Year: 2025
Type: article
Abstract: 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 exception fatigue in large-scale Java systems at an industrial level, where the frequency of Java exceptions being generated surpasses developers’ 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 ABEL , 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, ABEL 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 ABEL 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.
DOI: 10.1145/3721126
Authors: Junlin Liu, Mengyu Yao, Shaofei Li, Dingyu Yang, Zheshun Wu, Xiaojun Qu, Ziqi Zhang, Ding Li, Yao Guo, Xiangqun Chen
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
Awards: G1115618114, G2525199997, G5196063757
OpenAlex ID: W4408053971
