Enhancing Trust in LLM-Generated Code Summaries with Calibrated Confidence Scores

A good summary can often be very useful during program comprehension. While a brief, fluent, and relevant summary can be helpful, it does require significant human effort to produce. Often, good summaries are unavailable in software projects, thus making maintenance more difficult. There has been a considerable body of research into automated AI-based methods, using Large Language models (LLMs), to generate summaries of code; there also has been quite a bit work on ways to measure the performance of such summarization methods, with special attention paid to how closely these AI-generated summaries resemble a summary a human might have produced. Measures such as BERTScore and BLEU have been suggested and evaluated with human-subject studies. However, LLM-produced summaries can be too long, irrelevant, etc: generally, too dissimilar to what a human might say. Given an LLM-produced code summary, how can we judge if a summary is good enough? Given some input source code, and an LLM-generated summary, existing approaches can help judge brevity, fluency and relevance; however, it's difficult to gauge whether an LLM-produced summary sufficiently resembles what a human might produce, without a "golden" human-produced summary to compare against. We study this resemblance question as a calibration problem: given just the summary from an LLM, can we compute a confidence measure, that provides a reliable indication of whether the summary sufficiently resembles what a human would have produced in this situation? We examine this question using several LLMs, for several languages, and in several different settings. Our investigation suggests approaches to provide reliable predictions of the likelihood that an LLM-generated summary would sufficiently resemble a summary a human might write for the same code.