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检索条件"主题词=Strongly-Typed Genetic Programming"

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Comparing the expressive power of strongly-typed and Grammar-Guided genetic programming 23

Comparing the expressive power of Strongly-Typed and Grammar...

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genetic and Evolutionary Computation Conference (GECCO)

作者： Fonseca, Alcides Pocas, Diogo Univ Lisbon Fac Ciencias LASIGE Lisbon Portugal

ISBN: (纸本)9798400701191

Since genetic programming (GP) has been proposed, several flavors of GP have arisen, each with their own strengths and limitations. Grammar-Guided and strongly-typed GP (GGGP and STGP, respectively) are two popular flavors that have the advantage of allowing the practitioner to impose syntactic and semantic restrictions on the generated programs. GGGP makes use of (traditionally contextfree) grammars to restrict the generation of (and the application of genetic operators on) individuals. By guiding this generation according to a grammar, i.e. a set of rules, GGGP improves performance by searching for an good-enough solution on a subset of the search space. This approach has been extended with Attribute Grammars to encode semantic restrictions, while ContextFree Grammars would only encode syntactic restrictions. STGP is also able to restrict the shape of the generated programs using a very simple grammar together with a type system. In this work, we address the question of which approach has more expressive power. We demonstrate that STGP has higher expressive power than Context-Free GGGP and less expressive power than Attribute Grammatical Evolution.

关键词： Grammar-Guided genetic programming strongly-typed genetic programming genetic programming

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Data Types as a More Ergonomic Frontend for Grammar-Guided genetic programming 2022

Data Types as a More Ergonomic Frontend for Grammar-Guided G...

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21st ACM SIGPLAN International Conference on Generative programming: Concepts and Experiences (GPCE)

作者： Espada, Guilherme Ingelse, Leon Canelas, Paulo Barbosa, Pedro Fonseca, Alcides Univ Lisbon LASIGE Fac Ciencias Lisbon Portugal Carnegie Mellon Univ Sch Comp Sci Pittsburgh PA 15213 USA Inst Med Mol Lisbon Portugal

ISBN: (纸本)9781450399203

genetic programming (GP) is an heuristic method that can be applied to many Machine Learning, Optimization and Engineering problems. In particular, it has been widely used in Software Engineering for Test-case generation, Program Synthesis and Improvement of Software (GI). Grammar-Guided genetic programming (GGGP) approaches allow the user to refine the domain of valid program solutions. Backus Normal Form is the most popular interface for describing Context-Free Grammars (CFG) for GGGP. BNF and its derivatives have the disadvantage of interleaving the grammar language and the target language of the program. We propose to embed the grammar as an internal Domain-Specific Language in the host language of the framework. This approach has the same expressive power as BNF and EBNF while using the host language type-system to take advantage of all the existing tooling: linters, formatters, type-checkers, autocomplete, and legacy code support. These tools have a practical utility in designing software in general, and GP systems in particular. We also present Meta-Handlers, user-defined overrides of the tree-generation system. This technique extends our object-oriented encoding with more practicability and expressive power than existing CFG approaches, achieving the same expressive power of Attribute Grammars, but without the grammar vs target language duality. Furthermore, we evidence that this approach is feasible, showing an example Python implementation as proof. We also compare our approach against textual BNF-representations w.r.t. expressive power and ergonomics. These advantages do not come at the cost of performance, as shown by our empirical evaluation on 5 benchmarks of our example implementation against PonyGE2. We conclude that our approach has better ergonomics with the same expressive power and performance of textual BNF-based grammar encodings.

关键词： Grammar-guided genetic programming strongly-typed genetic programming genetic programming Framework

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strongly-typed genetic programming and purity analysis: input domain reduction for evolutionary testing problems 08

Strongly-typed genetic programming and purity analysis: inpu...

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Proceedings of the 10th annual conference on genetic and evolutionary computation

作者： José Carlos Bregieiro Ribeiro Mário Alberto Zenha-Rela Francisco Fernandéz de Vega Polytechnic Institute of Leiria Leiria Portugal University of Coimbra Coimbra Portugal University of Extremadura Mérida Spain

ISBN: (纸本)9781605581309

Search-based test case generation for object-oriented software is hindered by the size of the search space, which encompasses the arguments to the implicit and explicit parameters of the test object's public methods. The performance of this type of search problems can be enhanced by the definition of adequate Input Domain Reduction *** focus of our on-going work is on employing evolutionary algorithms for generating test data for the structural unit-testing of Java programs. Test cases are represented and evolved using the strongly-typed genetic programming paradigm; Purity Analysis is particularly useful in this situation because it provides a means to automatically identify and remove Function Set entries that do not contribute to the definition of interesting test scenarios.

关键词： search-based test case generation strongly-typed genetic programming input domain reduction

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Evolutionary unit testing of object-oriented software using strongly-typed genetic programming 06

Evolutionary unit testing of object-oriented software using ...

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8th Annual genetic and Evolutionary Computation Conference

作者： Wappler, Stefan Wegener, Joachim Tech Univ Berlin DaimlerChrysler Automot IT Inst Ernst Reuter Pl 7 D-10587 Berlin Germany DaimlerChrysler AG Res & Technol D-10559 Berlin Germany

ISBN: (纸本)9781595931863

Evolutionary algorithms have successfully been applied to software testing. Not only approaches that search for numeric test data for procedural test objects have been investigated, but also techniques for automatically generating test programs that represent object-oriented unit test cases. Compared to numeric test data, test programs optimized for object-oriented unit testing are more complex. Method call sequences that realize interesting test scenarios must be evolved. An arbitrary method call sequence is not necessarily feasible due to call dependences which exist among the methods that potentially appear in a method call sequence. The approach presented in this paper relies on a tree-based representation of method call sequences by which sequence feasibility is preserved throughout the entire search process. In contrast to other approaches in this area, neither repair of individuals nor penalty mechanisms are required. strongly-typed genetic programming is employed to generate method call trees. In order to deal with runtime exceptions, we use an extended distance-based fitness function. We performed experiments with four test objects. The initial results are promising: high code coverages were achieved completely automatically for all of the test objects.

关键词： automated test case generation evolutionary testing object-orientation strongly-typed genetic programming

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