{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T19:21:04Z","timestamp":1778613664682,"version":"3.51.4"},"reference-count":66,"publisher":"Association for Computing Machinery (ACM)","issue":"FSE","license":[{"start":{"date-parts":[[2024,7,12]],"date-time":"2024-07-12T00:00:00Z","timestamp":1720742400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Research Grants Council of the Hong Kong Special Administrative Region, China","award":["14206921"],"award-info":[{"award-number":["14206921"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. ACM Softw. Eng."],"published-print":{"date-parts":[[2024,7,12]]},"abstract":"<jats:p>Logging practices have been extensively investigated to assist developers in writing appropriate logging statements for documenting software behaviors. Although numerous automatic logging approaches have been proposed, their performance remains unsatisfactory due to the constraint of the single-method input, without informative programming context outside the method. Specifically, we identify three inherent limitations with single-method context: limited static scope of logging statements, inconsistent logging styles, and missing type information of logging variables.<\/jats:p>\n                  <jats:p>\n                    To tackle these limitations, we propose\n                    <jats:monospace>SCLogger<\/jats:monospace>\n                    , the first contextualized logging statement generation approach with inter-method static contexts. First,\n                    <jats:monospace>SCLogger<\/jats:monospace>\n                    extracts inter-method contexts with static analysis to construct the\n                    <jats:italic toggle=\"yes\">contextualized prompt<\/jats:italic>\n                    for language models to generate a tentative logging statement. The contextualized prompt consists of an extended static scope and sampled similar methods, ordered by the chain-of-thought (COT) strategy. Second,\n                    <jats:monospace>SCLogger<\/jats:monospace>\n                    refines the access of logging variables by formulating a new\n                    <jats:italic toggle=\"yes\">refinement prompt<\/jats:italic>\n                    for language models, which incorporates detailed type information of variables in the tentative logging statement.\n                  <\/jats:p>\n                  <jats:p>\n                    The evaluation results show that\n                    <jats:monospace>SCLogger<\/jats:monospace>\n                    surpasses the state-of-the-art approach by 8.7% in logging position accuracy, 32.1% in level accuracy, 19.6% in variable precision, and 138.4% in text BLEU-4 score. Furthermore,\n                    <jats:monospace>SCLogger<\/jats:monospace>\n                    consistently boosts the performance of logging statement generation across a range of large language models, thereby showcasing the generalizability of this approach.\n                  <\/jats:p>\n                  <jats:p>\n                    CCS Concepts: \u2022\n                    <jats:bold>Software and its engineering \u2192 Maintaining software<\/jats:bold>\n                    .\n                  <\/jats:p>","DOI":"10.1145\/3643754","type":"journal-article","created":{"date-parts":[[2024,7,12]],"date-time":"2024-07-12T10:22:09Z","timestamp":1720779729000},"page":"609-630","source":"Crossref","is-referenced-by-count":17,"title":["Go Static: Contextualized Logging Statement Generation"],"prefix":"10.1145","volume":"1","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-8370-644X","authenticated-orcid":false,"given":"Yichen","family":"Li","sequence":"first","affiliation":[{"name":"Chinese University of Hong Kong, Hong Kong, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-8798-5667","authenticated-orcid":false,"given":"Yintong","family":"Huo","sequence":"additional","affiliation":[{"name":"Chinese University of Hong Kong, Hong Kong, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6626-4437","authenticated-orcid":false,"given":"Renyi","family":"Zhong","sequence":"additional","affiliation":[{"name":"Chinese University of Hong Kong, Hong Kong, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-1988-6219","authenticated-orcid":false,"given":"Zhihan","family":"Jiang","sequence":"additional","affiliation":[{"name":"Chinese University of Hong Kong, Hong Kong, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0037-1912","authenticated-orcid":false,"given":"Jinyang","family":"Liu","sequence":"additional","affiliation":[{"name":"Chinese University of Hong Kong, Hong Kong, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-6962-5292","authenticated-orcid":false,"given":"Junjie","family":"Huang","sequence":"additional","affiliation":[{"name":"Chinese University of Hong Kong, Hong Kong, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5831-9474","authenticated-orcid":false,"given":"Jiazhen","family":"Gu","sequence":"additional","affiliation":[{"name":"Chinese University of Hong Kong, Hong Kong, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3377-8129","authenticated-orcid":false,"given":"Pinjia","family":"He","sequence":"additional","affiliation":[{"name":"Chinese University of Hong Kong, Shen Zhen, Shenzhen, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3666-5798","authenticated-orcid":false,"given":"Michael R.","family":"Lyu","sequence":"additional","affiliation":[{"name":"Chinese University of Hong Kong, Hong Kong, China"}]}],"member":"320","published-online":{"date-parts":[[2024,7,12]]},"reference":[{"key":"e_1_3_1_2_1","unstructured":"Apache. 2023. log4j. https:\/\/logging.apache.org\/log4j\/2.x\/"},{"key":"e_1_3_1_3_1","first-page":"71","article-title":"Characterizing and detecting anti-patterns in the logging code","author":"Chen Boyuan","year":"2017","unstructured":"Boyuan Chen and Zhen Ming Jiang. 2017. 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