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Harnessing the Potential of Gen-AI Coding Assistants in Public Sector Software Development

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arxiv 2409.17434 v1 pith:TW25LFM2 submitted 2024-09-25 cs.SE

classification cs.SE
keywords publicsectorcodecodingpotentialproductivitytoolscopilot
verification ladder T0 review T1 audit T2 compute T3 formal
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The study on GitHub Copilot by GovTech Singapore's Engineering Productivity Programme (EPP) reveals significant potential for AI Code Assistant tools to boost developer productivity and improve application quality in the public sector. Highlighting the substantial benefits for the public sector, the study observed an increased productivity (coding / tasks speed increased by 21-28%), which translates into accelerated development, and quicker go-to-market, with a notable consensus (95%) that the tool increases developer satisfaction. Particularly, junior developers experienced considerable efficiency gains and reduced coding times, illustrating Copilot's capability to enhance job satisfaction by easing routine tasks. This advancement allows for a sharper focus on complex projects, faster learning, and improved code quality. Recognising the strategic importance of these tools, the study recommends the development of an AI Framework to maximise such benefits while cautioning against potential over-reliance without solid foundational programming skills. It also advises public sector developers to classify their code as "Open" to use Gen-AI Coding Assistant tools on the Cloud like GitHub Copilot and to consider self-hosted tools like Codeium or Code Llama for confidential code to leverage technology efficiently within the public sector framework. With up to 8,000 developers, comprising both public officers and vendors developing applications for the public sector and its customers, there is significant potential to enhance productivity.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Do These Violent Delights Have Violent Ends? Measuring the Post-Merge Fate of Agentic Code

    cs.SE 2026-07 conditional novelty 6.5 of 10

    Post-merge, agentic code needs ~46–51% more corrective/bug-fix maintenance and introduces more security and dependency findings than human code, with higher burden in low-review projects.

  2. The Effects of GitHub Copilot on Computing Students' Programming Effectiveness, Efficiency, and Processes in Brownfield Programming Tasks

    cs.SE 2025-06 conditional novelty 6.0 of 10

    GitHub Copilot made undergraduate students faster and more test-successful on brownfield programming tasks, and shifted their workflow from manual coding and web search to prompting, reviewing, and integrating AI suggestions.

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