Developer Behaviors in Validating and Repairing LLM-Generated Code Using IDE and Eye Tracking
Abstract
The increasing use of large language model (LLM)-powered code generation tools, such as GitHub Copilot, is transforming software engineering practices. This paper investigates how developers validate and repair code generated by Copilot and examines the impact of code provenance awareness during these processes. We conducted a lab study with 28 participants tasked with validating and repairing Copilot-generated code in three software projects. Participants were randomly divided into two groups: one informed about the provenance of LLM-generated code and the other not. We collected data on IDE interactions, eye-tracking, cognitive workload assessments, and conducted semi-structured interviews. Our results indicate that, without explicit information, developers often fail to identify the LLM origin of the code. Developers exhibit LLM-specific behaviors such as frequent switching between code and comments, different attentional focus, and a tendency to delete and rewrite code. Being aware of the code’s provenance led to improved performance, increased search efforts, more frequent Copilot usage, and higher cognitive workload. These findings enhance our understanding of developer interactions with LLM-generated code and inform the design of tools for effective human-LLM collaboration in software development.
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Tang et al. conducted a focused study on how developers validate and repair LLM-generated code, finding that awareness of AI authorship affected both behavior and cognitive load.
“Recently, Tang et al. [15] conducted a focused study on how developers validate and repair LLM-generated code, finding that awareness of AI authorship affected both behavior and cognitive load.”
From Towards Decoding Developer Cognition in the Age of AI Assistants (2025) · cites this paper as [15]
Tang et al. [15] observed that developers similarly shifted between prompt and code to resolve mismatches between intended and generated behaviour.
“Which is in line with prior observations by Tang et al. [15], where developers similarly shifted between prompt and code to resolve mismatches between intended and generated behaviour.”
From Same Scrutiny, More Time: Eye Tracking Insights into Reviewing LLM-Labelled Code (2026) · cites this paper as [15]
Tang et al. [15] observed similar behaviour when developers repaired code generated by GitHub Copilot.
“A similar behaviour was observed by Tang et al. [15] when developers repaired code generated by GitHub Copilot.”
From Same Scrutiny, More Time: Eye Tracking Insights into Reviewing LLM-Labelled Code (2026) · cites this paper as [15]
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- Authors
- Tang, N., Chen, M., Ning, Z., Bansal, A., Huang, Y., McMillan, C., & Li, T. J.-J.
- Affiliations
- University of Notre Dame (US)
- Vanderbilt University (US)
- Venue
- 2024 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC)
- Publisher
- IEEE
- Published
- 2024-09-02
- Pages
- 40-46
- ISSN
- 1943-6092 (ISSN-L)
- OpenAlex
- W4403447861
- Cited by
- 18
- References
- 49
- Citations by year
- 2026: 7 · 2025: 10 · 2024: 1
- FWCI
- 6.84
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC) (publishedVersion)
- Funders
- 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 › Computer Science Applications › Open Source Software Innovations
- Keywords
- LLM-generated code, GitHub Copilot, code validation, code repair, developer behavior, human-LLM collaboration, mental workload, eye tracking, attentional focus
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