Search-Based Software Testing: Past, Present and Future

By Phil McMinn. 2011 IEEE Fourth International Conference on Software Testing, Verification and Validation Workshops, 2011

376 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

The Oracle Problem in Software Testing: A Survey (2015, neutral): “Search-Based Testing [3], [5], [83], [126], [128] and Dynamic”
An Empirical Evaluation of Using Large Language Models for Automated Unit Test Generation (2023, background): “…an alternative to (and potentially complements) traditional techniques for automated test generation, including feedback-directed random test generation [7], [8], [9], [10], [11], search-based and evolutionary techniques [16], [17], [63], [64], and dynamic symbolic execution [12], [13], [14], [15].”
FairFuzz: A Targeted Mutation Strategy for Increasing Greybox Fuzz Testing Coverage (2017, methodology): “Search-based software testing (SBST) [19, 20, 22–24, 29, 31, 32, 42] uses optimization techniques such as hill climbing and genetic algorithms to generate inputs that optimize some observable fitness function.”
Automated Test Case Generation as a Many-Objective Optimisation Problem with Dynamic Selection of the Targets (2018, neutral): “According to McMinn [38], there are several other potential non-coverage objectives that could be added to test case generation tools, such as minimising the oracle cost, maximising the test case diversity to increase the likelihood to expose more faults, etc.”