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Reusable symbolic evaluators are a key building block of solver-aided verification and synthesis tools. A reusable evaluator reduces the semantics of all paths in a program to logical constraints, and a client tool uses these constraints to formulate a satisfiability query that is discharged with SAT or SMT solvers. The correctness of the evaluator is critical to the soundness of the tool and the domain properties it aims to guarantee. Yet so far, the trust in these evaluators has been based on an ad-hoc foundation of testing and manual reasoning. This paper presents the first formal framework for reasoning about the behavior of reusable symbolic evaluators. We develop a new symbolic semantics for these evaluators that incorporates state merging. Symbolic evaluators use state merging to avoid path explosion and generate compact encodings. To accommodate a wide range of implementations, our semantics is parameterized by a symbolic factory, which abstracts away the details of merging and creation of symbolic values. The semantics targets a rich language that extends Core Scheme with assumptions and assertions, and thus supports branching, loops, and (first-class) procedures. The semantics is designed to support reusability, by guaranteeing two key properties: legality of the generated symbolic states, and the reducibility of symbolic evaluation to concrete evaluation. Legality makes it simpler for client tools to formulate queries, and reducibility enables testing of client tools on concrete inputs. We use the Lean theorem prover to mechanize our symbolic semantics, prove that it is sound and complete with respect to the concrete semantics, and prove that it guarantees legality and reducibility. To demonstrate the generality of our semantics, we develop Leanette, a reference evaluator written in Lean, and Rosette 4, an optimized evaluator written in Racket. We prove Leanette correct with respect to the semantics, and validate Rosette 4 against Leanette via solver-aided differential testing. To demonstrate the practicality of our approach, we port 16 published verification and synthesis tools from Rosette 3 to Rosette 4. Rosette 3 is an existing reusable evaluator that implements the classic merging semantics, adopted from bounded model checking. Rosette 4 replaces the semantic core of Rosette 3 but keeps its optimized symbolic factory. Our results show that Rosette 4 matches the performance of Rosette 3 across a wide range of benchmarks, while providing a cleaner interface that simplifies the implementation of client tools.
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This content will become publicly available on July 12, 2025
Natural Symbolic Execution-Based Testing for Big Data Analytics
Symbolic execution is an automated test input generation technique that models individual program paths as logical constraints. However, the realism of concrete test inputs generated by SMT solvers often comes into question. Existing symbolic execution tools only seek arbitrary solutions for given path constraints. These constraints do not incorporate the naturalness of inputs that observe statistical distributions, range constraints, or preferred string constants. This results in unnatural-looking inputs that fail to emulate real-world data. In this paper, we extend symbolic execution with consideration for incorporating naturalness. Our key insight is that users typically understand the semantics of program inputs, such as the distribution of height or possible values of zipcode, which can be leveraged to advance the ability of symbolic execution to produce natural test inputs. We instantiate this idea in NaturalSym, a symbolic execution-based test generation tool for data-intensive scalable computing (DISC) applications. NaturalSym generates natural-looking data that mimics real-world distributions by utilizing user-provided input semantics to drastically enhance the naturalness of inputs, while preserving strong bug-finding potential. On DISC applications and commercial big data test benchmarks, NaturalSym achieves a higher degree of realism —as evidenced by a perplexity score 35.1 points lower on median, and detects 1.29× injected faults compared to the state-of-the-art symbolic executor for DISC, BigTest. This is because BigTest draws inputs purely based on the satisfiability of path constraints constructed from branch predicates, while NaturalSym is able to draw natural concrete values based on user-specified semantics and prioritize using these values in input generation. Our empirical results demonstrate that NaturalSym finds injected faults 47.8× more than NaturalFuzz (a coverage-guided fuzzer) and 19.1× more than ChatGPT. Meanwhile, TestMiner (a mining-based approach) fails to detect any injected faults. NaturalSym is the first symbolic executor that combines the notion of input naturalness in symbolic path constraints during SMT-based input generation. We make our code available at https://github.com/UCLA-SEAL/NaturalSym.
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- PAR ID:
- 10527615
- Publisher / Repository:
- ACM
- Date Published:
- Journal Name:
- Proceedings of the ACM on Software Engineering
- Volume:
- 1
- Issue:
- FSE
- ISSN:
- 2994-970X
- Page Range / eLocation ID:
- 2677 to 2700
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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