Software logs play a vital role in ensuring the reliability and availability of large-scale software systems. In recent years, researchers have made significant efforts to build log analysis approaches to manage software systems. However, these approaches focus on log compression, log parsing and log anomaly detection. In the current context, engineers continue to spend substantial time and effort on resolving errors once anomalous logs have been detected. To achieve truly automated software system management and high-level Artificial Intelligence for IT Operations (AIOps), it’s necessary to bridge the gap between anomalous logs and their resolutions.

In this paper, we propose a novel framework LogExpert to automatically generate recommended resolutions for anomalous logs. Specifically, we build a log recognizer to utilize the wealth of software knowledge in technical forums such as Stack Overflow (SO). In addition, LogExpert combines the great power of a Large Language Model (LLM) with domain-specific knowledge to generate the resolution. We conducted a preliminary evaluation of our framework on datasets from SO. Our log recognizer achieves the F1 score of 0.936. Our lexical metrics and human evaluation show the overall LogExpert framework achieves excellent performance in log-based resolution generation. \footnote{Our code and datasets are available in the supplemental material.}