PELLI: Framework to effectively integrate LLMs for quality software generation

TL;DR

This paper introduces PELLI, a comprehensive framework for evaluating and integrating LLMs into software development by assessing multiple nonfunctional quality metrics across different domains.

Contribution

The paper presents PELLI, a novel iterative assessment framework that evaluates LLM-generated code on maintainability, performance, and reliability, extending prior work focused mainly on reliability.

Findings

GPT-4T and Gemini performed slightly better across metrics.

Prompt design significantly influences code quality.

Application domains show varied scores across metrics.

Abstract

Recent studies have revealed that when LLMs are appropriately prompted and configured, they demonstrate mixed results. Such results often meet or exceed the baseline performance. However, these comparisons have two primary issues. First, they mostly considered only reliability as a comparison metric and selected a few LLMs (such as Codex and ChatGPT) for comparision. This paper proposes a comprehensive code quality assessment framework called Programmatic Excellence via LLM Iteration (PELLI). PELLI is an iterative analysis-based process that upholds high-quality code changes. We extended the state-of-the-art by performing a comprehensive evaluation that generates quantitative metrics for analyzing three primary nonfunctional requirements (such as maintainability, performance, and reliability) while selecting five popular LLMs. For PELLI's applicability, we selected three application domains while following Python coding standards. Following this framework, practitioners can ensure harmonious integration between LLMs and human developers, ensuring that their potential is fully realized. PELLI can serve as a practical guide for developers aiming to leverage LLMs while adhering to recognized quality standards. This study's outcomes are crucial for advancing LLM technologies in real-world applications, providing stakeholders with a clear understanding of where these LLMs excel and where they require further refinement. Overall, based on three nonfunctional requirements, we have found that GPT-4T and Gemini performed slightly better. We also found that prompt design can influence the overall code quality. In addition, each application domain demonstrated high and low scores across various metrics, and even within the same metrics across different prompts.