Cited by 1Software Engineering ResearchSoftware Engineering Techniques and PracticesAdvanced Software Engineering Methodologies

Does Co-Development with AI Assistants Lead to More Maintainable Code? Replication Package

Open access
Equal Experts, Markus, Borg, Couderc, Noric·2025·Zenodo

Abstract

This is the replication package for the study "Echoes of AI: Investigating the Downstream Effects of AI Assistants on Software Maintainability" preregistered aat ICSME 2024 as “Does Co-Development with AI Assistants Lead to More Maintainable Code?” Abstract from the registered report: [Background/Context] AI assistants like GitHub Copilot are transforming software engineering, with several studies highlighting 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. Note: To maintain the integrity of the study, i.e., preventing any leakage to AI assistants' training data, we choose not to host the code in a public git repository. Instead, all relevant documents and code are shared through a replication package on Zenodo, available as PDF documents generated by repo2pdf (https://github.com/BankkRoll/repo2pdf). We have deliberately used settings to obfuscate the code (e.g., line numbers) to ensure it will not be scraped by any large language models before the study has been completed. Contents: Task 1 instructions. Task 2 instructions. The source code that the participants received. A causal graph with analysis details. Archive containing anonymized experimental data and analysis scripts.

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DataCite · 10.5281/zenodo.17977295

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  • All materials required to replicate the study (task instructions, cleaned and anonymized participant data, and the causal graph) are available on Zenodo under the Creative Commons Attribution 4.0 International license.

    “All materials required to replicate this study, e.g., task instructions, cleaned and anonymized participant data, and the causal graph, are available on Zenodo (Experts et al. 2025) under the Creative Commons Attribution 4.0 International license.”

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Authors
Experts, E., Markus, B., & Couderc, N.
Affiliations
  • CodeScene
  • Lund University (SE)
Venue
Zenodo
Publisher
Zenodo
Published
2025-12-18
OpenAlex
W6930062273
Cited by
0
References
0
Type
dataset
Language
en
Open access
Locations
Zenodo (CERN European Organization for Nuclear Research) (cc-by)
Topics
  • Physical Sciences › Computer Science › Information Systems › Software Engineering Research
  • Physical Sciences › Computer Science › Information Systems › Software Engineering Techniques and Practices
  • Physical Sciences › Computer Science › Artificial Intelligence › Advanced Software Engineering Methodologies
Keywords
AI assistants, GitHub Copilot, software maintainability, co-development, code evolution, developer productivity, code quality, test coverage, large language models, Java

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