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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2408.10758 |
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| _version_ | 1866913473695318016 |
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| author | Borg, Markus Hewett, Dave Graham, Donald Couderc, Noric Söderberg, Emma Church, Luke Farley, Dave |
| author_facet | Borg, Markus Hewett, Dave Graham, Donald Couderc, Noric Söderberg, Emma Church, Luke Farley, Dave |
| contents | [Background/Context] AI assistants like GitHub Copilot are transforming software engineering; several studies have highlighted productivity improvements. However, their impact on code quality, particularly in terms of maintainability, requires further investigation. [Objective/Aim] This study aims to examine the influence of AI assistants on software maintainability, specifically assessing how these tools affect the ability of developers to evolve code. [Method] We will conduct a two-phased controlled experiment involving professional developers. In Phase 1, developers will add a new feature to a Java project, with or without the aid of an AI assistant. Phase 2, a randomized controlled trial, will involve a different set of developers evolving random Phase 1 projects - working without AI assistants. We will employ Bayesian analysis to evaluate differences in completion time, perceived productivity, code quality, and test coverage. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_10758 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Does Co-Development with AI Assistants Lead to More Maintainable Code? A Registered Report Borg, Markus Hewett, Dave Graham, Donald Couderc, Noric Söderberg, Emma Church, Luke Farley, Dave Software Engineering [Background/Context] AI assistants like GitHub Copilot are transforming software engineering; several studies have highlighted productivity improvements. However, their impact on code quality, particularly in terms of maintainability, requires further investigation. [Objective/Aim] This study aims to examine the influence of AI assistants on software maintainability, specifically assessing how these tools affect the ability of developers to evolve code. [Method] We will conduct a two-phased controlled experiment involving professional developers. In Phase 1, developers will add a new feature to a Java project, with or without the aid of an AI assistant. Phase 2, a randomized controlled trial, will involve a different set of developers evolving random Phase 1 projects - working without AI assistants. We will employ Bayesian analysis to evaluate differences in completion time, perceived productivity, code quality, and test coverage. |
| title | Does Co-Development with AI Assistants Lead to More Maintainable Code? A Registered Report |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2408.10758 |