The Impact of AI Tool on Engineering at ANZ Bank An Empirical Study on GitHub Copilot within Corporate Environment
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arXiv
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| Format: | Preprint |
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2024
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| author | Chatterjee, Sayan Liu, Ching Louis Rowland, Gareth Hogarth, Tim |
| author_facet | Chatterjee, Sayan Liu, Ching Louis Rowland, Gareth Hogarth, Tim |
| contents | The increasing popularity of AI, particularly Large Language Models (LLMs), has significantly impacted various domains, including Software Engineering. This study explores the integration of AI tools in software engineering practices within a large organization. We focus on ANZ Bank, which employs over 5000 engineers covering all aspects of the software development life cycle. This paper details an experiment conducted using GitHub Copilot, a notable AI tool, within a controlled environment to evaluate its effectiveness in real-world engineering tasks. Additionally, this paper shares initial findings on the productivity improvements observed after GitHub Copilot was adopted on a large scale, with about 1000 engineers using it. ANZ Bank's six-week experiment with GitHub Copilot included two weeks of preparation and four weeks of active testing. The study evaluated participant sentiment and the tool's impact on productivity, code quality, and security. Initially, participants used GitHub Copilot for proposed use-cases, with their feedback gathered through regular surveys. In the second phase, they were divided into Control and Copilot groups, each tackling the same Python challenges, and their experiences were again surveyed. Results showed a notable boost in productivity and code quality with GitHub Copilot, though its impact on code security remained inconclusive. Participant responses were overall positive, confirming GitHub Copilot's effectiveness in large-scale software engineering environments. Early data from 1000 engineers also indicated a significant increase in productivity and job satisfaction. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_05636 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | The Impact of AI Tool on Engineering at ANZ Bank An Empirical Study on GitHub Copilot within Corporate Environment Chatterjee, Sayan Liu, Ching Louis Rowland, Gareth Hogarth, Tim Software Engineering Artificial Intelligence The increasing popularity of AI, particularly Large Language Models (LLMs), has significantly impacted various domains, including Software Engineering. This study explores the integration of AI tools in software engineering practices within a large organization. We focus on ANZ Bank, which employs over 5000 engineers covering all aspects of the software development life cycle. This paper details an experiment conducted using GitHub Copilot, a notable AI tool, within a controlled environment to evaluate its effectiveness in real-world engineering tasks. Additionally, this paper shares initial findings on the productivity improvements observed after GitHub Copilot was adopted on a large scale, with about 1000 engineers using it. ANZ Bank's six-week experiment with GitHub Copilot included two weeks of preparation and four weeks of active testing. The study evaluated participant sentiment and the tool's impact on productivity, code quality, and security. Initially, participants used GitHub Copilot for proposed use-cases, with their feedback gathered through regular surveys. In the second phase, they were divided into Control and Copilot groups, each tackling the same Python challenges, and their experiences were again surveyed. Results showed a notable boost in productivity and code quality with GitHub Copilot, though its impact on code security remained inconclusive. Participant responses were overall positive, confirming GitHub Copilot's effectiveness in large-scale software engineering environments. Early data from 1000 engineers also indicated a significant increase in productivity and job satisfaction. |
| title | The Impact of AI Tool on Engineering at ANZ Bank An Empirical Study on GitHub Copilot within Corporate Environment |
| topic | Software Engineering Artificial Intelligence |
| url | https://arxiv.org/abs/2402.05636 |