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 N atural S ym , 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, N atural S ym 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, B ig T est . This is because B ig T est draws inputs purely based on the satisfiability of path constraints constructed from branch predicates, while N atural S ym 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 N atural F uzz (a coverage-guided fuzzer) and 19.1× more than ChatGPT. Meanwhile, TestMiner (a mining-based approach) fails to detect any injected faults. N atural S ym 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 .