[1] 王蕾, 李丰, 李炼, 等. 污点分析技术的原理和实践应用[J]. 软件学报, 2017, 28(4):860-882. (WANG L, LI F, LI L, et al. Principle and practice of taint analysis[J]. Journal of Software, 2017, 28(4):860-882.) [2] 赵云山, 宫云战, 王前, 等. 静态缺陷检测中的误报消除技术研究[J]. 计算机研究与发展, 2012, 49(9):1822-1831. (ZHAO Y S, GONG Y Z, WANG Q, et al. False positive elimination in static defect detection[J]. Journal of Computer Research and Development, 2012, 49(9):1822-1831.) [3] 李筱, 周严, 李孟宸, 等. C/C++程序静态内存泄漏警报自动确认方法[J]. 软件学报, 2017, 28(4):827-844. (LI X, ZHOU Y, LI M C, et al. Automatically validating static memory leak warnings for C/C++programs[J]. Journal of Software, 2017, 28(4):827-844.) [4] GE X, TANEJA K, XIE T, et al. DyTa:dynamic symbolic execution guided with static verification results[C]//Proceedings of the 33rd International Conference on Software Engineering. New York:ACM, 2011:992-994. [5] 王蕾, 周卿, 何东杰, 等. 面向Android应用隐私泄露检测的多源污点分析技术[J]. 软件学报, 2019, 30(2):211-230. (WANG L, ZHOU Q, HE D J, et al. Multi-sources taint analysis technique for privacy leak detection of Android apps[J]. Journal of Software, 2019, 30(2):211-230.) [6] RICE H G. Classes of recursively enumerable sets and their decision problems[J]. Transactions of the American Mathematical Society, 1953, 74(2):358-366. [7] YANG Z, YANG M, ZHANG Y, et al. AppIntent:analyzing sensitive data transmission in Android for privacy leakage detection[C]//Proceedings of the 2013 ACM SIGSAC Conference on Computer & Communications Security. New York:ACM, 2013:1043-1054. [8] ARZT S, RASTHOFER S, HAHN R, et al. Using targeted symbolic execution for reducing false-positives in dataflow analysis[C]//Proceedings of the 4th ACM SIGPLAN International Workshop on State of the Art in Program Analysis. New York:ACM, 2015:1-6. [9] JUNKER M, HUUCK R, FEHNKER A, et al. SMT-based false positive elimination in static program analysis[C]//Proceedings of the 2012 International Conference on Formal Engineering Methods, LNCS 7635. Berlin:Springer, 2012:316-331. [10] ZHANG L, THING V L L. A hybrid symbolic execution assisted fuzzing method[C]//Proceedings of the 2017 IEEE Region 10 Conference. Piscataway:IEEE, 2017:822-825. [11] CAI J, YANG S, MEN J, et al. Automatic software vulnerability detection based on guided deep fuzzing[C]//Proceedings of the IEEE 5th International Conference on Software Engineering and Service Science. Piscataway:IEEE, 2014:231-234. [12] PENG H, SHOSHITAISHVILI Y, PAYER M. T-Fuzz:fuzzing by program transformation[C]//Proceedings of the 2018 IEEE Symposium on Security and Privacy. Piscataway:IEEE, 2018:697-710. [13] GODEFROID P, LEVIN M Y, MOLNAR D. SAGE:whitebox fuzzing for security testing[J]. Communications of the ACM, 2012, 55(3):40-44. [14] TANEJA K, XIE T, TILLMANN N, et al. Guided path exploration for regression test generation[C]//Proceedings of the 31st International Conference on Software Engineering. Piscataway:IEEE, 2009:311-314. [15] ARZT S, RASTHOFER S, FRITZ C, et al. FlowDroid:precise context, flow, field, object-sensitive and lifecycle-aware taint analysis for Android apps[J]. ACM SIGPLAN Notices, 2014, 49(6):259-269. [16] BODDEN E. Inter-procedural data-flow analysis with IFDS/IDE and soot[C]//Proceedings of the ACM SIGPLAN International Workshop on State of the Art in Java Program Analysis. New York:ACM, 2012:3-8. |