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Methodol."],"published-print":{"date-parts":[[2025,11,30]]},"abstract":"<jats:p>\n            Error logs like Java exceptions play a crucial role in diagnosing and resolving errors within the industry. Nonetheless, the extensive logging of Java exceptions may result in\n            <jats:italic toggle=\"yes\">exception fatigue<\/jats:italic>\n            in large-scale Java systems at an industrial level, where the frequency of Java exceptions being generated surpasses developers\u2019 ability to manage them effectively. Regrettably, there is a lack of research on the seriousness, prevalence, and solutions to this problem. To close this gap, we first make a comprehensive investigation into the exception fatigue problem within a prominent Internet corporation in China, namely Alibaba, confirming its importance in the industry. Consequently, we introduce a novel solution called\n            <jats:sc>ABEL<\/jats:sc>\n            , designed to automatically pinpoint the most relevant exceptions associated with software failures. The key challenge lies in the randomness of exceptions, which prevents existing sequence-based techniques from being effective. To address this challenge,\n            <jats:sc>ABEL<\/jats:sc>\n            establishes correlations between Java exceptions and the Key Performance Indicator (KPI) of applications, enabling the identification of exceptions leading to irregularities in KPI. Our evaluation of\n            <jats:sc>ABEL<\/jats:sc>\n            across four Java applications and five business KPIs within Alibaba illustrates its capability to pinpoint the primary cause of exception logs with an AC@5 (top-5 accuracy) exceeding 90%, effectively mitigating the exception fatigue problem within Alibaba. Furthermore, it can identify the root-cause exceptions in a real software failure within just 4 minutes, outperforming the manual investigation process by over an hour.\n          <\/jats:p>","DOI":"10.1145\/3721126","type":"journal-article","created":{"date-parts":[[2025,2,28]],"date-time":"2025-02-28T10:31:29Z","timestamp":1740738689000},"page":"1-28","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Not All Exceptions Are Created Equal: Triaging Error Logs in Real-World Enterprises"],"prefix":"10.1145","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5917-2251","authenticated-orcid":false,"given":"Junlin","family":"Liu","sequence":"first","affiliation":[{"name":"Key Laboratory of High Confidence Software Technologies (PKU), Ministry of Education, Beijing, China and School of Computer Science, Peking University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-8220-3470","authenticated-orcid":false,"given":"Mengyu","family":"Yao","sequence":"additional","affiliation":[{"name":"Key Laboratory of High Confidence Software Technologies (PKU), Ministry of Education, Beijing, China and School of Computer Science, Peking University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-6530-5935","authenticated-orcid":false,"given":"Shaofei","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory of High Confidence Software Technologies (PKU), Ministry of Education, Beijing, China and School of Computer Science, Peking University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8156-3926","authenticated-orcid":false,"given":"Dingyu","family":"Yang","sequence":"additional","affiliation":[{"name":"The State Key Laboratory of Blockchain and Data Security, Zhejiang University, Hangzhou, China and Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-9613-3903","authenticated-orcid":false,"given":"Zheshun","family":"Wu","sequence":"additional","affiliation":[{"name":"Alibaba Group, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-9275-6020","authenticated-orcid":false,"given":"Xiaojun","family":"Qu","sequence":"additional","affiliation":[{"name":"Alibaba Group, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8493-0261","authenticated-orcid":false,"given":"Ziqi","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Illinois Urbana-Champaign, Urbana, IL, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7558-9137","authenticated-orcid":false,"given":"Ding","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory of High Confidence Software Technologies (PKU), Ministry of Education, Beijing, China and School of Computer Science, Peking University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5064-5286","authenticated-orcid":false,"given":"Yao","family":"Guo","sequence":"additional","affiliation":[{"name":"Key Laboratory of High Confidence Software Technologies (PKU), Ministry of Education, Beijing, China and School of Computer Science, Peking University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7366-5906","authenticated-orcid":false,"given":"Xiangqun","family":"Chen","sequence":"additional","affiliation":[{"name":"Key Laboratory of High Confidence Software Technologies (PKU), Ministry of Education, Beijing, China and School of Computer Science, Peking University, Beijing, China"}]}],"member":"320","published-online":{"date-parts":[[2025,10,4]]},"reference":[{"key":"e_1_3_3_2_2","unstructured":"Surveytown. 2023. 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