Abstract-We present an algorithm for constructing fitness functions that improve the efficiency of search-based testing when trying to generate branch adequate test data. The algorithm combines symbolic information with dynamic analysis and has two key advantages: It does not require any change in the underlying test data generation technique and it avoids many problems traditionally associated with symbolic execution, in particular the presence of loops. We have evaluated the algorithm on industrial closed source and open source systems using both local and global search-based testing techniques, demonstrating that both are statistically significantly more efficient using our approach. The test for significance was done using a onesided, paired Wilcoxon signed rank test. On average, the local search requires 23.41% and the global search 7.78% fewer fitness evaluations when using a symbolic execution based fitness function generated by the algorithm.

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Such guidelines could be obtained by doing secondary studies on a body of evidence consisting of case studies evaluating and comparing testing techniques and tools. However, such a body of evidence is also lacking. In this paper, we will make a first step towards creating such body of evidence by defining a general methodological evaluation framework that can simplify the design of case studies for comparing software testing tools, and make the results more precise, reliable, and easy to compare. Using this framework, (1) software testing practitioners can more easily define case studies through an instantiation of the framework, (2) results can be better compared since they are all executed according to a similar design, (3) the gap in existing work on methodological evaluation frameworks will be narrowed, and (4) a body of evidence will be initiated. By means of validating the framework, we will present successful applications of this methodological framework to various case studies for evaluating testing tools in an industrial environment with real objects and real subjects.

Modern interactive software, such as computer games, employ complex user interfaces. Although these user interfaces make the games attractive and powerful, unfortunately they also make them extremely difficult to test. Not only do we have to deal with their functional complexity, but also the fine grained interactivity of their user interface blows up their interaction space, so that traditional automated testing techniques have trouble handling it. An agent-based testing approach offers an alternative solution: agents' goal driven planning, adaptivity, and reasoning ability can provide an extra edge towards effective navigation in complex interaction space. This paper presents aplib, a Java library for programming intelligent test agents, featuring novel tactical programming as an abstract way to exert control over agents' underlying reasoning-based behavior. This type of control is suitable for programming testing tasks. Aplib is implemented in such a way to provide the fluency of a Domain Specific Language (DSL). Its embedded DSL approach also means that aplib programmers will get all the advantages that Java programmers get: rich language features and a whole array of development tools.Keywords: automated game testing • AI for automated testing • intelligent agents for testing • agents tactical programming • intelligent agent programming