Sensitivity Analysis for a Scenario-Based Reliability Prediction Model

As a popular means for capturing behavioural requirements, scenariosshow how components interact to provide system-level functionality.If component reliability information is available, scenarioscan be used to perform early system reliability assessment. Inprevious work we presented an automated approach for predictingsoftware system reliability that extends a scenario specificationto model (1) the probability of component failure, and (2) scenariotransition probabilities. Probabilistic behaviour models ofthe system are then synthesized from the extended scenario specification.From the system behaviour model, reliability predictioncan be computed. This paper complements our previous work andpresents a sensitivity analysis that supports reasoning about howcomponent reliability and usage profiles impact on the overall systemreliability. For this purpose, we present how the system reliabilityvaries as a function of the components reliabilities and thescenario transition probabilities. Taking into account the concurrentnature of component-based software systems, we also analysethe effect of implied scenarios prevention into the sensitivity analysisof our reliability prediction technique

Reliability prediction in model driven development

Evaluating the implications of an architecture design early in the software development lifecycle is important in order to reduce costs of development. Reliability is an important concern with regard to the correct delivery of software system service. Recently, the UML Profile for Modeling Quality of Service has defined a set of UML extensions to represent dependability concerns (including reliability) and other non-functional requirements in early stages of the software development lifecycle. Our research has shown that these extensions are not comprehensive enough to support reliability analysis for model-driven software engineering, because the description of reliability characteristics in this profile lacks support for certain dynamic aspects that are essential in modeling reliability. In this work, we define a profile for reliability analysis by extending the UML 2.0 specification to support reliability prediction based on scenario specifications. A UML model specified using the profile is translated to a labelled transition system (LTS), which is used for automated reliability prediction and identification of implied scenarios; the results of this analysis are then fed back to the UML model. The result is a comprehensive framework for addressing software reliability modeling, including analysis and evolution of reliability predictions. We exemplify our approach using the Boiler System used in previous work and demonstrate how reliability analysis results can be integrated into UML models

Controllability in partial and uncertain environments

© 2014 IEEE.Controller synthesis is a well studied problem that attempts to automatically generate an operational behaviour model of the system-to-be that satisfies a given goal when deployed in a given domain model that behaves according to specified assumptions. A limitation of many controller synthesis techniques is that they require complete descriptions of the problem domain. This is limiting in the context of modern incremental development processes when a fully described problem domain is unavailable, undesirable or uneconomical. Previous work on Modal Transition Systems (MTS) control problems exists, however it is restricted to deterministic MTSs and deterministic Labelled Transition Systems (LTS) implementations. In this paper we study the Modal Transition System Control Problem in its full generality, allowing for nondeterministic MTSs modelling the environments behaviour and nondeterministic LTS implementations. Given an nondeterministic MTS we ask if all, none or some of the nondeterministic LTSs it describes admit an LTS controller that guarantees a given property. We show a technique that solves effectively the MTS realisability problem and it can be, in some cases, reduced to deterministic control problems. In all cases the MTS realisability problem is in same complexity class as the corresponding LTS problem

Introduction to doctoral symposium

Submitted versio

Monitoring and control in scenario-based requirements analysis

Scenarios are an effective means for eliciting, validating and documenting requirements. At the requirements level, scenarios describe sequences of interactions between the software-to-be and agents in the environment. Interactions correspond to the occurrence of an event that is controlled by one agent and monitored by another.This paper presents a technique to analyse requirements-level scenarios for unforeseen, potentially harmful, consequences. Our aim is to perform analysis early in system development, where it is highly cost-effective. The approach recognises the importance of monitoring and control issues and extends existing work on implied scenarios accordingly. These so-called input-output implied scenarios expose problematic behaviours in scenario descriptions that cannot be detected using standard implied scenarios. Validation of these implied scenarios supports requirements elaboration. We demonstrate the relevance of input-output implied scenarios using a number of examples

Robust degradation and enhancement of robot mission behaviour in unpredictable environments

© 2015 ACM.Temporal logic based approaches that automatically generate controllers have been shown to be useful for mission level planning of motion, surveillance and navigation, among others. These approaches critically rely on the validity of the environment models used for synthesis. Yet simplifying assumptions are inevitable to reduce complexity and provide mission-level guarantees; no plan can guarantee results in a model of a world in which everything can go wrong. In this paper, we show how our approach, which reduces reliance on a single model by introducing a stack of models, can endow systems with incremental guarantees based on increasingly strengthened assumptions, supporting graceful degradation when the environment does not behave as expected, and progressive enhancement when it does

Enhancing Architectural Mismatch Detection with Assumptions

Detecting software architecture inconsistencies is a critical issue in software design. Software systems are described in terms of components, component behavior and interaction and mismatch detection is explored through techniques based on behavior analysis. Integration problems, however, are not only caused by behavioral mismatch: components make assumptions about their environment to guarantee functional and non-functional properties. If the actual deployment environment of each component does not satisfy its assumptions, component and system properties may not hold. In this work we propose to extend the idea of architectural mismatch to include the notion of assumption. We concentrate on a subset of possible assumptions and show how software architects can benefit from using them. We also present a discussion on how architecture description languages (ADLs) can be extended to include assumptions. 1

Iterator-based temporal logic task planning

Temporal logic task planning for robotic systemssuffers from state explosion when specifications involve largenumbers of discrete locations. We provide a novel approach,particularly suited for tasks specifications with universallyquantified locations, that has constant time with respect tothenumber of locations, enabling synthesis of plans for an arbitrarynumber of them. We propose a hybrid control framework thatuses an iterator to manage the discretised workspace hidingitfrom a plan enacted by a discrete event controller. A downsideof our approach is that it incurs in increased overhead whenexecuting a synthesised plan. We demonstrate that the overheadis reasonable for missions of a fixed-wing Unmanned AerialVehicle in simulated and real scenarios for up to700 000locations

Using contexts to extract models from code

Behaviour models facilitate the understanding and analysis of software systems by providing an abstract view of their behaviours and also by enabling the use of validation and verification techniques to detect errors. However, depending on the size and complexity of these systems, constructing models may not be a trivial task, even for experienced developers. Model extraction techniques can automatically obtain models from existing code, thus reducing the effort and expertise required of engineers and helping avoid errors often present in manually constructed models. Existing approaches for model extraction often fail to produce faithful models, either because they only consider static information, which may include infeasible behaviours, or because they are based only on dynamic information, thus relying on observed executions, which usually results in incomplete models. This paper describes a model extraction approach based on the concept of contexts, which are abstractions of concrete states of a program, combining static and dynamic information. Contexts merge some of the advantages of using either type of information and, by their combination, can overcome some of their problems. The approach is partially implemented by a tool called LTS Extractor, which translates information collected from execution traces produced by instrumented Java code to labelled transition systems (LTS), which can be analysed in an existing verification tool. Results from case studies are presented and discussed, showing that, considering a certain level of abstraction and a set of execution traces, the produced models are correct descriptions of the programs from which they were extracted. Thus, they can be used for a variety of analyses, such as program understanding, validation, verification, and evolution