How Execution Features Relate to Failures: An Empirical Study and Diagnosis Approach

Fault localization aims to identify code regions responsible for failures. Traditional techniques primarily correlate statement execution with failures; however, program behavior involves diverse execution features, including variable values, branch conditions, and definition-use pairs, which can provide richer diagnostic insights. This paper comprehensively investigates execution features for fault understanding, addressing two complementary goals. First, we conduct an empirical study of 310 bugs across 20 projects, analyzing 17 execution features and assessing their correlation with failure outcomes. Our findings suggest that fault localization benefits from a broader range of execution features: (1) Scalar pairs exhibit the strongest correlation with failures; (2) Beyond line executions, def-use pairs and functions executed are key indicators for fault localization; and (3) Combining multiple features enhances effectiveness compared to relying on individual features. Second, building on these insights, we introduce a debugging approach that learns relevant features from labeled test outcomes. The approach extracts fine-grained execution features and trains a decision tree to differentiate passing and failing runs. The trained model generates fault diagnoses that explain the underlying causes of failures. Our evaluation demonstrates that the generated diagnoses achieve high predictive accuracy. These interpretable diagnoses empower developers to debug software efficiently by providing deeper insights into failures.