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Solving String Constraints With Regex-Dependent Functions Through Transducers With Priorities And Variables

Published in The 49th Annual ACM SIGPLAN Symposium on Principles of Programming Languages (POPL), 2022

Regular expressions are a classical concept in formal language theory. Regular expressions in programming languages (RegEx) such as JavaScript, feature non-standard semantics of operators (e.g. greedy/lazy Kleene star), as well as additional features such as capturing groups and references. While symbolic execution of programs containing RegExes appeals to string solvers natively supporting important features of RegEx, such a string solver is hitherto missing. In this paper, we propose the first string theory and string solver that natively provides such support. The key idea of our string solver is to introduce a new automata model, called prioritized streaming string transducers (PSST), to formalize the semantics of RegEx-dependent string functions. PSSTs combine priorities, which have previously been introduced in prioritized finite-state automata to capture greedy/lazy semantics, with string variables as in streaming string transducers to model capturing groups. We validate the consistency of the formal semantics with the actual JavaScript semantics by extensive experiments. Furthermore, to solve the string constraints, we show that PSSTs enjoy nice closure and algorithmic properties, in particular, the regularity-preserving property (i.e., pre-images of regular constraints under PSSTs are regular), and introduce a sound sequent calculus that exploits these properties and performs propagation of regular constraints by means of taking post-images or pre-images. Although the satisfiability of the string constraint language is generally undecidable, we show that our approach is complete for the so-called straightline fragment. We evaluate the performance of our string solver on over 195 000 string constraints generated from an open-source RegEx library. The experimental results show the efficacy of our approach, drastically improving the existing methods (via symbolic execution) in both precision and efficiency

Recommended citation: Taolue Chen, Alejandro Flores-Lamas, Matthew Hague, Zhilei Han, Denghang Hu, Shuanglong Kan, Anthony W. Lin, Philipp R ╠łummer, and Zhilin Wu. 2022. Solving String Constraints with Regex-Dependent Functions through Transducers with Priorities and Variables. Proc. ACM Program. Lang. 6, POPL, Article 45 (January 2022), 45 pages.

Data-driven Recurrent Set Learning For Non-termination Analysis

Published in IEEE/ACM International Conference on Software Engineering (ICSE), 2023

Termination is a fundamental liveness property for program verification. In this paper, we revisit the problem of non-termination analysis and propose the first data-driven learning algorithm for synthesizing recurrent sets, where the non-terminating samples are effectively speculated by a novel method. To ensure convergence of learning, we develop a learning algorithm which is guaranteed to converge to a valid recurrent set if one exists, and thus establish its relative completeness. The methods are implemented in a prototype tool, and experimental results on public benchmarks show its efficacy in proving non-termination as it outperforms state-of-the-art tools, both in terms of cases solved and performance. Evaluation on non-linear programs also demonstrates its ability to handle complex programs.



Teaching experience 1

Undergraduate course, University 1, Department, 2014

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Teaching experience 2

Workshop, University 1, Department, 2015

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