A Genetic Programming Approach to Automated Test Generation for Object-Oriented Software

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{SeesingG06c,

• author = "Arjan Seesing and Hans-Gerhard Gross",
• title = "A Genetic Programming Approach to Automated Test Generation for Object-Oriented Software",
• booktitle = "Proceedings of the 1st International Workshop on Evaluation of Novel Approaches to Software Engineering",
• year = "2006",
• keywords = "genetic algorithms, genetic programming, SBSE",
• bibsource = "http://crestweb.cs.ucl.ac.uk/resources/sbse_repository/repository.html",
• URL = "http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.97.4413&rep=rep1&type=pdf",
• size = "9 pages",
• abstract = "In this article we propose a new method for creating test software for object-oriented systems using a genetic programming approach. We believe this approach is advantageous over the more established search-based test-case generation approaches because the test software is represented and altered as a fully functional computer program. Genetic programming (GP) uses a tree-shaped data structure which is more directly comparable and suitable for being mapped instantly to abstract syntax trees commonly used in computer languages and compilers. These structures can be manipulated and executed directly, bypassing intricate and error prone conversion procedures between different representations. In addition, tree structures make more operations possible, which keep the structure and semantics of the evolving test software better intact during program evolution, compared to linear structures. This speeds up the evolutionary program generation process because the loss of evolved structures due to mutations and crossover is prevented more effectively.",
• notes = "Cf. \cite{SeesingG06b}",

}

Genetic Programming entries for Arjan Seesing Hans-Gerhard Gross

Citations