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Search-based testing generates test cases by encoding an adequacy criterion as the fitness function that drives a search-based optimization algorithm. Genetic algorithms have been successfully applied in search-based testing: while most of them use adequacy criteria based on the structure of the program, some try to maximize the mutation score of the test suite. This work presents a genetic algorithm for generating a test suite for mutation testing. The algorithm adopts several features from existing bacteriological algorithms, using single test cases as individuals and keeping generated individuals in a memory. The algorithm can optionally use automated seeding when producing the first population, by taking into account interesting constants in the source code. We have implemented this algorithm in a framework and we have applied it to a WS-BPEL composition, measuring to which extent the genetic algorithm improves the initial random test suite. We compare our genetic algorithm, with and without automated seeding, to random testing.

High-quality software needs to meet both functional and non-functional requirements. In some cases, software must accomplish specific performance requirements, but most of the time, only high-level performance requirements are available: it is up to the developer to decide what performance should be expected from each part of the system. In this work, we show several algorithms that infer the required throughput and time limits for each action in an UML activity diagram from a global constraint and some optional local annotations. After studying their theoretical and empirical performance, we propose an approach for generating performance test cases from the activity diagram after it has been implemented as code. Our approach decouples the performance analysis model from the implementation details of the code to be tested.

WS-BPEL eases programming in the large by composing web services, but poses new challenges to classical white-box testing techniques. These have to be updated to take context into account and cope with its specific instructions for web service management. Takuan is an open-source system that dynamically generates invariants reflecting the internal logic of a WS-BPEL composition. After several improvements and the development of a graphical interface, we consider Takuan to be a mature tool that can help find both bugs in the WS-BPEL composition and missing test cases in the test suite.

This paper shows a novel tool, GAmera, the first mutant generation tool for testing Web Service compositions written in the WS-BPEL language. After several improvements and the development of a graphical interface, we consider GAmera to be a mature tool that implements an optimization technique to reduce the number of generated mutants without significant loss of testing effectiveness. A genetic algorithm is used for generating and selecting a subset of high-quality mutants. This selection reduces the computational cost of mutation testing. The subset of mutants generated with this tool allows the user to improve the quality of the initial test suite.

The dynamic and asynchronous nature of OASIS WS-BPEL 2.0 standard language for web service composition presents a challenge for traditional white-box testing techniques. Takuan is a tool that can help with this problem. It analyzes execution logs of a WS-BPEL process in a real-world WS-BPEL engine to dynamically generate composition invariants. Nevertheless, it suffered some performance issues when handling a large number of variables with complex multidimensional content. We present two techniques implemented to automatically alleviate these issues: dynamic analysis of XPath expressions to avoid inferring nonsensical invariants, and discarding invariants enforced by the XML Schema. We present practical results supporting our work and comment the impact and applicability of these techniques beyond WS-BPEL itself.