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The Impact of Generative AI-Powered Code Generation Tools on Software Engineer Hiring: Recruiters' Experiences, Perceptions, and Strategies

Alyssia Chen, Timothy Huo, Yunhee Nam, Dan Port, Anthony Peruma

TL;DR

This study aims to explore recruiters' experiences and perceptions regarding GenAI-powered code generation tools, as well as their challenges and strategies for evaluating candidates.

Abstract

The rapid advancements in Generative AI (GenAI) tools, such as ChatGPT and GitHub Copilot, are transforming software engineering by automating code generation tasks. While these tools improve developer productivity, they also present challenges for organizations and hiring professionals in evaluating software engineering candidates' true abilities and potential. Although there is existing research on these tools in both industry and academia, there is a lack of research on how these tools specifically affect the hiring process. Therefore, this study aims to explore recruiters' experiences and perceptions regarding GenAI-powered code generation tools, as well as their challenges and strategies for evaluating candidates. Findings from our survey of 32 industry professionals indicate that although most participants are familiar with such tools, the majority of organizations have not adjusted their candidate evaluation methods to account for candidates' use/knowledge of these tools. There are mixed opinions on whether candidates should be allowed to use these tools during interviews, with many participants valuing candidates who can effectively demonstrate their skills in using these tools. Additionally, most participants believe that it is important to incorporate GenAI-powered code generation tools into computer science curricula and mention the key risks and benefits of doing so.

The Impact of Generative AI-Powered Code Generation Tools on Software Engineer Hiring: Recruiters' Experiences, Perceptions, and Strategies

TL;DR

This study aims to explore recruiters' experiences and perceptions regarding GenAI-powered code generation tools, as well as their challenges and strategies for evaluating candidates.

Abstract

The rapid advancements in Generative AI (GenAI) tools, such as ChatGPT and GitHub Copilot, are transforming software engineering by automating code generation tasks. While these tools improve developer productivity, they also present challenges for organizations and hiring professionals in evaluating software engineering candidates' true abilities and potential. Although there is existing research on these tools in both industry and academia, there is a lack of research on how these tools specifically affect the hiring process. Therefore, this study aims to explore recruiters' experiences and perceptions regarding GenAI-powered code generation tools, as well as their challenges and strategies for evaluating candidates. Findings from our survey of 32 industry professionals indicate that although most participants are familiar with such tools, the majority of organizations have not adjusted their candidate evaluation methods to account for candidates' use/knowledge of these tools. There are mixed opinions on whether candidates should be allowed to use these tools during interviews, with many participants valuing candidates who can effectively demonstrate their skills in using these tools. Additionally, most participants believe that it is important to incorporate GenAI-powered code generation tools into computer science curricula and mention the key risks and benefits of doing so.
Paper Structure (12 sections, 5 figures, 2 tables)

This paper contains 12 sections, 5 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Goal & Research Questions
  3. Contribution
  4. Related Work
  5. Method
  6. Survey Design
  7. Survey Participants
  8. Data Analysis
  9. Results
  10. Discussion & Takeaways
  11. Threats To Validity
  12. Conclusion & Future Work

Figures (5)

  • Figure 1: Frequency of AI code generation tool usage for work
  • Figure 2: Perceived challenges in assessing candidate skills with AI code generation tools
  • Figure 3: Frequency of directly asking a candidate about their personal experience and skills in using AI code generation tools
  • Figure 4: Extent of preference given to candidates who demonstrate skills in using AI code generation tools
  • Figure 5: Importance of integrating AI code generation tools into computer science curricula.