {On the Application of Machine Learning Models to Assess and Predict Software Reusability}, booktitle = {Proc.\ MaLTeSQuE}, publisher = {ACM}, pages = {19-18}, doi = {10.1145/3549034.3561177}, year = {2022}, } 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)


@InProceedings{MaLTeSQuE22p25, author = {Nikolaos Nikolaidis and Dimitrios Zisis and Apostolos Ampatzoglou and Nikolaos Mittas and Alexander Chatzigeorgiou}, title = {Using Machine Learning to Guide the Application of Software Refactorings: A Preliminary Exploration}, booktitle = {Proc.\ MaLTeSQuE}, publisher = {ACM}, pages = {25-24}, doi = {10.1145/3549034.3561178}, year = {2022}, } 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)


@InProceedings{MaLTeSQuE22p31, author = {Liu Chao and Xie Qiaoluan and Li Yong and Xu Yang and Choi Hyun-Deok}, title = {DeepCrash: Deep Metric Learning for Crash Bucketing Based on Stack Trace}, booktitle = {Proc.\ MaLTeSQuE}, publisher = {ACM}, pages = {31-30}, doi = {10.1145/3549034.3561179}, year = {2022}, } Publisher's Version

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