Modular verification of dynamically adaptive systems
Cyber-physical systems increasingly rely on dynamically adaptive programs to respond to changes in their physical environment; examples include ecosystem monitoring and disaster relief systems. These systems are considered high-assurance since errors during execution could result in injury, loss of life, environmental impact, and/or financial loss. In order to facilitate the development and verification of dynamically adaptive systems, we separate functional concerns from adaptive concerns. Specifically, we model a dynamically adaptive program as a collection of (non-adaptive) steady-state programs and a set of adaptations that realize transitions among steady state programs in response to environmental changes. We use Linear Temporal Logic (LTL) to specify properties of the non-adaptive portions of the system, and we use A-LTL (an adapt-operator extension toLTL) to concisely specify properties that hold during the adaptation process. Model checking offers an attractive approach to automatically analyzing models for adherence to formal properties and thus providing assurance. However, currently, model checkers are unable to verify properties specified using A-LTL. Moreover, as the number of steady-state programs and adaptations increase, the verification costs (in terms of space and time) potentially become unwieldy. To address these issues, we propose a modular model checking approach to verifying that a formal model of an adaptive program satisfies its requirements specified in LTL and A-LTL, respectively.

title: AMOEBA-RT: Run-Time Verification of Adaptive Software
abstract: Increasingly, software must dynamically adapt its behavior in response to changes in the supporting computing, communication infrastructure, and in the surrounding physical environment. Assurance that the adaptive software correctly satisfies its requirements is crucial if the software is to be used in high assurance systems, such as command and control or critical infrastructure protection systems. Adaptive software development for these systems must be grounded upon formalism and rigorous software engineering methodology to gain assurance. In this paper, we briefly describe AMOebA-RT , a run-time monitoring and verification technique that provides assurance that dynamically adaptive software satisfies its requirements.

title: Automating change evolution in model-driven engineering
abstract: The escalating complexity of software and system models is making it difficult to rapidly explore the effects of a design decision. Automating such exploration with model transformation and aspect-oriented techniques can improve both productivity and model quality. The combination of model transformation and aspect weaving provides a powerful technology for rapidly transforming legacy systems from the high-level properties that models describe. Further, by applying aspect-oriented techniques and program transformation, small changes at the modeling level can trigger very large transformations at the source code level. Thus, model engineers can explore alternative configurations using an aspect weaver targeted for modeling tools and then use the models to generate program transformation rules for adapting legacy source code on a wide scale.

title: Autonomous Objects and Bottom-Up Composition in ZOO Applied to a Case Study of Biological Reactivity
abstract: As part of our work on the formal analysis of object-oriented models, we turn to systems where many autonomous individuals interact to give rise to complex collective behaviour. We adapt our ZOO [1,2] structuring and apply it to a case study based on a published model of part of the immune system . The formalisation calls for a bottom-up solution with no central control over individual units, and includes an approach to represent feedback channelsenabling broadcast communication between individuals and across levels.

title: The transient solution of time-dependent M/M/1 queues
abstract: The transient behavior of time-dependent M/M/1 queues is studied. The boundary probability function pi /sub 0/(t), which is the probability that the queue is empty at time t, is shown with analyticity arguments to satisfy a Volterra-type integral equation. The boundary integral equation is derived, and a numerical algorithm is used to solve the integral equation and to find the expected queue size from pi /sub 0/(t). The approach can be applied to many other types of time-dependent queues. Examples are given.

title: A model-driven approach to enforce crosscutting assertion checking
abstract: Design by Contract provides an effective principle to enable the construction of robust software by describing properties of a module using logical assertions. This paper presents a model-driven approach for weaving assertion checking aspects into a large software system. The approach is based on a technique called two-level aspect weaving. At the top level, crosscutting assertions are weaved into a model by use of a model weaver. The second step of the weaving process occurs when the Model-Driven Program Transformation technique is applied to perform large-scale adaptation of the underlying source code from the contracts specified in the high-level models. The paper briefly presents a case study to illustrate the concept.

title: Systematic benchmarking of microarray data feature extraction and classification
abstract: A combination of microarrays with classification methods is a promising approach to supporting clinical management decisions in oncology. The aim of this paper is to systematically benchmark the role of classification models. Each classification model is a combination of one feature extraction method and one classification method. We consider four feature extraction methods and five classification methods, from which 20 classification models can be derived. The feature extraction methods are t-statistics, non-parametric Wilcoxon statistics, ad hoc signal-to-noise statistics, and principal component analysis (PCA), and the classification methods are Fisher linear discriminant analysis (FLDA), the support vector machine (SVM), the k nearest-neighbour classifier (kNN), diagonal linear discriminant analysis (DLDA), and diagonal quadratic discriminant analysis (DQDA). Twenty randomizations of each of three binary cancer classification problems derived from publicly available datasets are examined. PCA plus FLDA is found to be the optimal classification model.