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Toward Effective AI Support for Developers: A survey of desires and concerns

Authors:

Mansi Khemka,

Brian HouckAuthors Info & Claims

Queue, Volume 22, Issue 3

Pages 53 - 78

https://doi.org/10.1145/3675416

Published: 10 July 2024 Publication History

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Abstract

The journey of integrating AI into the daily lives of software engineers is not without its challenges. Yet, it promises a transformative shift in how developers can translate their creative visions into tangible solutions. As we have seen, AI tools such as GitHub Copilot are already reshaping the code-writing experience, enabling developers to be more productive and to spend more time on creative and complex tasks. The skepticism around AI, from concerns about job security to its real-world efficacy, underscores the need for a balanced approach that prioritizes transparency, education, and ethical considerations. With these efforts, AI has the potential not only to alleviate the burdens of mundane tasks, but also to unlock new horizons of innovation and growth.

References

[1]

Barke, S., James, M. B., Polikarpova, N. 2023. Grounded Copilot: how programmers interact with code-generating models. Proceedings of the ACM on Programming Languages, 7(OOPSLA1), Article 78, 85?111; https://dl.acm.org/doi/abs/10.1145/3586030.

Digital Library

Google Scholar

[2]

Beller, M., Orgovan, S., Buja, S., Zimmermann, T. 2020. Mind the gap: on the relationship between automatically measured and self-reported productivity. IEEE Software 38(5); https://ieeexplore.ieee.org/document/9311217.

Google Scholar

[3]

Finnie-Ansley, J., Denny, P., Becker, B. A., Luxton-Reilly, A., Prather, J. 2022. The robots are coming: exploring the implications of OpenAI Codex on introductory programming. In Proceedings of the 24th Australasian Computing Education Conference, 10?19; https://dl.acm.org/doi/abs/10.1145/3511861.3511863.

Digital Library

Google Scholar

[4]

Forsgren, N., Storey, M. A., Maddila, C., Zimmermann, T., Houck, B., Butler, J. 2021. The SPACE of developer productivity: there's more to it than you think. acmqueue 19(1), 20?48; https://queue.acm.org/detail.cfm?id=3454124.

Google Scholar

[5]

Gradi&scaron;nik, M., Beranič, T., Karakatič, S. 2020. Impact of historical software metric changes in predicting future maintainability trends in open-source software development. Applied Sciences 10(13), 4624; https://www.mdpi.com/2076-3417/10/13/4624.

Google Scholar

[6]

Meyer, A., Barr, E., Bird, C., Zimmermann, T. 2019. Today was a good day: the daily life of software developers. IEEE Transactions on Software Engineering 47(5); https://ieeexplore.ieee.org/document/8666786.

Google Scholar

[7]

Microsoft AI. Empowering responsible AI practices. 2024; https://www.microsoft.com/en-us/ai/responsible-ai.

Google Scholar

[8]

Mozannar, H., Bansal, G., Fourney, A., Horvitz, E. 2022. Reading between the lines: modeling user behavior and costs in AI-assisted programming. arXiv:2210.14306; https://arxiv.org/abs/2210.14306.

Google Scholar

[9]

Peng, S., Kalliamvakou, E., Cihon, P., Demirer, M. 2023. The impact of AI on developer productivity: evidence from GitHub Copilot. arXiv:2302.06590; https://arxiv.org/abs/2302.06590.

Google Scholar

[10]

Ribeiro, M. T., Singh, S., Guestrin, C. 2016. Why should I trust you? Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1135?1144; https://dl.acm.org/doi/10.1145/2939672.2939778.

Digital Library

Google Scholar

[11]

Vaithilingam, P., Zhang, T., Glassman, E. L. 2022. Expectation vs. experience: evaluating the usability of code generation tools powered by large language models. In Extended Abstracts of the Conference on Human Factors in Computing, 1?7; https://dl.acm.org/doi/10.1145/3491101.3519665.

Digital Library

Google Scholar

[12]

Ziegler, A., Kalliamvakou, E., Li, X. A., Rice, A., Rifkin, D., Simister, S., Sittampalam, G., Aftandilian, E. 2022. Productivity assessment of neural code completion. In Proceedings of the 6th ACM SIGPLAN International Symposium on Machine Programming, 21?29; https://dl.acm.org/doi/10.1145/3520312.3534864.

Digital Library

Google Scholar

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López-Meneses EMellado-Moreno PGallardo Herrerías CPelícano-Piris N(2025)Educational Data Mining and Predictive Modeling in the Age of Artificial Intelligence: An In-Depth Analysis of Research DynamicsComputers10.3390/computers1402006814:2(68)Online publication date: 14-Feb-2025
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Toward Effective AI Support for Developers: A survey of desires and concerns

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Queue Volume 22, Issue 3

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May/June 2024

98 pages

EISSN:1542-7749

DOI:10.1145/3676308

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Association for Computing Machinery

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Publication History

Published: 10 July 2024

Published in QUEUE Volume 22, Issue 3

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López-Meneses EMellado-Moreno PGallardo Herrerías CPelícano-Piris N(2025)Educational Data Mining and Predictive Modeling in the Age of Artificial Intelligence: An In-Depth Analysis of Research DynamicsComputers10.3390/computers1402006814:2(68)Online publication date: 14-Feb-2025
https://doi.org/10.3390/computers14020068
Kapadia KRuwali SMalav TSeshadri SSeidmann APress DKolachalama V(2024)Enhancing Efficiency with an AI-Augmented Clinician in NeurologyAging and disease10.14336/AD.2024.1249(0)Online publication date: 2024
https://doi.org/10.14336/AD.2024.1249

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Cited By

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Recommendations

The SPACE of Developer Productivity: There's more to it than you think.

DevEx in Action: A study of its tangible impacts

Productivity assessment of neural code completion

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