Software testing requires the use of a model to guide such efforts as test selection and test verification. Often, such models are implicit, existing only in the head of a human tester, applying test inputs in an ad hoc fashion. The mental model testers build encapsulates application behavior, allowing testers to understand the application's capabilities and more effectively test its range of possible behaviors. When these mental models are written down, they become sharable, reusable testing artifacts. In this case, testers are performing what has become to be known as model-based testing. Model-based testing has recently gained attention with the popularization of models (including UML) in software design and development. There are a number of models of software in use today, a few of which make good models for testing. This paper introduces model-based testing and discusses its tasks in general terms with finite state models (arguably the most popular software models) as examples. In addition, advantages, difficulties, and shortcoming of various model-based approaches are concisely presented. Finally, we close with a discussion of where model-based testing fits in the present and future of software engineering.
Outbreaks of computer viruses and worms have established a pressing need for developing proactive antivirus solutions. A proactive antivirus solution is one that reliably and accurately detects novel malicious mobile code and one that either prevents damage or recovers systems from the damage that such code inflicts. Research has indicated that behavioral analysis, though provably imprecise, can feasibly predict whether novel behavior poses a threat. Nevertheless, even the most reliable detection methods can conceivably misclassify malicious code or deem it harmful only after substantial damage has taken place. The study of damage control and recovery mechanisms is, therefore, clearly essential to the development of better proactive systems. Earlier work has demonstrated that undoing the damage of malicious code is possible with an appropriate behavior monitoring and recording mechanism. However, it remains that even if a system is recovered, the virulent code may have already propagated to other systems, some of which may not be well-equipped in terms of proactive defenses. Curbing the propagation of undesired code once it has left the boundaries of a system is a hard problem and one that has not received much attention. This work focuses on a specific instance of this difficult problem: viruses and worms that spread by email. In this paper, we explore how advantageous it would be to have a The authors would like to thank the Cisco Critical Infrastructure Assurance Group for their support in developing Hephaestus, and the Office of Naval Research (Award Number N00014-01-1-0862) for support in the ongoing development of Gatekeeper.
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