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Model-based testing (MBT) approaches help automatically generate test cases using models extracted from software artifacts, and hold the promise to greatly affect how we build software. A review of the literature shows that certain specialized domains are applying MBT, but it does not yet seem to be a mainstream approach. The authors therefore conducted a systematic review of the literature to investigate how much evidence is available on MBT's costs and benefits, especially regarding how these techniques compare to other common testing approaches. They use these results to derive suggestions regarding what types of studies might further increase the deployment of these techniques

This paper describes an ongoing research on test case generation based on Unified Modeling Language (UML). The described approach builds on and combines existing techniques for data and graph coverage. It first uses the Category-Partition method to introduce data into the UML model. UML Use Cases and Activity diagrams are used to respectively describe which functionalities should be tested and how to test them. This combination has the potential to create a very large number of test cases. This approach offers two ways to manage the number of tests. First, custom annotations and guards use the CategoryPartition data which allows the designer tight control over possible, or impossible, paths. Second, automation allows different configurations for both the data and the graph coverage. The process of modeling UML activity diagrams, annotating them with test data requirements, and generating test scripts from the models is described. The goal of this paper is to illustrate the benefits of our model-based approach for improving automation on software testing. The approach is demonstrated and evaluated based on use cases developed for testing a graphical user interface (GUI).

Traditional centralized monitoring systems do not scale to present-day large, complex, networkcomputing systems. Based o n r ecent SNMP standards for distributed management, this paper addresses the scalability problem through distribution of monitoring tasks, applicable for tools such as SI-MONE (SNMP-based monitoring prototype implemented by the authors).Distribution is achieved b y i n t r oducing one or more levels of a dual entity called the Intermediate Level Manager (ILM) between a manager and the agents. The ILM accepts monitoring tasks described i n t h e form of scripts and delegated by the next higher entity. The solution is exible and integratable into a SNMP tool without altering other system components.A testbed of up to 1024 monitoring elements is used to assess scalability. Noticeable improvements in the round trip delay (from seconds to less than tenth of a second) were observed when more than 200 monitoring elements are p r esent and as few as 2 ILM's are used.

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