- author = "Stefan Wappler and Joachim Wegener",
- title = "Evolutionary unit testing of object-oriented software using strongly-typed genetic programming",
- booktitle = "{GECCO 2006:} Proceedings of the 8th annual conference on Genetic and evolutionary computation",
- year = "2006",
- editor = "Maarten Keijzer and Mike Cattolico and Dirk Arnold and Vladan Babovic and Christian Blum and Peter Bosman and Martin V. Butz and Carlos {Coello Coello} and Dipankar Dasgupta and Sevan G. Ficici and James Foster and Arturo Hernandez-Aguirre and Greg Hornby and Hod Lipson and Phil McMinn and Jason Moore and Guenther Raidl and Franz Rothlauf and Conor Ryan and Dirk Thierens",
- volume = "2",
- ISBN = "1-59593-186-4",
- pages = "1925--1932",
- address = "Seattle, Washington, USA",
-
URL = "
http://gpbib.cs.ucl.ac.uk/gecco2006/docs/p1925.pdf",
-
DOI = "
doi:10.1145/1143997.1144317",
- publisher = "ACM Press",
- publisher_address = "New York, NY, 10286-1405, USA",
- month = "8-12 " # jul,
- organisation = "ACM SIGEVO (formerly ISGEC)",
- keywords = "genetic algorithms, genetic programming, Search-Based Software Engineering, automated test case generation, evolutionary testing, object-orientation, strongly-typed genetic programming, test coverage of code, testing tools, verification, Java, OO unit testing, STGP",
- size = "8 pages",
- abstract = "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.",
-
notes = "GECCO-2006 A joint meeting of the fifteenth
international conference on genetic algorithms
(ICGA-2006) and the eleventh annual genetic programming
conference (GP-2006).
ACM Order Number 910060.
Two stage EA: GP (ECJ) run first then every tree's {"}numerical parameters{"} optimised by a separate EA (GEATbx). Polymorphic set-based typing. Aim is to cover a particular program branch in class under test (CUT). Fitness is linear combination of three (software engineering) metrics. Static analysis done using Tatsubori's OpenJava. GP pop=10. GEATbx 4 pops, each of ten. Problems stack, BitSet, full test coverage of each obtained.",
