Samuel A. Ajila

Ali Mohammadi Esfahani, Nafiseh Kahani, Samuel A. Ajila

This study investigates the reliability of code generation by Large Language Models (LLMs), focusing on identifying and analyzing defects in the generated code. Despite the advanced capabilities of LLMs in automating code generation, ensuring the accuracy and functionality of the output remains a significant challenge. By using a structured defect classification method to understand their nature and origins this study categorizes and analyzes 367 identified defects from code snippets generated by LLMs, with a significant proportion being functionality and algorithm errors. These error categories indicate key areas where LLMs frequently fail, underscoring the need for targeted improvements. To enhance the accuracy of code generation, this paper implemented five prompt engineering techniques, including Scratchpad Prompting, Program of Thoughts Prompting, Chain-of-Thought Prompting, Chain of Code Prompting, and Structured Chain-of-Thought Prompting. These techniques were applied to refine the input prompts, aiming to reduce ambiguities and improve the models' accuracy rate. The research findings suggest that precise and structured prompting significantly mitigates common defects, thereby increasing the reliability of LLM-generated code.

Ali Mohammadi Esfahani, Nafiseh Kahani, Samuel A. Ajila

Large Language Models (LLMs) can generate code from natural language, but their performance is highly sensitive to prompt formulation. We propose a reinforcement-learning-based framework that models prompt refinement as a sequential decision-making problem. A Proximal Policy Optimization (PPO) agent iteratively improves prompts using a hybrid action space that combines direct generation, genetic lexical mutation and semantic rewriting, guided by shaped rewards derived from unit-test feedback. We evaluate the framework on MBPP+, HumanEval+, and APPS using CodeT5+, CodeLLaMA, and DeepSeek-Coder as frozen code generators. On the 500-task MBPP+ test set, the PPO agent achieves strict Pass@1 scores of 57.58%, 64.80%, and 85.50%, respectively, outperforming EPiC, Reflexion, and Random-Hybrid. Soft-Pass@1 reaches 67.90%, 73.10%, and 88.20%, respectively. Similar improvements are observed on HumanEval+ and APPS across all backbone models. The results demonstrate that reinforcement learning with shaped test-driven rewards improves functional correctness in LLM-based code generation.