Applying SMT Solvers to the Test Template Framework

The Test Template Framework (TTF) is a model-based testing method for the Z notation. In the TTF, test cases are generated from test specifications, which are predicates written in Z. In turn, the Z notation is based on first-order logic with equality and Zermelo-Fraenkel set theory. In this way, a test case is a witness satisfying a formula in that theory. Satisfiability Modulo Theory (SMT) solvers are software tools that decide the satisfiability of arbitrary formulas in a large number of built-in logical theories and their combination. In this paper, we present the first results of applying two SMT solvers, Yices and CVC3, as the engines to find test cases from TTF's test specifications. In doing so, shallow embeddings of a significant portion of the Z notation into the input languages of Yices and CVC3 are provided, given that they do not directly support Zermelo-Fraenkel set theory as defined in Z. Finally, the results of applying these embeddings to a number of test specifications of eight cases studies are analysed.Comment: In Proceedings MBT 2012, arXiv:1202.582

Does the routine use of global coronary heart disease risk scores translate into clinical benefits or harms? A systematic review of the literature

<p>Abstract</p> <p>Background</p> <p>Guidelines now recommend routine assessment of global coronary heart disease (CHD) risk scores. We performed a systematic review to assess whether global CHD risk scores result in clinical benefits or harms.</p> <p>Methods</p> <p>We searched MEDLINE (1966 through June 13, 2007) for articles relevant to our review. Using predefined inclusion and exclusion criteria, we included studies of any design that provided physicians with global risk scores or allowed them to calculate scores themselves, and then measured clinical benefits and/or harms. Two reviewers reviewed potentially relevant studies for inclusion and resolved disagreement by consensus. Data from each article was then abstracted into an evidence table by one reviewer and the quality of evidence was assessed independently by two reviewers.</p> <p>Results</p> <p>11 studies met criteria for inclusion in our review. Six studies addressed clinical benefits and 5 addressed clinical harms. Six studies were rated as "fair" quality and the others were deemed "methodologically limited". Two fair quality studies showed that physician knowledge of global CHD risk is associated with increased prescription of cardiovascular drugs in high risk (but not all) patients. Two additional fair quality studies showed no effect on their primary outcomes, but one was underpowered and the other focused on prescribing of lifestyle changes, rather than drugs whose prescribing might be expected to be targeted by risk level. One of these aforementioned studies showed improved blood pressure in high-risk patients, but no improvement in the proportion of patients at high risk, perhaps due to the high proportion of participants with baseline risks significantly exceeding the risk threshold. Two fair quality studies found no evidence of harm from patient knowledge of global risk scores when they were accompanied by counseling, and optional or scheduled follow-up. Other studies were too methodologically limited to draw conclusions.</p> <p>Conclusion</p> <p>Our review provides preliminary evidence that physicians' knowledge of global CHD risk scores may translate into modestly increased prescribing of cardiovascular drugs and modest short-term reductions in CHD risk factors without clinical harm. Whether these results are replicable, and translate across other practice settings or into improved long-term CHD outcomes remains to be seen.</p

The UniForM Workbench, a Universal Development Environment for Formal Methods

The UniForM Workbench supports combination of Formal Methods (on a solid logical foundation), provides tools for the development of hybrid, real-time or reactive systems, transformation, verification, validation and testing. Moreover, it..

To compose, or not to compose, that is the question: an analysis of compositional state space generation

To combat state space explosion several compositional verification approaches have been proposed. One such approach is compositional aggregation, where a given system consisting of a number of parallel components is iteratively composed and minimised. Compositional aggregation has shown to perform better (in the size of the largest state space in memory at one time) than classical monolithic composition in a number of cases. However, there are also cases in which compositional aggregation performs much worse. It is unclear when one should apply compositional aggregation in favor of other techniques and how it is affected by action hiding and the scale of the model. This paper presents a descriptive analysis following the quantitiative experimental approach. The experiments were conducted in a controlled test bed setup in a computer laboratory environment. A total of eight scalable models with different network topologies considering a number of varying properties were investigated comprising 119 subjects. This makes it the most comprehensive study done so far on the topic. We investigate whether there is any systematic difference in the success of compositional aggregation based on the model, scaling, and action hiding. Our results indicate that both scaling up the model and hiding more behaviour has a positive influence on compositional aggregation

Deadlock detection for actor-based coroutines

The actor-based language studied in this paper features asynchronous method calls and supports coroutines which allow for the cooperative scheduling of the method invocations belonging to an actor. We model the local behavior of an actor as a well-structured transition system by means of predicate abstraction and derive the decidability of the occurrence of deadlocks caused by the coroutine mode of method execution

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