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Causality-Guided Adaptive Interventional Debugging

Summary: Adaptive Interventional Debugging (AID) for runtime nondeterminism in database applications fuses statistical debugging, causal analysis, fault injection, and group testing to pinpoint root causes and how they trigger failures. Using temporal predicates to bound causality and targeted interventions, AID converges faster than group testing and delivers precise causal explanations, validated on real and synthetic applications. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
5909
Venue
SIGMOD
Year
2020
Pagerank
4.4075011e-05
Overall Rank
9,024 | 37.23%
DOI
10.1145/3318464.3389694

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Incoming Citations (Sorted by Pagerank)

Showing 3 of 3 citing papers.

Rank Citing Paper Year Venue Pagerank
5,313 XInsight: eXplainable Data Analysis Through The Lens of Causality 2023 SIGMOD 5.573009e-05
6,944 DataPrism: Exposing Disconnect between Data and Systems 2022 SIGMOD 4.8912787e-05
10,954 Counterfactual Explanation at Will, with Zero Privacy Leakage 2024 SIGMOD 4.1945683e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 8 of 8 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

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