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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.10822 |
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| _version_ | 1866912482790998016 |
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| author | Elsisi, Omar Melo, Glaucia |
| author_facet | Elsisi, Omar Melo, Glaucia |
| contents | Conversational agents, such as chatbots and virtual assistants, have become essential in software development, boosting productivity, collaboration, and automating various tasks. This paper examines the role of adaptive AI-powered conversational agents in software development, highlighting their ability to offer dynamic, context-aware assistance to developers. Unlike traditional rule-based systems, adaptive AI agents use machine learning and natural language processing to learn from interactions and improve over time, providing more personalized and responsive help. We look at how these tools have evolved from simple query-based systems to advanced AI-driven solutions like GitHub Copilot and Microsoft Teams bots. We also explore the challenges of integrating adaptive AI into software development processes. The study aims to assess the benefits and limitations of these systems, address concerns like data privacy and ethical issues, and offer insights into their future use in the field. Ultimately, adaptive AI chatbots have great potential to revolutionize software development by delivering real-time, customized support and enhancing the efficiency of development cycles. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_10822 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Past, Present and Future: Exploring Adaptive AI in Software Development Bots Elsisi, Omar Melo, Glaucia Software Engineering Artificial Intelligence Conversational agents, such as chatbots and virtual assistants, have become essential in software development, boosting productivity, collaboration, and automating various tasks. This paper examines the role of adaptive AI-powered conversational agents in software development, highlighting their ability to offer dynamic, context-aware assistance to developers. Unlike traditional rule-based systems, adaptive AI agents use machine learning and natural language processing to learn from interactions and improve over time, providing more personalized and responsive help. We look at how these tools have evolved from simple query-based systems to advanced AI-driven solutions like GitHub Copilot and Microsoft Teams bots. We also explore the challenges of integrating adaptive AI into software development processes. The study aims to assess the benefits and limitations of these systems, address concerns like data privacy and ethical issues, and offer insights into their future use in the field. Ultimately, adaptive AI chatbots have great potential to revolutionize software development by delivering real-time, customized support and enhancing the efficiency of development cycles. |
| title | Past, Present and Future: Exploring Adaptive AI in Software Development Bots |
| topic | Software Engineering Artificial Intelligence |
| url | https://arxiv.org/abs/2507.10822 |