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Toward Automatically Completing GitHub Workflows.

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Title: Toward Automatically Completing GitHub Workflows.
Authors: Mastropaolo, Antonio1 antonio.mastropaolo@usi.ch, Zampetti, Fiorella2 fiorella.zampetti@unisannio.it, Bavota, Gabriele3 gabriele.bavota@usi.ch, Di Penta, Massimiliano2 dipenta@unisannio.it
Source: ICSE: International Conference on Software Engineering. 2024, p1-12. 12p.
Subjects: Github Inc., Workflow, Computer software development, Empirical research, Machine learning
Abstract: Continuous integration and delivery (CI/CD) are nowadays at the core of software development. Their benefits come at the cost of setting up and maintaining the CI/CD pipeline, which requires knowledge and skills often orthogonal to those entailed in other software-related tasks. While several recommender systems have been proposed to support developers across a variety of tasks, little automated support is available when it comes to setting up and maintaining CI/CD pipelines. We present GH-WCOM (GitHub Workflow COMpletion), a Transformer-based approach supporting developers in writing a specific type of CI/CD pipelines, namely GitHub workflows. To deal with such a task, we designed an abstraction process to help the learning of the transformer while still making GH-WCOM able to recommend very peculiar workflow elements such as tool options and scripting elements. Our empirical study shows that GH-WCOM provides up to 34.23% correct predictions, and the model's confidence is a reliable proxy for the recommendations' correctness likelihood. [ABSTRACT FROM AUTHOR]
Copyright of ICSE: International Conference on Software Engineering is the property of Association for Computing Machinery and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Toward Automatically Completing GitHub Workflows.
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  Data: <searchLink fieldCode="AR" term="%22Mastropaolo%2C+Antonio%22">Mastropaolo, Antonio</searchLink><relatesTo>1</relatesTo><i> antonio.mastropaolo@usi.ch</i><br /><searchLink fieldCode="AR" term="%22Zampetti%2C+Fiorella%22">Zampetti, Fiorella</searchLink><relatesTo>2</relatesTo><i> fiorella.zampetti@unisannio.it</i><br /><searchLink fieldCode="AR" term="%22Bavota%2C+Gabriele%22">Bavota, Gabriele</searchLink><relatesTo>3</relatesTo><i> gabriele.bavota@usi.ch</i><br /><searchLink fieldCode="AR" term="%22Di+Penta%2C+Massimiliano%22">Di Penta, Massimiliano</searchLink><relatesTo>2</relatesTo><i> dipenta@unisannio.it</i>
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  Data: <searchLink fieldCode="JN" term="%22ICSE%3A+International+Conference+on+Software+Engineering%22">ICSE: International Conference on Software Engineering</searchLink>. 2024, p1-12. 12p.
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  Data: <searchLink fieldCode="DE" term="%22Github+Inc%2E%22">Github Inc.</searchLink><br /><searchLink fieldCode="DE" term="%22Workflow%22">Workflow</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+software+development%22">Computer software development</searchLink><br /><searchLink fieldCode="DE" term="%22Empirical+research%22">Empirical research</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink>
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  Data: Continuous integration and delivery (CI/CD) are nowadays at the core of software development. Their benefits come at the cost of setting up and maintaining the CI/CD pipeline, which requires knowledge and skills often orthogonal to those entailed in other software-related tasks. While several recommender systems have been proposed to support developers across a variety of tasks, little automated support is available when it comes to setting up and maintaining CI/CD pipelines. We present GH-WCOM (GitHub Workflow COMpletion), a Transformer-based approach supporting developers in writing a specific type of CI/CD pipelines, namely GitHub workflows. To deal with such a task, we designed an abstraction process to help the learning of the transformer while still making GH-WCOM able to recommend very peculiar workflow elements such as tool options and scripting elements. Our empirical study shows that GH-WCOM provides up to 34.23% correct predictions, and the model's confidence is a reliable proxy for the recommendations' correctness likelihood. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
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  Data: <i>Copyright of ICSE: International Conference on Software Engineering is the property of Association for Computing Machinery and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1145/3597503.3623351
    Languages:
      – Code: eng
        Text: English
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        PageCount: 12
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    Subjects:
      – SubjectFull: Github Inc.
        Type: general
      – SubjectFull: Workflow
        Type: general
      – SubjectFull: Computer software development
        Type: general
      – SubjectFull: Empirical research
        Type: general
      – SubjectFull: Machine learning
        Type: general
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      – TitleFull: Toward Automatically Completing GitHub Workflows.
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            NameFull: Bavota, Gabriele
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          Dates:
            – D: 01
              M: 05
              Text: 2024
              Type: published
              Y: 2024
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            – TitleFull: ICSE: International Conference on Software Engineering
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