Toward Automatically Completing GitHub Workflows.
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| Title: | Toward Automatically Completing GitHub Workflows. |
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| 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] |
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| Database: | Engineering Source |
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| 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] |
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| DOI: | 10.1145/3597503.3623351 |
