How Do Software Professionals Evaluate AI-Generated Code? (Registered Report)

2026-07-13 · Source: cs.SE updates on arXiv.org · Field: Technology & Digital — Software Development & Engineering, Artificial Intelligence & Machine Learning · Depth: Expert, extended

Summary

A registered report outlines a constructivist grounded theory study investigating how software professionals evaluate AI-generated code, a critical area given the increasing integration of tools like GitHub Copilot, ChatGPT, and Claude into everyday workflows. The research aims to develop a theory grounded in professionals' evaluative practices, perceptions, and preferences. The methodology involves a survey, semi-structured interviews, and laddering interviews, targeting 20-50 software professionals until theoretical saturation is achieved. An initial survey in Finland gathered 163 responses between December 2025 and February 2026, exploring generative AI adoption, validation practices, and trust. The study acknowledges challenges such as understanding non-authored code, hidden bugs, debugging difficulties, and risks of over-reliance and skill decay, which create tension between productivity gains and the need for careful evaluation.

Key takeaway

For software engineers integrating AI code generation tools, you must develop robust evaluation strategies to mitigate risks like hidden bugs, over-reliance, and skill decay. Recognize that productivity gains can be negated by time spent on careful evaluation, so prioritize understanding how your team constructs and reasons about AI-generated code. Consider implementing structured evaluation frameworks to ensure quality and alignment with intent, rather than solely trusting AI outputs.

Key insights

The study aims to theorize how software professionals evaluate AI-generated code amidst productivity pressures and inherent challenges.

Principles

• AI-generated code evaluation is complex.
• Over-reliance risks skill decay.
• Productivity pressures influence evaluation.

Method

The study employs constructivist grounded theory, combining a survey, semi-structured interviews, and laddering interviews to iteratively build a theory of evaluation practices.

In practice

• Identify common AI code weaknesses.
• Use AI to critique or test code.
• Understand user expectations vs. tool capabilities.

Topics

• Generative AI
• Code Evaluation
• Software Engineering
• Grounded Theory
• GitHub Copilot
• Automation Bias

Best for: AI Scientist, Research Scientist, Software Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.SE updates on arXiv.org.