Article
Causality-Guided Adaptive Interventional Debugging (AID)
Runtime nondeterminism in software systems causes intermittent failures that are difficult to reproduce and debug. Traditional statistical debugging detects correlations but cannot identify true root causes. This paper implements a Causality-Guided Adaptive Interventional Debugging (AID) system using Django to automatically identify root causes of intermittent software failures. The system analyzes execution logs containing runtime predicates from successful and failed executions, constructs an Approximate Causal Directed Acyclic Graph (AC-DAG), and applies group intervention strategy to iteratively prune non-causal predicates. Experimental evaluation on synthetic and real-world bug datasets shows the system identifies root causes with 92% accuracy while reducing debugging iterations by 65% compared to traditional statistical debugging approaches
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