A Theoretical and Empirical Study of Search-Based Testing: Local, Global, and Hybrid Search

By Mark Harman, Phil McMinn. IEEE Transactions on Software Engineering, 2010

451 citations recorded by the source corpus.

Subfields: Information Systems, Software

View Paper: https://www.semanticscholar.org/paper/1a2052d74bdeaddc5fc15b49d732a43785a7c8f0

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Citations in the corpus

Automated Test Case Generation as a Many-Objective Optimisation Problem with Dynamic Selection of the Targets (2018, background): “geting one branch at a time [1], [19], [23], [33], and (ii)”
sFuzz: An Efficient Adaptive Fuzzer for Solidity Smart Contracts (2020, methodology): “2 return newSeeds; generate more test cases in the next step. We refer the readers to Section 2 for an example. Remark The above-described strategy is inspired by search-based software testing (SBST) [16, 24] and yet it differs from SBST in several ways. The high-level reason for the difference is t”
An orchestrated survey of methodologies for automated software test case generation (2013, methodology): “SBST has been applied to a wide variety of testing goals including structural (Harman and McMinn, 2010; McMinn et al., 2012; Michael et al., 2001; Tonella, 2004), functional (Wegener and Bühler, 2004), non-functional (Wegener and Grochtmann, 1998) and state-based”
Search Based Software Engineering: Techniques, Taxonomy, Tutorial (2010, background): “Though this problem has been very widely studied, this hill climbing approach still proved to be an attractive approach 17 years later, when it was studied and (favourably) compared empirically and theoretically to a more sophisticated genetic algorithm [49], suggesting that a hybrid that combined b”
Mutation-Driven Generation of Unit Tests and Oracles (2010, background): “search [22] (for example, if the input parameters are”
EvoSuite: automatic test suite generation for object-oriented software (2011, methodology): “EvoSuite uses a search-based approach integrating state-of-the-art techniques such as for example hybrid search [9], dynamic symbolic execution [7] and testability transformation [8].”
No More Manual Tests? Evaluating and Improving ChatGPT for Unit Test Generation (2023, unknown): “Traditional unit test generation techniques leverage searchbased [7], [8], [9], constraint-based [10], [11], [12], or random-”
Higher Order Mutation Testing (2009, background): “Reaching all mutants of a program can be achieved by any branch adequate test set, so reachability is an instance of branch coverage, which is widely studied in literature [10, 111, 123, 241].”
Software Testing With Large Language Models: Survey, Landscape, and Vision (2023, unknown): “Although various approaches, including search-based [3], [4], constraint-based [5] or random-based [6] techniques to generate a suite of unit tests, the coverage and the meaningfulness of the generated tests are still far from satisfactory [7], [8].”
Whole Test Suite Generation (2013, background): “But the use of local search for test suite evolution is still an unexplored field, although promising (see for example [ 11], [13], [26]).”
A Hitchhiker's guide to statistical tests for assessing randomized algorithms in software engineering (2014, methodology): “For example, automated techniques exist to generate test cases that satisfy some desired coverage criteria on the system under test, such as for example branch [58] and path coverage [51].”
Machine Learning Testing: Survey, Landscapes and Horizons (2019, methodology): “For example, the literature uses the terms ‘statebased testing’ [16] and ‘search-based testing’ [17], [18] to refer to testing techniques that make use of concepts of state and search space, whereas we use the terms ‘GUI testing’ [19] and ‘unit testing’ [20] to refer to test techniques that tackle c”
You Are the Only Possible Oracle: Effective Test Selection for End Users of Interactive Machine Learning Systems (2014, background): “Recently, in work including information retrieval-based bug localization [50] and test generation [22], [25], the use of artificial intelligence and machine learning techniques has become a popular research topic, and a major aspect of endeavors to deal with increasing software complexity [23].”
A practical guide for using statistical tests to assess randomized algorithms in software engineering (2011, methodology): “This is an extremely low number (for example, for test data generation in branch coverage it is often the case of using 100,000 fitness evaluations for each branch [26]) and we can conclude that there is very limited search taking place, which implies that a random search would have likely yielded s”
From evolutionary computation to the evolution of things (2015, background): “In the field of Search Based Software Engineering, evolutionary algorithms are gaining prominence in response to the mismatch between the availability of expert software engineers, and the explosion of interconnected devices requiring new and/or updated software 88 .”