A Data-Driven Methodology for Quality Aware Code Fixing

In today’s rapidly changing software development landscape, ensuring code quality is essential to reliability, maintainability, andsecurity among other aspects. Identifying code quality issues can be tackled; however, implementing code quality improvementscan be a complex and time-consuming task. To address this problem, we present a novel methodology designed to assist developersby suggesting alternative code snippets that not only match the functionality of the original code but also improve its quality basedon predefined metrics. Our system is based on a language-agnostic approach that allows the analysis of code snippets written indifferent programming languages. It employs advanced techniques to assess functional similarity and evaluates syntactic similarity,suggesting alternatives that minimize the need for extensive modification. The evaluation of our system on multiple axes demon-strates the effectiveness of our approach in providing usable code alternatives that are both functionally equivalent and syntacticallysimilar to the original snippets, while significantly improving quality metrics. We argue that our methodology and tool can bevaluable for the software engineering community, bridging the gap between the identification of code quality problems and theimplementation of practical solutions that improve software quality.