{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T19:21:02Z","timestamp":1778613662216,"version":"3.51.4"},"reference-count":95,"publisher":"Association for Computing Machinery (ACM)","issue":"1","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Softw. Eng. Methodol."],"published-print":{"date-parts":[[2026,1,31]]},"abstract":"<jats:p>Developers write logging statements to monitor software runtime behaviors and system state. However, poorly constructed or misleading log messages can inadvertently obfuscate actual program execution patterns, thereby impeding effective software maintenance. Existing research on analyzing issues within logging statements is limited, primarily focusing on detecting a singular type of defect and relying on manual intervention for fixes rather than automated solutions.<\/jats:p>\n                  <jats:p>\n                    To address the limitation, we initiate a systematic study that pinpoints four specific types of defects in logging statements (i.e., statement code inconsistency, static dynamic inconsistency, temporal relation inconsistency, and readability issues) through the analysis of real-world log-centric changes. We then propose\n                    <jats:sc>LogUpdater<\/jats:sc>\n                    , a two-stage framework for automatically detecting and updating logging statements for these specific defects. In the offline stage,\n                    <jats:sc>LogUpdater<\/jats:sc>\n                    constructs a similarity-based classifier on a set of synthetic defective logging statements to identify specific defect types. During the online testing phase, this classifier first evaluates logging statements in a given code snippet to determine the necessity and type of improvements required. Then,\n                    <jats:sc>LogUpdater<\/jats:sc>\n                    constructs type-aware prompts from historical logging update changes for an LLM-based recommendation framework to suggest updates addressing these specific defects.\n                  <\/jats:p>\n                  <jats:p>\n                    We evaluate the effectiveness of\n                    <jats:sc>LogUpdater<\/jats:sc>\n                    on a dataset containing real-world logging changes, a synthetic dataset, and a new real-world project dataset. The results indicate that our approach is highly effective in detecting logging defects, achieving an F1-score of 0.625. Additionally, it exhibits significant improvements in suggesting precise static text and dynamic variables, with enhancements of 48.12% and 24.90%, respectively. Furthermore,\n                    <jats:sc>LogUpdater<\/jats:sc>\n                    achieves a 61.49% success rate in recommending correct updates on new real-world projects. We reported 40 problematic logging statements and their fixes to GitHub via pull requests, resulting in 25 changes confirmed and merged across 11 different projects.\n                  <\/jats:p>","DOI":"10.1145\/3731754","type":"journal-article","created":{"date-parts":[[2025,4,25]],"date-time":"2025-04-25T13:23:23Z","timestamp":1745587403000},"page":"1-31","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["<scp>LogUpdater<\/scp>\n                    : Automated Detection and Repair of Specific Defects in Logging Statements"],"prefix":"10.1145","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6626-4437","authenticated-orcid":false,"given":"Renyi","family":"Zhong","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, Hong Kong"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-8370-644X","authenticated-orcid":false,"given":"Yichen","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, Hong Kong"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1987-2533","authenticated-orcid":false,"given":"Jinxi","family":"Kuang","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, Hong Kong"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1096-2732","authenticated-orcid":false,"given":"Wenwei","family":"Gu","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, Hong Kong"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-8798-5667","authenticated-orcid":false,"given":"Yintong","family":"Huo","sequence":"additional","affiliation":[{"name":"Singapore Management University, Singapore, Singapore"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3666-5798","authenticated-orcid":false,"given":"Michael R.","family":"Lyu","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, Hong Kong"}]}],"member":"320","published-online":{"date-parts":[[2025,12,11]]},"reference":[{"key":"e_1_3_2_2_2","unstructured":"AI@Meta. 2024. 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