Search‐based software test data generation: a survey

By Phil McMinn. Software testing, verification & reliability, 2004

1,574 citations recorded by the source corpus.

Subfields: Information Systems, Software

View Paper: https://www.semanticscholar.org/paper/dad0ae85f7a2cfbff1d44098347f6c00f90f8a9a

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Citations in the corpus

Search-Based Software Testing: Past, Present and Future (2011, methodology): “The term ‘Search-Based Software Testing’ began to be used to refer to a software testing approach that used a metaheuristic algorithm, with the amount of work in Search-Based Test Data Generation alone reaching a level that led to a survey of the field by McMinn in 2004 [21].”
Search-based software engineering: Trends, techniques and applications (2012, neutral): “There have been several important surveys in this widely studied general area [Afzal et al. 2009; Ali et al. 2010; McMinn 2004].”
Constructing Interaction Test Suites for Highly-Configurable Systems in the Presence of Constraints: A Greedy Approach (2008, methodology): “Examples of search activity in software testing include the use of genetic algorithms for test case generation [40], [41], [42], greedy algorithms for regression test case selection and prioritization [43], and our own work that uses simulated annealing for generation of CIT samples [9], [44].”
Search-Based Procedural Content Generation: A Taxonomy and Survey (2011, background): “For example, a 100 100 maze would need to be encoded as a vector of length 10 000, which is more than many search algorithms can effectively approach.”
A Systematic Review of the Application and Empirical Investigation of Search-Based Test Case Generation (2010, background): “A systematic review synthesizes the existing work in a systematic, comprehensive, and unbiased manner.”
Search Based Software Engineering: Techniques, Taxonomy, Tutorial (2010, methodology): “High level description of a Genetic Algorithm, adapted from McMinn [65] This section presents an application of a search-based approach to the Test Case Prioritisation (TCP) in regression testing, illustrating the steps that are necessary to obtain the first set of results.”
DeepTest: Automated Testing of Deep-Neural-Network-Driven Autonomous Cars (2017, neutral): “[56], and Pasareanu et al.”
The Current State and Future of Search Based Software Engineering (2007, methodology): “Fortunately, there is also already an excellent survey of work on search based testing [60].”
Regression testing minimization, selection and prioritization: a survey (2012, background): “Automatic test data generation has made advances in both functional and non-functional testing [183].”
Mutation-Driven Generation of Unit Tests and Oracles (2010, methodology): “This basically is the prevailing approach level and branch distance measurement applied in a search-based test data generation [29].”
An orchestrated survey of methodologies for automated software test case generation (2013, background): “, 2010), as well s overviews of techniques (Harman et al., 2009; McMinn, 2004; cMinn et al., 2012).”
Pynguin: Automated Unit Test Generation for Python (2022, background): “Over the years, researchers have presented many approaches for generating test input data, such as random [4] and search-based [14] techniques.”
A Hitchhiker's guide to statistical tests for assessing randomized algorithms in software engineering (2014, methodology): “Applications of search algorithms include software testing [8], requirement engineering [9], project planning and cost estimation [10], bug fixing [11], automated maintenance [12], service-oriented software engineering [13], compiler optimization [14] and quality assessment [15].”
sFuzz: An Efficient Adaptive Fuzzer for Solidity Smart Contracts (2020, methodology): “inspired by search-based software testing [16, 24], the latter distinguishes itself by having a lightweight objective function (designed considering characteristics of Solidity programs) as well as a novel multi-objective optimization strategy.”
Whole Test Suite Generation (2013, methodology): “We use an evolutionary technique [ 1], [34] in which, instead of evolving each test case individually, we evolve all the test cases in a test suite at the same time, and the fitness function considers all the testing goals simultaneo usly.”
Machine Learning Testing: Survey, Landscapes and Horizons (2019, methodology): “Search-based Software Test generation (SBST) [87] uses a meta-heuristic optimising search technique, such as a Genetic Algorithm, to automatically generate test inputs.”