Impact of Comments on LLM Comprehension of Legacy Code
This addresses a gap in software engineering for developers and maintainers dealing with legacy systems, but it is incremental as it applies an existing evaluation method to a new domain.
The paper tackled the problem of evaluating large language models' (LLMs) comprehension of legacy code, particularly focusing on how comment prevalence and inaccuracies affect it, using multiple-choice question answering (MCQA) as an evaluation method, but no concrete results or numbers are reported.
Large language models (LLMs) have been increasingly integrated into software engineering and maintenance tasks due to their high performance with software engineering tasks and robust understanding of modern programming languages. However, the ability of LLMs to comprehend code written with legacy languages remains a research gap challenged by real-world legacy systems lacking or containing inaccurate documentation that may impact LLM comprehension. To assess LLM comprehension of legacy languages, there is a need for objective LLM evaluation. In order to objectively measure LLM comprehension of legacy languages, we need an efficient, quantitative evaluation method. We leverage multiple-choice question answering (MCQA), an emerging LLM evaluation methodology, to evaluate LLM comprehension of legacy code and the impact of comment prevalence and inaccurate comments. In this work, we present preliminary findings on the impact of documentation on LLM comprehension of legacy code and outline strategic objectives for future work.