arXiv subjects

Junsong Pu

Publications and source records attributed to Junsong Pu.

5 recordsLinked to original sources

arXiv2026

AutoSQL: Extracting SQL Templates from Imperative ORM Code in Large-Scale Repositories

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%.

cs.SE↗

arXiv2026

MASPrism: Lightweight Failure Attribution for Multi-Agent Systems Using Prefill-Stage Signals

Failure attribution in LLM-based multi-agent systems aims to identify the steps that contribute to a failed execution. This task remains difficult because a single execution can contain many agent actions and tool calls, failure evidence can appear many steps after the original mistake, and existing methods often rely on costly agent workflows, replay, or training on synthetic failure logs. To address these challenges, we propose MASPrism, a lightweight framework that performs failure attribution using prefill-stage signals from a small language model (SLM). MASPrism first extracts token-level negative log-likelihood and attention weights during a prefill pass to identify symptom-like steps and earlier candidate sources, without decoding. It then reconstructs a focused diagnostic prompt and performs a second prefill pass to rank failure-source candidates. Using Qwen3-0.6B as the SLM, MASPrism achieves the best performance on three of the four evaluated subsets across Who&When and TRAIL, improving Top-1 accuracy on Who&When-HC by 33.41% over the best baseline. On TRAIL, MASPrism outperforms strong proprietary LLMs, including Gemini-2.5-Pro, with up to 89.50% relative improvement. MASPrism processes each trace in 2.66 seconds on average, achieving a 6.69$\times$ speedup over the single-pass prompting baseline, with zero output tokens. These results show that MASPrism provides an effective and practical framework for failure attribution in long multi-agent execution logs.

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arXiv2026

TypeScript Repository Indexing for Code Agent Retrieval

Graph-based code indexing can improve context retrieval for LLM-based code agents by preserving call chains and dependency relationships that keyword search and similarity retrieval often miss. ABCoder is an open-source framework that parses codebases into a function-level code index called UniAST. Its existing parsers combine lightweight AST parsers for syntactic analysis with language servers for semantic resolution, but because LSP-based resolution requires a JSON-RPC call for each symbol lookup, these per-symbol calls become a bottleneck on large TypeScript repositories. We present abcoder-ts-parser, a TypeScript parser built on the TypeScript Compiler API that works directly with the compiler's AST, semantic information, and module resolution logic. We evaluate the parser on three open-source TypeScript projects with up to 1.2 million lines of code and find that it produces reliable indexes significantly more efficiently than the existing architecture. For a live demonstration, watch: https://youtu.be/ryssr7ouvdE

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arXiv2025

ErrorPrism: Reconstructing Error Propagation Paths in Cloud Service Systems

Reliability management in cloud service systems is challenging due to the cascading effect of failures. Error wrapping, a practice prevalent in modern microservice development, enriches errors with context at each layer of the function call stack, constructing an error chain that describes a failure from its technical origin to its business impact. However, this also presents a significant traceability problem when recovering the complete error propagation path from the final log message back to its source. Existing approaches are ineffective at addressing this problem. To fill this gap, we present ErrorPrism in this work for automated reconstruction of error propagation paths in production microservice systems. ErrorPrism first performs static analysis on service code repositories to build a function call graph and map log strings to relevant candidate functions. This significantly reduces the path search space for subsequent analysis. Then, ErrorPrism employs an LLM agent to perform an iterative backward search to accurately reconstruct the complete, multi-hop error path. Evaluated on 67 production microservices at ByteDance, ErrorPrism achieves 97.0% accuracy in reconstructing paths for 102 real-world errors, outperforming existing static analysis and LLM-based approaches. ErrorPrism provides an effective and practical tool for root cause analysis in industrial microservice systems.

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arXiv2025

Tracezip: Efficient Distributed Tracing via Trace Compression

Distributed tracing serves as a fundamental building block in the monitoring and testing of cloud service systems. To reduce computational and storage overheads, the de facto practice is to capture fewer traces via sampling. However, existing work faces a trade-off between the completeness of tracing and system overhead. On one hand, head-based sampling indiscriminately selects requests to trace when they enter the system, which may miss critical events. On the other hand, tail-based sampling first captures all requests and then selectively persists the edge-case traces, which entails the overheads related to trace collection and ingestion. Taking a different path, we propose Tracezip in this paper to enhance the efficiency of distributed tracing via trace compression. Our key insight is that there exists significant redundancy among traces, which results in repetitive transmission of identical data between services and the backend. We design a new data structure named Span Retrieval Tree (SRT) that continuously encapsulates such redundancy at the service side and transforms trace spans into a lightweight form. At the backend, the complete traces can be seamlessly reconstructed by retrieving the common data that are already delivered by previous spans. Tracezip includes a series of strategies to optimize the structure of SRT and a differential update mechanism to efficiently synchronize SRT between services and the backend. Our evaluation on microservices benchmarks, popular cloud service systems, and production trace data demonstrates that Tracezip can achieve substantial performance gains in trace collection with negligible overhead. We have implemented Tracezip inside the OpenTelemetry Collector, making it compatible with existing tracing APIs.

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