Pisco: An Isolation Bug Case Reduction and Deduplication Framework

Practical implementations of Isolation levels (ILs) might deviate from their theoretical definitions, resulting in isolation bugs. Triggering isolation bugs usually requires concurrent test cases, which causes high debugging complexity. These test cases typically contain numerous irrelevant operations, resulting in large and redundant raw cases that violate the three essential criteria for bug report submission, i.e., reproducibility, conciseness , and uniqueness. Achieving these criteria involves three key challenges: 1) deterministically reproduce bugs despite high concurrency; 2) efficiently reduce cases without losing bug-triggering operations; 3) accurately distinguish unique bugs from the bug reports having complex transaction behaviors. To address these challenges, we propose an isolation bug case reduction and deduplication framework Pisco. First, we propose to simulate the DBMS's internal state to infer the order of conflicting operations for deterministic bug reproduction. Second, we introduce a dependency-aware divide-and-conquer strategy for efficient case reduction. Finally, we design a domain knowledge-driven, multi-agent collaboration framework for accurate bug deduplication. Extensive experiments show that Pisco reduces the cases to their minimal forms in just 20.0%/33.3% of the time required by C-Reduce / DDMin and has a deduplication ratio of up to 91.6%.