Skip to main content Link Expand (external link) Copy Copied Anand S, Burke E K, Chen T Y, et al. An orchestrated survey of methodologies for automated software test case generation . Journal of Systems and Software, 2013, 86(8): 1978-2001 Runeson P. A survey of unit testing practices . IEEE Software, 2006, 23(4): 22-29. Sadowski C, Söderberg E, Church L, et al. Modern code review: A case study at Google . International Conference on Software Engineering: Software Engineering in Practice. 2018: 181-190. Yoo S, Harman M. Regression testing minimization, selection and prioritization: a survey . Software Testing, Verification and Reliability, 2012, 22(2): 67-120. Chen T Y, Kuo F C, Merkel R G, et al. Adaptive random testing: The art of test case diversity . Journal of Systems and Software, 2010, 83(1): 60-66. Huang R, Sun W, Xu Y, et al. A survey on adaptive random testing . IEEE Transactions on Software Engineering, 2019. Nie C, Leung H. A survey of combinatorial testing . ACM Computing Surveys (CSUR), 2011, 43(2): 1-29. Kuhn D R, Kacker R N, Lei Y. Practical combinatorial testing . NIST special Publication, 2010 Cadar C, Sen K. Symbolic execution for software testing: Three decades later . Communications of the ACM, 2013, 56(2): 82-90. Baldoni R, Coppa E, D’elia D C, et al. A survey of symbolic execution techniques . ACM Computing Surveys, 2018, 51(3): 1-39. McMinn P. Search‐based software test data generation: a survey . Software Testing, Verification and Reliability, 2004, 14(2): 105-156. Harman M, McMinn P, De Souza J T, et al. Search based software engineering: Techniques, taxonomy, tutorial . Empirical Software Engineering and Verification. 2010: 1-59. Jia Y, Harman M. An analysis and survey of the development of mutation testing . IEEE Transactions on Software Engineering, 2010, 37(5): 649-678. Petrović G, Ivanković M, Fraser G, et al. Practical mutation testing at scale: A view from Google . IEEE Transactions on Software Engineering, 2021, 48(10): 3900-3912. Chen T Y, Kuo F C, Liu H, et al. Metamorphic testing: A review of challenges and opportunities . ACM Computing Surveys, 2018, 51(1): 1-27. Manès V J M, Han H S, Han C, et al. The art, science, and engineering of fuzzing: A survey . IEEE Transactions on Software Engineering, 2019. Zhang J M, Harman M, Ma L, et al. Machine learning testing: Survey, landscapes and horizons . IEEE Transactions on Software Engineering, 2020. Randoop : Automatic unit test generation for Java (Feedback-directed random test generation)EvoSuite : Automatic test suite generation for Java (Search-based test generation)EvoMaster : A tool for automatically generating system-level test cases (Search-based test generation)KLEE : A dynamic symbolic execution enginejCUTE : Java concolic unit testing engineAFL++ : American Fuzzy Lop fuzzerPICT : Pairwise independent combinatorial toolCAGen : Fast combinatorial test set generationPIT : Mutation testing system for Java