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We present\n                    <jats:sc>ComCat<\/jats:sc>\n                    , an approach to automate comment generation by augmenting Large Language Models (LLMs) with expertise-guided context to target the annotation of source code with comments that improve comprehension. Our approach enables the selection of the most relevant and informative comments for a given snippet or file containing source code. We develop the\n                    <jats:sc>ComCat<\/jats:sc>\n                    pipeline to comment C\/C++ files by (1) automatically identifying suitable locations in which to place comments, (2) predicting the most helpful type of comment for each location, and (3) generating a comment based on the selected location and comment type. In a human subject evaluation, we demonstrate that\n                    <jats:sc>ComCat<\/jats:sc>\n                    -generated comments significantly improve developer code comprehension across three indicative software engineering tasks by up to 13% for 80% of participants. In addition, we demonstrate that\n                    <jats:sc>ComCat<\/jats:sc>\n                    -generated comments are at least as accurate and readable as human-generated comments and are preferred over standard ChatGPT-generated comments for up to 92% of snippets of code. Furthermore, we develop and release a dataset containing source code snippets, human-written comments, and human-annotated comment categories.\n                    <jats:sc>ComCat<\/jats:sc>\n                    leverages LLMs to offer a significant improvement in code comprehension across a variety of human software engineering tasks.\n                  <\/jats:p>","DOI":"10.1145\/3742475","type":"journal-article","created":{"date-parts":[[2025,6,4]],"date-time":"2025-06-04T12:58:03Z","timestamp":1749041883000},"page":"1-30","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["<scp>ComCat<\/scp>\n                    : Expertise-Guided Context Generation to Enhance Code Comprehension"],"prefix":"10.1145","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-8051-1063","authenticated-orcid":false,"given":"Skyler","family":"Grandel","sequence":"first","affiliation":[{"name":"Computer Science, Vanderbilt University, Nashville, Tennessee, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-1225-6312","authenticated-orcid":false,"given":"Scott Thomas","family":"Andersen","sequence":"additional","affiliation":[{"name":"Computer Science, Universidad Nacional Aut\u00f3noma de M\u00e9xico, Ciudad de Mexico, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5885-5365","authenticated-orcid":false,"given":"Yu","family":"Huang","sequence":"additional","affiliation":[{"name":"Computer Science, Vanderbilt University, Nashville, Tennessee, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4001-3442","authenticated-orcid":false,"given":"Kevin","family":"Leach","sequence":"additional","affiliation":[{"name":"Computer Science, Vanderbilt University, Nashville, Tennessee, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2026,2,13]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICSM.2015.7332514"},{"key":"e_1_3_2_3_2","doi-asserted-by":"crossref","first-page":"590","DOI":"10.1145\/3377811.3380405","volume-title":"ACM\/IEEE 42nd International Conference on Software Engineering","author":"Aghajani Emad","year":"2020","unstructured":"Emad Aghajani, Csaba Nagy, Mario Linares-V\u00e1squez, Laura Moreno, Gabriele Bavota, Michele Lanza, and David C. 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