Exploring the Evidence-Based SE Beliefs of Generative AI Tools
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866916893299834880 |
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| author | Brown, Chris Cusati, Jason |
| author_facet | Brown, Chris Cusati, Jason |
| contents | Background: Recent innovations in generative artificial intelligence (AI) have transformed how programmers develop and maintain software. The advanced capabilities of generative AI tools in supporting development tasks have led to a rise in their adoption within software engineering (SE) workflows. However, little is known about how AI tools perceive evidence-based practices supported by empirical SE research. Aim: To this end, we explore the "beliefs" of generative AI tools increasingly used to support software development in practice. Method: We conduct a preliminary evaluation conceptually replicating prior work to investigate 17 evidence-based claims across five generative AI tools. Results: Our findings demonstrate generative AI tools have ambiguous beliefs regarding research claims and lack credible evidence to support responses. Conclusions: Based on our results, we provide implications for practitioners integrating generative AI-based systems into development contexts and shed light on future research directions to enhance the reliability and trustworthiness of generative AI -- aiming to increase awareness and adoption of evidence-based SE research findings in practice. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_13900 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Exploring the Evidence-Based SE Beliefs of Generative AI Tools Brown, Chris Cusati, Jason Software Engineering Background: Recent innovations in generative artificial intelligence (AI) have transformed how programmers develop and maintain software. The advanced capabilities of generative AI tools in supporting development tasks have led to a rise in their adoption within software engineering (SE) workflows. However, little is known about how AI tools perceive evidence-based practices supported by empirical SE research. Aim: To this end, we explore the "beliefs" of generative AI tools increasingly used to support software development in practice. Method: We conduct a preliminary evaluation conceptually replicating prior work to investigate 17 evidence-based claims across five generative AI tools. Results: Our findings demonstrate generative AI tools have ambiguous beliefs regarding research claims and lack credible evidence to support responses. Conclusions: Based on our results, we provide implications for practitioners integrating generative AI-based systems into development contexts and shed light on future research directions to enhance the reliability and trustworthiness of generative AI -- aiming to increase awareness and adoption of evidence-based SE research findings in practice. |
| title | Exploring the Evidence-Based SE Beliefs of Generative AI Tools |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2407.13900 |