Example-Based Automatic Migration of Continuous Integration Systems

• URL: http://arxiv.org/abs/2407.02644v1
• Date: Tue, 2 Jul 2024 20:19:21 GMT
• Title: Example-Based Automatic Migration of Continuous Integration Systems
• Authors: Dhia Elhaq Rzig, Alaa Houerbi, Chungha Sung, Foyzul Hassan,
• Abstract summary: 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. This migration is effort intensive and error-prone owing to limited knowledge of the new CI system and its syntax. We propose a novel approach for CI system's automatic migration: CIMig.
• Score: 2.2836654317217326
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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].

Related papers

• VersiCode: Towards Version-controllable Code Generation [58.82709231906735]
  Large Language Models (LLMs) have made tremendous strides in code generation, but existing research fails to account for the dynamic nature of software development. We propose two novel tasks aimed at bridging this gap: version-specific code completion (VSCC) and version-aware code migration (VACM) We conduct an extensive evaluation on VersiCode, which reveals that version-controllable code generation is indeed a significant challenge.
  arXiv  Detail & Related papers  (2024-06-11T16:15:06Z)
• Towards Modular LLMs by Building and Reusing a Library of LoRAs [64.43376695346538]
  We study how to best build a library of adapters given multi-task data. We introduce model-based clustering, MBC, a method that groups tasks based on the similarity of their adapter parameters. To re-use the library, we present a novel zero-shot routing mechanism, Arrow, which enables dynamic selection of the most relevant adapters.
  arXiv  Detail & Related papers  (2024-05-18T03:02:23Z)
• Detecting Continuous Integration Skip : A Reinforcement Learning-based Approach [0.4297070083645049]
  Continuous Integration (CI) practices facilitate the seamless integration of code changes by employing automated building and testing processes. Some frameworks, such as Travis CI and GitHub Actions have significantly contributed to simplifying and enhancing the CI process. Developers continue to encounter difficulties in accurately flagging commits as either suitable for CI execution or as candidates for skipping.
  arXiv  Detail & Related papers  (2024-05-15T18:48:57Z)
• Design2Code: Benchmarking Multimodal Code Generation for Automated Front-End Engineering [74.99736967448423]
  We construct Design2Code - the first real-world benchmark for this task. We manually curate 484 diverse real-world webpages as test cases and develop a set of automatic evaluation metrics. Our fine-grained break-down metrics indicate that models mostly lag in recalling visual elements from the input webpages and generating correct layout designs.
  arXiv  Detail & Related papers  (2024-03-05T17:56:27Z)
• DS-Agent: Automated Data Science by Empowering Large Language Models with Case-Based Reasoning [56.887047551101574]
  We present DS-Agent, a novel framework that harnesses large language models (LLMs) agent and case-based reasoning (CBR) In the development stage, DS-Agent follows the CBR framework to structure an automatic iteration pipeline, which can flexibly capitalize on the expert knowledge from Kaggle. In the deployment stage, DS-Agent implements a low-resource deployment stage with a simplified CBR paradigm, significantly reducing the demand on foundational capabilities of LLMs.
  arXiv  Detail & Related papers  (2024-02-27T12:26:07Z)
• A Generative AI Assistant to Accelerate Cloud Migration [2.9248916859490173]
  The Cloud Migration LLM accepts input from the user specifying the parameters of their migration, and outputs a migration strategy with an architecture diagram. A user study suggests that the migration LLM can assist inexperienced users in finding the right cloud migration profile, while avoiding complexities of a manual approach.
  arXiv  Detail & Related papers  (2024-01-03T14:13:24Z)
• ML-Bench: Evaluating Large Language Models and Agents for Machine Learning Tasks on Repository-Level Code [76.84199699772903]
  ML-Bench is a benchmark rooted in real-world programming applications that leverage existing code repositories to perform tasks. To evaluate both Large Language Models (LLMs) and AI agents, two setups are employed: ML-LLM-Bench for assessing LLMs' text-to-code conversion within a predefined deployment environment, and ML-Agent-Bench for testing autonomous agents in an end-to-end task execution within a Linux sandbox environment.
  arXiv  Detail & Related papers  (2023-11-16T12:03:21Z)
• Interactive, Iterative, Tooled, Rule-Based Migration of Microsoft Access to Web Technologies [0.11650821883155184]
  We are working on migrating Microsoft Access monolithic applications to the web front-end and producing back-end. To enable the developers to drive the migration to the target systems, we propose an Interactive, Iterative, Tooled, Rule-Based Migration approach.
  arXiv  Detail & Related papers  (2023-09-07T06:46:28Z)
• A Preliminary Investigation of MLOps Practices in GitHub [10.190501703364234]
  Machine learning applications have led to an increasing interest in MLOps. We present an initial investigation of the MLOps practices implemented in a set of ML-enabled systems retrieved from GitHub.
  arXiv  Detail & Related papers  (2022-09-23T07:29:56Z)
• Characterizing Python Library Migrations [2.2557806157585834]
  We label 3,096 migration-related code changes in 335 Python library migrations. We find that 40% of library pairs have API mappings that involve non-function program elements. On average, a developer needs to learn about 4 APIs and 2 API mappings to perform a migration.
  arXiv  Detail & Related papers  (2022-07-03T21:00:08Z)
• Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents [111.33545170562337]
  We investigate the possibility of grounding high-level tasks, expressed in natural language, to a chosen set of actionable steps. We find that if pre-trained LMs are large enough and prompted appropriately, they can effectively decompose high-level tasks into low-level plans. We propose a procedure that conditions on existing demonstrations and semantically translates the plans to admissible actions.
  arXiv  Detail & Related papers  (2022-01-18T18:59:45Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.