Log Parsing using Semantic Filtering based Prompt Learning

Log parsing is the initial stage of automated log analysis, which mainly involves converting semi-structured log messages into structured log templates. However, the initial log data size of software systems and services is relatively small, which limits the effectiveness of existing $\log$ parsers. Traditional data-driven log parsers use the structured nature of logs to construct domain rules, which may make it difficult to maintain stable performance when dealing with log data of different sizes and types due to mismatches. To address the limitations of existing methods, this paper proposes a log parsing method called LogSPL, which captures high-quality samples from small-scale log data based on semantics and extracts log templates using prompt learning. The research in this paper performs extensive experiments on 16 benchmark $\log$ datasets. The results show that LogSPL improves parsing accuracy and group accuracy by 3.2% and 2.6% over the optimal parser.