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Object Oriented Software Testing with Genetic Programming and Program Analysis

Object Oriented Software Testing with Genetic Programming and Program Analysis
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Author(s): Arjan Seesing (Enigmatry, Netherlands) and Hans-Gerhard Gross (Delft University of Technology, Netherlands)
Copyright: 2012
Pages: 15
Source title: Computer Engineering: Concepts, Methodologies, Tools and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-61350-456-7.ch414

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Abstract

Testing is a difficult and costly activity in the development of object-oriented programs. The challenge is to come up with a sufficient set of test scenarios, out of the typically huge volume of possible test cases, to demonstrate correct behavior and acceptable quality of the software. This can be reformulated as a search problem to be solved by sophisticated heuristic search techniques such as evolutionary algorithms. The goal is to find an optimal set of test cases to achieve a given test coverage criterion. This chapter introduces and evaluates genetic programming as a heuristic search algorithm which is suitable to evolve object-oriented test programs automatically to achieve high coverage of a class. It outlines why the object paradigm is different to the procedural paradigm with respect to testing, and why a genetic programming approach might be better suited than the genetic algorithms typically used for testing procedural code. The evaluation of our implementation of a genetic programming approach, augmented with program analysis techniques for better performance, indicates that object-oriented software testing with genetic programming is feasible in principle. However, having many adjustable parameters, evolutionary search heuristics have to be fined-tuned to the optimization problem at hand for optimal performance, and, therefore, represent a difficult optimization problem in their own right.

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