A Large-scale Benchmark for Log Parsing

Log data is pivotal in activities like anomaly detection and failure diagnosis in the automated maintenance of software systems. Due to their unstructured format, log parsing is often required to transform them into a structured format for automated analysis. A variety of log parsers exist, making it vital to benchmark these tools to comprehend their features and performance. However, existing datasets for log parsing are limited in terms of scale and representativeness, posing challenges for studies that aim to evaluate or develop log parsers. This problem becomes more pronounced when these parsers are evaluated for production use. To address these issues, we introduce a new collection of large-scale annotated log datasets, named LogPub, which more accurately mirrors log data observed in real-world software systems. LogPub comprises 14 datasets, each averaging 3.6 million log lines. Utilizing LogPub, we re-evaluate 15 log parsers in a more rigorous and practical setting. We also propose a new evaluation metric to lessen the sensitivity of current metrics to imbalanced data distribution. Furthermore, we are the first to scrutinize the detailed performance of log parsers on logs that represent rare system events and offer comprehensive information for system troubleshooting. Parsing such logs accurately is vital yet challenging. We believe that our work could shed light on the design and evaluation of log parsers in more realistic settings, thereby facilitating their implementation in production systems

Bug Triaging with High Confidence Predictions

Correctly assigning bugs to the right developer or team, i.e., bug triaging, is a costly activity. A concerted effort at Ericsson has been done to adopt automated bug triaging to reduce development costs. We also perform a case study on Eclipse bug reports. In this work, we replicate the research approaches that have been widely used in the literature including FixerCache. We apply them on over 10k bug reports for 9 large products at Ericsson and 2 large Eclipse products containing 21 components. We find that a logistic regression classifier including simple textual and categorical attributes of the bug reports has the highest accuracy of 79.00% and 46% on Ericsson and Eclipse bug reports respectively. Ericsson’s bug reports often contain logs that have crash dumps and alarms. We add this information to the bug triage models. We find that this information does not improve the accuracy of bug triaging in Ericsson’s context. Eclipse bug reports contain the stack traces that we add to the bug triaging model. Stack traces are only present in 8% of bug reports and do not improve the triage accuracy. Although our models perform as well as the best ones reported in the literature, a criticism of bug triaging at Ericsson is that accuracy is not sufficient for regular use. We develop a novel approach that only triages bugs when the model has high confidence in the triage prediction. We find that we improve the accuracy to 90% at Ericsson and 70% at Eclipse, but we can make predictions for 62% and 25% of the total Ericsson and Eclipse bug reports,respectively