Philipp Zech

Risk-Based Security Testing in Cloud Computing Environments

Assuring the security of a software system in terms of testing nowadays still is a quite tricky task to conduct. Security requirements are taken as a foundation to derive tests to be executed against a system under test. Yet, these positive requirements by far do not cover all the relevant security aspects to be considered. Hence, especially in the event of security testing, negative requirements, derived from risk analysis, are vital to be incorporated. If considering todays emerging trend in the adoption of cloud computing, security testing even has a more important significance. Due to a clouds openness, in theory there exists an infinite number of tests. Hence, a concise technique to incorporate the results of risk analysis in security testing is inevitable. We therefore propose a new model-driven methodology for the security testing of cloud environments, ingesting misuse cases, defined by negative requirements derived from risk analysis.

Model-based security testing: a taxonomy and systematic classification

Model‐based security testing relies on models to test whether a software system meets its security requirements. It is an active research field of high relevance for industrial applications, with many approaches and notable results published in recent years. This article provides a taxonomy for model‐based security testing approaches. It comprises filter criteria (i.e. model of system security, security model of the environment and explicit test selection criteria) as well as evidence criteria (i.e. maturity of evaluated system, evidence measures and evidence level). The taxonomy is based on a comprehensive analysis of existing classification schemes for model‐based testing and security testing. To demonstrate its adequacy, 119 publications on model‐based security testing are systematically extracted from the five most relevant digital libraries by three researchers and classified according to the defined filter and evidence criteria. On the basis of the classified publications, the article provides an overview of the state of the art in model‐based security testing and discusses promising research directions with regard to security properties, coverage criteria and the feasibility and return on investment of model‐based security testing. Copyright

A generic platform for model-based regression testing

Model-based testing has gained widespread acceptance in the last few years. Models enable the platform independent analysis and design of tests in an early phase of software development resulting in effort reduction in terms of time and money. Furthermore, test models are easier to maintain than test code when software systems evolve due to their platform independence and traceability support. Nevertheless, most regression testing approaches, which ensure that system evolution does not introduce unintended effects, are solely code-based. Additionally, many model-based testing approaches do not consider regression testing when applied in practice, mainly due to the lack of appropriate tool support. Therefore, in this paper we present a generic tool platform for model-based regression testing based on the model versioning and evolution framework MoVE. Our approach enhances existing model-based testing approaches with regression testing capabilities aiming at better tool support for model-based regression testing. In a case study, we apply our platform to the model-based testing approaches UML Testing Profile and Telling TestStories.

Towards a Model Based Security Testing Approach of Cloud Computing Environments

In recent years Cloud computing became one of the most aggressively emerging computer paradigms resulting in a growing rate of application in the area of IT outsourcing. However, as recent studies have shown, security most of the time is the one requirement, neglected at all. Yet, especially because of the nature of usage of Cloud computing, security is inevitable. Unfortunately, assuring the security of a Cloud computing environment is not a one time task, it is a task to be performed during the complete lifespan of the Cloud. This is motivated by the fact that Clouds undergo daily changes in terms of newly deployed applications and offered services. Based on this assumption, in this paper, we propose a novel model -- based, change -- driven approach, employing risk analysis, to test the security of a Cloud computing environment among all layers. As a main intrusion point, our approach exploits the public service interfaces, as they are a major source of newly introduced vulnerabilities, possibly leading to severe security incidents.

A Tool-Based Methodology for System Testing of Service-Oriented Systems

We present a tool environment and its underlying principles for Telling TestStories, an approach to model-driven system testing of service-oriented systems. Telling TestStories is based on tightly integrated platform–independent system and test models. The approach is capable of test–driven development on the model level, and guarantees high quality system and test models by checking consistency and coverage. Additionally, Telling TestStories provides full traceability between the requirements, the system and test models, and the executable services of the system. The tool environment supports these features in an integrated and clear way.

Towards Risk -- Driven Security Testing of Service Centric Systems

The increased deployment of service centric systems in security critical application contexts poses new challenges to properly test such a systems security. If taking a closer look at the inherent complexity of such applications, sophisticated approaches to testing security are indispensable. In our paper we propose a novel model - based methodology for the risk - driven security testing of service centric systems.

Cloud risk analysis by textual models

The development of secure software systems strongly relies on the availability of a known risk profile. In cloud computing, such a known risk profile does not exist yet, resulting in highly insecure cloud deployments. In our paper we propose a textual modeling language for cloud deployments making it possible to derive a risk profile using a risk analysis, based on stable model semantics.

Towards Adaptive Test Code Generation for Service Oriented Systems

With the increasing number of service oriented system implementations, new challenges concerning their development and testing are emerging.This paper presents an approach for model--driven system testing of service oriented systems. The approach offers a systematic testing methodology and it is based on tightly integrated system and test models with a formal metamodel. The test code generation itself is supported by automatic consistency and coverage checks and has a flexible adapter concept that allows different target technologies to be integrated.

Model-based regression testing by OCL

Model-based testing has gained widespread acceptance over the last decade, not only in academia but also in industry. Despite its powerful features of abstraction and automation, most existing approaches and tools provide only limited support for regression testing. Yet regression testing, the repeated execution of selected test cases after system modification, is vital, because changes may introduce new bugs or unwanted side effects that must be avoided at all costs. Model-based testing’s potential for supporting regression testing has yet to be explored, even though syntactic and semantic abstractions within software models already allow identifying changes in software systems. This change information can easily be used for test case selection. In this article, we present a model-based regression testing method based on OCL. By means of a running example using the UML Testing Profile we show how our method supports regression testing on the basis of an existing model-based testing method.

Security Risk Analysis by Logic Programming

In recent years, the complexity of software systems has increased drastically. But methods for assuring their security by testing failed to keep up with this increased complexity. As a result, currently security testing at the most is done by penetration testing, which lacks an underlying structured method. Thus, in this paper we propose an automated risk analysis by logic programming, whose results are valuable in doing structured security testing by additionally focusing on non-functional requirements, a main source for security bugs.