Testing of Future Internet Applications Running in the Cloud

Testing of Future Internet Applications Running in the Cloud

Tanja Vos (Universidad Politécnica de Valencia, Spain), Paolo Tonella (Fondazione Bruno Kessler, Italy), Joachim Wegener (Berner & Mattner, Germany), Mark Harman (University College London, UK), Wishnu Prasetya (University of Utrecht, The Netherlands) and Shmuel Ur (Bristol University, UK)
DOI: 10.4018/978-1-4666-2536-5.ch014
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The cloud will be populated by software applications that consist of advanced, dynamic, and largely autonomic interactions among services, end-user applications, content, and media. The complexity of the technologies involved in the cloud makes testing extremely challenging and demands novel approaches and major advancements in the field. This chapter describes the main challenges associated with the testing of applications running in the cloud. The authors present a research agenda that has been defined in order to address the testing challenges. The goal of the agenda is to investigate the technologies for the development of an automated testing environment, which can monitor the applications under test and can react dynamically to the observed changes. Realization of this environment involves substantial research in areas such as search based testing, model inference, oracle learning, and anomaly detection.
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FI testing demands for major advancements in several areas of software testing. We discuss the state of the art in each of these areas separately, in the following.

Beyond the State of the Art of Search Based Techniques

The current state of the art in search based techniques is described in (Harman, 2007; Harman & Afshin, 2010). The area of testing is the most prominent software engineering domain for the application of search techniques. Search based testing techniques have been applied to various real world complex systems (e.g., embedded systems) (Vos et al., 2010; Baars et al., 2010) to deal with automated test case generation for structural (white-box) as well as functional (black-box) testing. Also the testing of various non-functional properties have been investigated (Afzal et al., 2009). While these testing targets remain relevant for FI applications as well, the continuous, autonomous testing framework that we envision introduces new opportunities for search based exploration of the solution space. Correspondingly, novel fitness function definitions and search algorithms will be required.

Innovative approaches to genetic programming applied to testing may also contribute to FI testing. So far, genetic programming has received limited attention in testing. It has been successfully used to conduct unit testing of object oriented code, by providing a simple and effective mechanism to bring the object under test to a proper internal state (Tonella, 2004). We think genetic programming can be pushed beyond such simple applications, by considering it as a powerful technique to co-evolve the testing engine together with the self-modifying, adaptive FI application under test.

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