Accelerating Software Development Using Generative AI: ChatGPT Case Study
Open accessAbstract
The Software Development Life Cycle (SDLC) comprises multiple phases, each requiring Subject Matter Experts (SMEs) with phase-specific skills. The efficacy and quality of deliverables of each phase are skill dependent. In recent times, Generative AI techniques, including Large-scale Language Models (LLMs) like GPT, have become significant players in software engineering. These models, trained on extensive text data, can offer valuable contributions to software development. Interacting with LLMs involves feeding prompts with the context information and guiding the generation of textual responses. The quality of the response is dependent on the quality of the prompt given. This paper proposes a systematic prompting approach based on meta-model concepts for SDLC phases. The approach is validated using ChatGPT for small but complex business application development. We share the approach and our experience, learnings, benefits obtained, and the challenges encountered while applying the approach using ChatGPT. Our experience indicates that Generative AI techniques, such as ChatGPT, have the potential to reduce the skills barrier and accelerate software development substantially.
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Rajbhoj et al. (2024) studied the development of a small business application and reported a 70.66% productivity gain, primarily due to ChatGPT's ability to generate structural code.
“Similarly, Rajbhoj et al. (2024) studied the development of a small business application and reported a 70.66% productivity gain, primarily due to ChatGPT's ability to generate structural code.”
From Developer Productivity With and Without GitHub Copilot: A Longitudinal Mixed-Methods Case Study (2026) · cites this paper as Rajbhoj et al. (2024)
Prior studies focus on short-term gains in controlled or semi-controlled settings, either by measuring task completion time or by simulating software projects.
“Instead, they focus on short-term gains in controlled or semi-controlled settings, either by measuring task completion time (Peng et al., 2023), or by simulating software projects to illustrate how GenAI tools can assist developers during different phases of the development process (Al-Ahmad et al., 2024; Rajbhoj et al., 2024).”
From Developer Productivity With and Without GitHub Copilot: A Longitudinal Mixed-Methods Case Study (2026) · cites this paper as Rajbhoj et al., 2024
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- Authors
- Rajbhoj, A., Somase, A., Kulkarni, P., & Kulkarni, V.
- Affiliations
- Tata Consultancy Services (India) (IN)
- Venue
- Proceedings of the 17th Innovations in Software Engineering Conference
- Publisher
- ACM
- Published
- 2024-02-22
- Pages
- 1-11
- OpenAlex
- W4391979605
- Cited by
- 50
- References
- 20
- Citations by year
- 2026: 9 · 2025: 34 · 2024: 7
- FWCI
- 19.04
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Innovations in Software Engineering Conference (ISEC) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Software Engineering Research
- Physical Sciences › Computer Science › Information Systems › Software Engineering Techniques and Practices
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Keywords
- generative AI in software development, ChatGPT, large language models, prompt engineering, software development lifecycle, meta-model, business application development, software engineering automation
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