Logs are essential for system monitoring and failure diagnoses in modern software systems, yet querying them through natural language remains an open challenge. Existing approaches either treat logs as plain text, generate queries for schema-light backends, or assume predefined relational schemas, but none addresses a fundamental obstacle: raw logs carry no executable schema over which structured queries can be defined and run. To address these limitations, we present LogNLQ, a framework that formulates natural-language log querying as executable SQL generation over parser-induced and semantically grounded schemas. LogNLQ parses raw logs into template-partitioned relational tables, then applies dual-granularity semantic grounding to annotate both templates and parameter columns with interpretable names and descriptions. At query time, relevant schema candidates are retrieved via semantic search, and a large language model (LLM) generates executable SQL constrained to the retrieved context. To support rigorous evaluation, we introduce LogNLQ-Bench, an execution-verified benchmark of 8,895 queries over four real-world log datasets. Experimental results demonstrate that LogNLQ consistently outperforms all representative baselines by wide margins, with especially pronounced gains on analytically complex scenario queries.
Jue Wang, Jinyang Liu, Zhuangbin Chen et al.· 0 citations
Suboptimal SQL queries can significantly degrade the performance of cloud systems, motivating the extraction and auditing of SQL statements before deployment. However, Go ORM frameworks construct SQL imperatively through scattered method-call sequences, making it difficult to statically recover the resulting SQL templates. We present AutoSQL, a system that reconstructs SQL templates from Go ORM code. AutoSQL constructs a Code Index, a directed graph that captures structural dependencies between functions, types, and global variables as navigable edges. It then traces upstream call chains from ORM invocation sites to identify database-interacting functions as entry points. For each entry point, an LLM agent traverses the Code Index to collect code slices that influence SQL generation, switching to pattern-based search when the graph cannot resolve a retrieval goal. We call this strategy Hybrid Context Retrieval. Once sufficient context is collected, the agent synthesizes SQL templates. Evaluation on a benchmark of 579 test-covered entry points and 1,186 runtime-traced SQL statements from five large-scale Go repositories shows that AutoSQL achieves 68.04% to 72.18% recall, exceeding the static reachability baseline by 11.80% to 15.94% and outperforming existing methods by 8.52% to 21.50%.
Junsong Pu, Yichen Li, Zhuangbin Chen et al.· 0 citations