mlsanityData to 7 Oct 2026

A Survey of Symbolic Execution Techniques

arXiv 1610.00502 ↗First posted 3 Oct 2016cs.SE cs.PLcited by 36

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Abstract (arXiv)

Many security and software testing applications require checking whether certain properties of a program hold for any possible usage scenario. For instance, a tool for identifying software vulnerabilities may need to rule out the existence of any backdoor to bypass a program's authentication. One approach would be to test the program using different, possibly random inputs. As the backdoor may only be hit for very specific program workloads, automated exploration of the space of possible inputs is of the essence. Symbolic execution provides an elegant solution to the problem, by systematically exploring many possible execution paths at the same time without necessarily requiring concrete inputs. Rather than taking on fully specified input values, the technique abstractly represents them as symbols, resorting to constraint solvers to construct actual instances that would cause property violations. Symbolic execution has been incubated in dozens of tools developed over the last four decades, leading to major practical breakthroughs in a number of prominent software reliability applications. The goal of this survey is to provide an overview of the main ideas, challenges, and solutions developed in the area, distilling them for a broad audience. The present survey has been accepted for publication at ACM Computing Surveys. If you are considering citing this survey, we would appreciate if you could use the following BibTeX entry: http://goo.gl/Hf5Fvc

Key facts. Cited by 36 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 Oct 2016.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 4; 2023: 6; 2024: 4; 2025: 12; 2026: 10.

36 papers.

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  1. 2025CyberGym: Evaluating AI Agents' Real-World Cybersecurity Capabilities at Scalecited by 71
  2. 2024Code-Aware Prompting: A study of Coverage Guided Test Generation in Regression Setting using LLMcited by 63
  3. 2026SkillNet: Create, Evaluate, and Connect AI Skillscited by 48
  4. 2023CAT-LM: Training Language Models on Aligned Code And Testscited by 28
  5. 2022A Quantitative Geometric Approach to Neural-Network Smoothnesscited by 16
  6. 2024LLM-Powered Test Case Generation for Detecting Bugs in Plausible Programscited by 14
  7. 2023Binary Code Summarization: Benchmarking ChatGPT/GPT-4 and Other Large Language Modelscited by 13
  8. 2022Toward Trustworthy Neural Program Synthesiscited by 11
  9. 2025A Contemporary Survey of Large Language Model Assisted Program Analysiscited by 10
  10. 2022Safe Neurosymbolic Learning with Differentiable Symbolic Executioncited by 9
  11. 2025SACTOR: LLM-Driven Correct and Idiomatic C to Rust Translation with Static Analysis and FFI-Based Verificationcited by 9
  12. 2023When Less is Enough: Positive and Unlabeled Learning Model for Vulnerability Detectioncited by 8
24 more
  1. 2025TestForge: Feedback-Driven, Agentic Test Suite Generationcited by 8
  2. 2023Automating the Correctness Assessment of AI-generated Code for Security Contextscited by 6
  3. 2024Generating executable oracles to check conformance of client code to requirements of JDK Javadocs using LLMscited by 6
  4. 2022MANDO: Multi-Level Heterogeneous Graph Embeddings for Fine-Grained Detection of Smart Contract Vulnerabilitiescited by 4
  5. 2025Automated Testing of COBOL to Java Transformationcited by 4
  6. 2023Statement-based Memory for Neural Source Code Summarizationcited by 2
  7. 2024Dynamic Neural Control Flow Execution: An Agent-Based Deep Equilibrium Approach for Binary Vulnerability Detectioncited by 2
  8. 2025Automated Validation of COBOL to Java Transformationcited by 2
  9. 2025DiffuTester: Accelerating Unit Test Generation for Diffusion LLMs via Mining Structural Patterncited by 2
  10. 2023Neuro-Symbolic Execution of Generic Source Codecited by 1
  11. 2025AutoStub: Genetic Programming-Based Stub Creation for Symbolic Executioncited by 1
  12. 2025R1-Fuzz: Specializing Language Models for Textual Fuzzing via Reinforcement Learningcited by 1
  13. 2025Navigating the Labyrinth: Path-Sensitive Unit Test Generation with Large Language Modelscited by 1
  14. 2026Program Analysis Guided LLM Agent for Proof-of-Concept Generationcited by 1
  15. 2026RAVEN: Agentic RAG for Automated Vulnerability Repaircited by 1
  16. 2025Polygon: Symbolic Reasoning for SQL using Conflict-Driven Under-Approximation Searchcited by 0
  17. 2025Validating Solidity Code Defects using Symbolic and Concrete Execution powered by Large Language Modelscited by 0
  18. 2026AutoEG: Exploiting Known Third-Party Vulnerabilities in Black-Box Web Applicationscited by 0
  19. 2026Improving MPI Error Detection and Repair with Large Language Models and Bug Referencescited by 0
  20. 2026A Multi-Agent Framework for Automated Exploit Generation with Constraint-Guided Comprehension and Reflectioncited by 0
  21. 2026Analyzing Chain of Thought (CoT) Approaches in Control Flow Code Deobfuscation Taskscited by 0
  22. 2026Agentic Method for Deterministic Validation of Legacy Code Migrationcited by 0
  23. 2026Agent-Based Test Assertion Generation via Diverse Perspective Aggregationcited by 0
  24. 2026Agentic Planning for Symbolic Executioncited by 0