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Statistical Symbolic Execution with Informed Sampling.

, , , and . Software Engineering & Management, volume P-239 of LNI, page 51-52. GI, (2015)

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A survey of new trends in symbolic execution for software testing and analysis., and . Int. J. Softw. Tools Technol. Transf., 11 (4): 339-353 (2009)Parallelization Techniques for Verifying Neural Networks., , , , , , , , , and . CoRR, (2020)Analysing the effect of uncertainty in airport surface operations., , and . ISSTA/ECOOP Workshops, page 132-137. ACM, (2018)On the probabilistic analysis of neural networks., , , and . SEAMS@ICSE, page 5-8. ACM, (2020)Guest editorial: special multi-issue on selected topics in Automated Software Engineering., and . Autom. Softw. Eng., 22 (3): 289-290 (2015)Saffron: Adaptive Grammar-based Fuzzing for Worst-Case Analysis., , , , , and . ACM SIGSOFT Softw. Eng. Notes, 44 (4): 14 (2019)Tenet: A Flexible Framework for Machine-Learning-based Vulnerability Detection., , , , , , and . CAIN, page 102-103. IEEE, (2023)Case Study: Analysis of Autonomous Center Line Tracking Neural Networks., , , and . VSTTE, volume 13124 of Lecture Notes in Computer Science, page 104-121. Springer, (2021)Planning, Scheduling and Monitoring for Airport Surface Operations., , , , , , and . AAAI Workshop: Planning for Hybrid Systems, volume WS-16-12 of AAAI Technical Report, AAAI Press, (2016)978-1-57735-759-9.Symbolic Execution for Importance Analysis and Adversarial Generation in Neural Networks., , , , , and . ISSRE, page 313-322. IEEE, (2019)