Applied Intelligence and Computing
Analysis of LLM Code Synthesis in Software Productivity
Authors: Anurag Anand, Shivali Chopra and Mohit Arora
Publishing Date: 01-01-2025
ISBN: 978-81-955020-9-7
Abstract
The use of LLMs in code generation tools has introduced a paradigm shift in software development, streamlining the process and enhancing automation and efficiency. This study presents a comprehensive analysis of the applications and effectiveness of the Large Language Model (LLM) in code synthesis based upon the analysis of various models. The LLM techniques where programming codes are significantly constrained on high level and lowlevel programming paradigm, has emerged as a dominant strategy in software productivity due to its inherent ability to promote efficiency and minimize time to build logic. Our research systematically explores the impact of LLM on the performance outcomes on various programming languages, comparing it to traditional code practices. We analyze multiple case studies, quantitatively evaluating the success rates, efficiency, and problem-solving capacity of LLM-based solutions. Preliminary findings indicate that LLM encourages a unique problem-solving approach, despite its limitations, often results in highly efficient and innovative solutions. However, the technique also presents a steep learning curve that may deter novice programmers. This study aims to contribute to the body of knowledge on software productivity strategies and the continuing discourse on code efficiency and optimization.
Keywords
Code Generation, DevinAI, GPT-4, Code Llama, StarCoder, Tabnine, DS Code Generation, LLM Security, LLEMMA.
Cite as
Anurag Anand, Shivali Chopra and Mohit Arora, "Analysis of LLM Code Synthesis in Software Productivity", In: Mukesh Saraswat and Rajani Kumari (eds), Applied Intelligence and Computing, SCRS, India, 2025, pp. 247-259. https://doi.org/10.56155/978-81-955020-9-7-24