Evolutionary testing of classes

By Paolo Tonella. International Symposium on Software Testing and Analysis, 2004

493 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Software

View Paper: https://www.semanticscholar.org/paper/b28826ebec1f90cf04fba0418b81020c5ad17020

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In Computational Theory and Mathematics

In Software

Citations in the corpus

TimeAware test suite prioritization (2006, result): “Since both Tonella and McMinn and Holcombe report a lines of code (LOC) size metric instead of NCSS, it is possible that the di.erence in case study application size is even greater.”
Parameter tuning or default values? An empirical investigation in search-based software engineering (2013, methodology): “The test cases may have variable length [2], and so earlier approaches to testing object-oriented software made use of method sequences [17, 35] or strongly typed genetic programming [30,37].”
Fairness testing: testing software for discrimination (2017, methodology): “Automated testing research has produced tools to generate tests, including random testing, such as Randoop [63, 64], NightHawk [4], JCrasher [20], CarFast [65], and T3 [69]; search-based testing, such as EvoSuite [29], TestFul [9], and eToc [78]; dynamic symbolic execution tools, such as DSC [37], S”
Mutation-Driven Generation of Unit Tests and Oracles (2010, methodology): “[34] P. Tonella.”
Automated Test Case Generation as a Many-Objective Optimisation Problem with Dynamic Selection of the Targets (2018, methodology): “Search-based test case generators use meta-heuristic optimisation algorithms, such as genetic algorithms (GA), to produce new test cases from the previously generated (initially, random) ones, so as to reduce and eventually nullify their distance from each of the yet uncovered targets [34], [48].”
A Systematic Review of the Application and Empirical Investigation of Search-Based Test Case Generation (2010, background): “Other examples of encodings are binary, gray code, and decimal, as described in [41].”
Whole Test Suite Generation (2013, methodology): “Search-based techniques have also been applied to test object-oriented software using method sequences [21], [43] or strongly typed genetic programming [37], [47].”
Search Based Software Engineering: A Comprehensive Analysis and Review of Trends Techniques and Applications (2009, methodology): “Tonella [450] was the first to apply Search Based testing techniques to the problem of testing Object Oriented (OO) software.”
A practical guide for using statistical tests to assess randomized algorithms in software engineering (2011, methodology): “For example, when testing for object-oriented software using search algorithms (e.g., [59]), complex non-standard search operators are required.”
Automated Unit Test Generation for Python (2020, unknown): “Search-based approaches use a similar representation, but apply evolutionary search algorithms to maximize code coverage [1,4,9,19].”
A Hitchhiker's guide to statistical tests for assessing randomized algorithms in software engineering (2014, methodology): “For example, when testing for object-oriented software using search algorithms (e.g., [114]), complex non-standard search operators are required.”
EvoSuite: automatic test suite generation for object-oriented software (2011, background): “TestFul [1] and eToc [13] are perhaps the most related tools.”
Search-Based Software Testing: Past, Present and Future (2011, methodology): “There has also been work dealing with dynamic data structures [33] , and the eToc tool of Tonella [34] will generate object parameters for Java programs.”
EvoDroid: segmented evolutionary testing of Android apps (2014, methodology): “Two unit level evolutionary testing approaches were presented in [36,37].”