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Example-Based Automatic Migration of Continuous Integration Systems

Dhia Elhaq Rzig, Alaa Houerbi, Chungha Sung, Foyzul Hassan

TL;DR

CI system migrations between tools like Travis CI and GitHub Actions are labor-intensive and error-prone due to DSL differences and limited tool support. CIMig introduces an example-based migration framework that learns translation and hierarchization rules from semantically equivalent CI configurations using Apriori Rule Mining and Frequent-Tree Mining to produce TARs for AST-based translation. In evaluation, CIMig translates a majority of Travis and GHA configurations with competitive similarity scores and delivers meaningful time savings in a user study, while also identifying limitations such as non-equivalents and multi-level dependencies. The approach demonstrates practical impact by enabling faster, data-driven CI migrations and shows promise for generalization to other configuration systems, with potential enhancement via LLM-assisted rule generation. Overall, CIMig contributes a scalable, dataset-driven method for automatic CI configuration migration that can augment or extend existing manual or rule-based importers.

Abstract

Continuous Integration (CI) is a widely adopted practice for faster code change integration and testing. Developers often migrate between CI systems in pursuit of features like matrix building or better logging. However, this migration is effort intensive and error-prone owing to limited knowledge of the new CI system and its syntax. Moreover, these migrations require multiple iterations and significant time to achieve stability in the new CI system, and there is insufficient support for the automatic migration of CI configurations. To mitigate this, we propose a novel approach for CI system's automatic migration: CIMig. Our approach utilizes Example-Based mining, where it extracts translation rules and configuration patterns from existing migration examples, and employs them to reproduce this migration in new contexts. To empirically validate and evaluate our approach, we apply it to the migration between Travis CI and GitHub Actions. We gathered learnings from 1001 projects, and then applied them to migrate an evaluation set of 251 projects. This helped us perform a qualitative and quantitative evaluation of CIMig, and we contextualize our results by comparing them with those of the manual-rule-based GitHub Actions Importer. Furthermore, our tool generated files that were rated favorably by developers and saved them an average of 42.4 minutes over the manual migration of these same projects. Our learning-based approach is also more flexible, as proven by our ability to apply it to migrate GitHub Actions files to Travis, which GitHub Actions Importer can not do. We believe CIMig is the first approach of its kin to migrate CI systems and can be applied to other software configuration system migrations. Our replication package is available at [5].

Example-Based Automatic Migration of Continuous Integration Systems

TL;DR

CI system migrations between tools like Travis CI and GitHub Actions are labor-intensive and error-prone due to DSL differences and limited tool support. CIMig introduces an example-based migration framework that learns translation and hierarchization rules from semantically equivalent CI configurations using Apriori Rule Mining and Frequent-Tree Mining to produce TARs for AST-based translation. In evaluation, CIMig translates a majority of Travis and GHA configurations with competitive similarity scores and delivers meaningful time savings in a user study, while also identifying limitations such as non-equivalents and multi-level dependencies. The approach demonstrates practical impact by enabling faster, data-driven CI migrations and shows promise for generalization to other configuration systems, with potential enhancement via LLM-assisted rule generation. Overall, CIMig contributes a scalable, dataset-driven method for automatic CI configuration migration that can augment or extend existing manual or rule-based importers.

Abstract

Continuous Integration (CI) is a widely adopted practice for faster code change integration and testing. Developers often migrate between CI systems in pursuit of features like matrix building or better logging. However, this migration is effort intensive and error-prone owing to limited knowledge of the new CI system and its syntax. Moreover, these migrations require multiple iterations and significant time to achieve stability in the new CI system, and there is insufficient support for the automatic migration of CI configurations. To mitigate this, we propose a novel approach for CI system's automatic migration: CIMig. Our approach utilizes Example-Based mining, where it extracts translation rules and configuration patterns from existing migration examples, and employs them to reproduce this migration in new contexts. To empirically validate and evaluate our approach, we apply it to the migration between Travis CI and GitHub Actions. We gathered learnings from 1001 projects, and then applied them to migrate an evaluation set of 251 projects. This helped us perform a qualitative and quantitative evaluation of CIMig, and we contextualize our results by comparing them with those of the manual-rule-based GitHub Actions Importer. Furthermore, our tool generated files that were rated favorably by developers and saved them an average of 42.4 minutes over the manual migration of these same projects. Our learning-based approach is also more flexible, as proven by our ability to apply it to migrate GitHub Actions files to Travis, which GitHub Actions Importer can not do. We believe CIMig is the first approach of its kin to migrate CI systems and can be applied to other software configuration system migrations. Our replication package is available at [5].
Paper Structure (31 sections, 12 figures, 1 table, 1 algorithm)

This paper contains 31 sections, 12 figures, 1 table, 1 algorithm.

Table of Contents

  1. Introduction
  2. Problem Contextualization
  3. Background
  4. Continuous Integration
  5. Example-based Learning
  6. Apriori Rule Mining
  7. Frequent Tree Mining and Tree Association Rules
  8. Approach
  9. Data Preparation
  10. Training CIMig
  11. Task A: Apriori Rule Mining Process
  12. Task B: Frequent-Tree Mining Process.
  13. Parameter Tuning for Task A & B
  14. Using CIMig
  15. Step 1: Abstraction and Parsing
  16. ...and 16 more sections

Figures (12)

  • Figure 1: Example of Migration from Travis CI to GitHub Actions
  • Figure 2: Overview of CIMig when used to migrate between Travis CI and GHA
  • Figure 3: Travis CI H-2 AST Extraction Example
  • Figure 4: Example of Frequent Tree mined from GHA and Generated TAR
  • Figure 5: Example of Travis CI File and its corresponding AST
  • ...and 7 more figures