Syntax-guided program synthesis relies on domain-specific languages (DSLs) to constrain the search space and improve efficiency. However, manually designing optimal DSLs is challenging and often results in suboptimal performance. In this paper, we propose AMaze , a novel framework that automatically optimizes DSLs to accelerate synthesis. AMaze iteratively refines a DSL by identifying key program fragments, termed feature components, whose enumeration ranks correlate with synthesis time. Using a dynamic-programming-based algorithm to calculate enumeration ranks of feature components and a machine learning model based on them, AMaze estimates synthesis cost instead of directly invoking the synthesizer, which is impractical due to high computational cost. We evaluate AMaze on state-of-the-art synthesizers, including DryadSynth , Duet , Polygen , and EUsolver , across multiple domains. Empirical results demonstrate that AMaze achieves up to 4.35× speedup, effectively reducing synthesis time while maintaining expressiveness.

Testing-based fault localization has been a research focus in software engineering in the past decades. It localizes faulty program elements based on a set of passing and failing test executions. Since whether a fault could be triggered and detected by a test is related to program semantics, it is crucial to model program semantics in fault localization approaches. Existing approaches either consider the full semantics of the program (e.g., mutation-based fault localization and angelic debugging), leading to scalability issues, or ignore the semantics of the program (e.g., spectrum-based fault localization), leading to imprecise localization results. Our key idea is: by modeling only the correctness of program values but not their full semantics, a balance could be reached between effectiveness and scalability. To realize this idea, we introduce a probabilistic model by efficient approximation of program semantics and several techniques to address scalability challenges. Our approach, SmartFL (SeMantics bAsed pRobabilisTic Fault Localization), is evaluated on a real-world dataset, Defects4J 2.0. The top-1 statementlevel accuracy of our approach is 14%, which improves 130% over the best SBFL and MBFL methods. The average time cost is 205 seconds per fault, which is half of SBFL methods. After combining our approach with existing approaches using the CombineFL framework, the performance of the combined approach is significantly boosted by an average of 10% on top-1, top-3, and top-5 accuracy compared to state-of-the-art combination methods.

Testing-based fault localization has been a significant topic in software engineering in the past decades. It localizes a faulty program element based on a set of passing and failing test executions. Since whether a fault could be triggered and detected by a test is related to program semantics, it is crucial to model program semantics in fault localization approaches. Existing approaches either consider the full semantics of the program (e.g., mutation-based fault localization and angelic debugging), leading to scalability issues, or ignore the semantics of the program (e.g., spectrum-based fault localization), leading to imprecise localization results. Our key idea is: by modeling only the correctness of program values but not their full semantics, a balance could be reached between effectiveness and scalability. To realize this idea, we introduce a probabilistic approach to model program semantics and utilize information from static analysis and dynamic execution traces in our modeling. Our approach, SmartFL (SeMantics bAsed pRobabilisTic Fault Localization), is evaluated on a real-world dataset, Defects4J. The top-1 statement-level accuracy of our approach is 21%, which is the best among state-of-the-art methods. The average time cost is 210 seconds per fault while existing methods that capture full semantics are often 10x or more slower.