Role of Testers in Selecting an Enterprise Architecture Solution: An Exploratory Study

Software testing groups are playing an increasingly prominent role in both the software development lifecycle (SDLC) and in the long-term planning of technology architectures that support large-scale organizational information systems. The advent of integrated enterprise architectures (EA) provides new opportunities for testing groups to play a proactive role in building consistent and testable guidelines for improving enterprise-wide software quality. Given that testing groups historically have not been invited to participate in EA decisions, there is little academic literature or industry best practices on approaches that testers might use to guide their participation. This article draws lessons from the experience of a Fortune 100 corporation whose testing group used theoretical notions of “testability” to guide its involvement in an EA acquisition process. It describes how it operationalized testability criteria, incorporating controllability, observability, and simplicity, into various stages of the process and illustrates the benefits and challenges of taking such an approach

Informativity of noisy data for structural properties of linear systems

This paper deals with developing tests for checking whether an unknown system has certain structural properties. The tests that we are aiming at are in terms of noisy input-state-output data obtained from the unknown system. Since, in general, the data do not determine the unknown system uniquely, many systems are compatible with the same set of data. Therefore we can not apply system identification and apply existing, model based, tests. Instead, we will use the concept of informativity, and establish tests for informativity of the given noisy data. We will do this for a range of system properties, among which strong observability and detectability and strong controllability and stabilizability. These informativity tests will be in terms of rank tests on polynomial matrices that can be constructed from the noisy data. We will also set up a geometric framework for informativity analysis. Within that framework we will give geometric tests for informativity for strong observability, observability, and left-invertibilty

Information-theoretic approach to the study of control systems

We propose an information-theoretic framework for analyzing control systems based on the close relationship of controllers to communication channels. A communication channel takes an input state and transforms it into an output state. A controller, similarly, takes the initial state of a system to be controlled and transforms it into a target state. In this sense, a controller can be thought of as an actuation channel that acts on inputs to produce desired outputs. In this transformation process, two different control strategies can be adopted: (i) the controller applies an actuation dynamics that is independent of the state of the system to be controlled (open-loop control); or (ii) the controller enacts an actuation dynamics that is based on some information about the state of the controlled system (closed-loop control). Using this communication channel model of control, we provide necessary and sufficient conditions for a system to be perfectly controllable and perfectly observable in terms of information and entropy. In addition, we derive a quantitative trade-off between the amount of information gathered by a closed-loop controller and its relative performance advantage over an open-loop controller in stabilizing a system. This work supplements earlier results [H. Touchette, S. Lloyd, Phys. Rev. Lett. 84, 1156 (2000)] by providing new derivations of the advantage afforded by closed-loop control and by proposing an information-based optimality criterion for control systems. New applications of this approach pertaining to proportional controllers, and the control of chaotic maps are also presented.Comment: 18 pages, 7 eps figure

SOFTWARE TESTABILITY MEASURE FOR SAE ARCHITECTURE ANALYSIS AND DESIGN LANGUAGE (AADL)SOFTWARE TESTABILITY MEASURE FOR SAE ARCHITECTURE ANALYSIS AND DESIGN LANGUAGE (AADL)

Testability is an important quality attribute of software, especially for critical systems such as avionics, medical, and automotive. Improvement in the early testability of software architecture, the first artifact of the software system, will help reduce issues and costs later in the development process. AADL, an architecture analysis description language suitable for critical embedded, real-time systems, can be used for design documentation, analysis and code generation. Because the capability of AADL can be extended, it is possible to add new analyses to its core language. Tools such as the Open Source AADL Tool Environment (OSATE) provide plugins for processing AADL models. Although adding new plugins in OSATE extends AADL, there currently exists no AADL extension for testability measurement. The purpose of this thesis is to propose such a method to measure the testability of AADL models as well as to develop a testability plugin in OSATE. Much research has been conducted on testability of hardware, software and embedded systems, resulting in several approaches for measuring this quality attribute. Among them, the approach measuring testability as a product of controllability and observability using information transfer graph (ITG) is the most applicable for measuring the testability of AADL models. This thesis proposes a method applying this approach to AADL models. A complete testability measure plugin for OSATE was developed based on this approach and detailed examples are given in this thesis to demonstrate its applicability

Model-Based Testing for the Cloud

Software in the cloud is characterised by the need to be highly adaptive and continuously available. Incremental changes are applied to the deployed system and need to be tested in the field. Different configurations need to be tested. Higher quality standards regarding both functional and non-functional properties are put on those systems, as they often face large and diverse customer bases and/or are used as services from different peer service implementations. The properties of interest include interoperability, privacy, security, reliability, performance, resource use, timing constraints, service dependencies, availability, and so on. This paper discusses the state of the art in model-based testing of cloud systems. It focuses on two central aspects of the problem domain: (a) dealing with the adaptive and dynamic character of cloud software when tested with model-based testing, by developing new online and offline test strategies, and (b) dealing with the variety of modeling concerns for functional and non-functional properties, by devising a unified framework for them where this is possible. Having discussed the state of the art we identify challenges and future directions

Observability/Identifiability of Rigid Motion under Perspective Projection

The "visual motion" problem consists of estimating the motion of an object viewed under projection. In this paper we address the feasibility of such a problem. We will show that the model which defines the visual motion problem for feature points in the euclidean 3D space lacks of both linear and local (weak) observability. The locally observable manifold is covered with three levels of lie differentiations. Indeed, by imposing metric constraints on the state-space, it is possible to reduce the set of indistinguishable states. We will then analyze a model for visual motion estimation in terms of identification of an Exterior Differential System, with the parameters living on a topological manifold, called the "essential manifold", which includes explicitly in its definition the forementioned metric constraints. We will show that rigid motion is globally observable/identifiable under perspective projection with zero level of lie differentiation under some general position conditions. Such conditions hold when the viewer does not move on a quadric surface containing all the visible points

Controllability of structural brain networks.

Cognitive function is driven by dynamic interactions between large-scale neural circuits or networks, enabling behaviour. However, fundamental principles constraining these dynamic network processes have remained elusive. Here we use tools from control and network theories to offer a mechanistic explanation for how the brain moves between cognitive states drawn from the network organization of white matter microstructure. Our results suggest that densely connected areas, particularly in the default mode system, facilitate the movement of the brain to many easily reachable states. Weakly connected areas, particularly in cognitive control systems, facilitate the movement of the brain to difficult-to-reach states. Areas located on the boundary between network communities, particularly in attentional control systems, facilitate the integration or segregation of diverse cognitive systems. Our results suggest that structural network differences between cognitive circuits dictate their distinct roles in controlling trajectories of brain network function