On Evidence-based Risk Management in Requirements Engineering

Background: The sensitivity of Requirements Engineering (RE) to the context makes it difficult to efficiently control problems therein, thus, hampering an effective risk management devoted to allow for early corrective or even preventive measures. Problem: There is still little empirical knowledge about context-specific RE phenomena which would be necessary for an effective context- sensitive risk management in RE. Goal: We propose and validate an evidence-based approach to assess risks in RE using cross-company data about problems, causes and effects. Research Method: We use survey data from 228 companies and build a probabilistic network that supports the forecast of context-specific RE phenomena. We implement this approach using spreadsheets to support a light-weight risk assessment. Results: Our results from an initial validation in 6 companies strengthen our confidence that the approach increases the awareness for individual risk factors in RE, and the feedback further allows for disseminating our approach into practice.Comment: 20 pages, submitted to 10th Software Quality Days conference, 201

Development of strategies for effective communication of food risks and benefits across Europe: Design and conceptual framework of the FoodRisC project

The FoodRisC project is funded under the Seventh Framework Programme (CORDIS FP7) of the European Commission; Grant agreement no.: 245124. Copyright @ 2011 Barnett et al.BACKGROUND: European consumers are faced with a myriad of food related risk and benefit information and it is regularly left up to the consumer to interpret these, often conflicting, pieces of information as a coherent message. This conflict is especially apparent in times of food crises and can have major public health implications. Scientific results and risk assessments cannot always be easily communicated into simple guidelines and advice that non-scientists like the public or the media can easily understand especially when there is conflicting, uncertain or complex information about a particular food or aspects thereof. The need for improved strategies and tools for communication about food risks and benefits is therefore paramount. The FoodRisC project ("Food Risk Communication - Perceptions and communication of food risks/benefits across Europe: development of effective communication strategies") aims to address this issue. The FoodRisC project will examine consumer perceptions and investigate how people acquire and use information in food domains in order to develop targeted strategies for food communication across Europe.METHODS/DESIGN: This project consists of 6 research work packages which, using qualitative and quantitative methodologies, are focused on development of a framework for investigating food risk/benefit issues across Europe, exploration of the role of new and traditional media in food communication and testing of the framework in order to develop evidence based communication strategies and tools. The main outcome of the FoodRisC project will be a toolkit to enable coherent communication of food risk/benefit messages in Europe. The toolkit will integrate theoretical models and new measurement paradigms as well as building on social marketing approaches around consumer segmentation. Use of the toolkit and guides will assist policy makers, food authorities and other end users in developing common approaches to communicating coherent messages to consumers in Europe.DISCUSSION: The FoodRisC project offers a unique approach to the investigation of food risk/benefit communication. The effective spread of food risk/benefit information will assist initiatives aimed at reducing the burden of food-related illness and disease, reducing the economic impact of food crises and ensuring that confidence in safe and nutritious food is fostered and maintained in Europe.This article is available through the Brunel Open Access Publishing Fund

An Assessment of PIER Electric Grid Research 2003-2014 White Paper

This white paper describes the circumstances in California around the turn of the 21st century that led the California Energy Commission (CEC) to direct additional Public Interest Energy Research funds to address critical electric grid issues, especially those arising from integrating high penetrations of variable renewable generation with the electric grid. It contains an assessment of the beneficial science and technology advances of the resultant portfolio of electric grid research projects administered under the direction of the CEC by a competitively selected contractor, the University of California’s California Institute for Energy and the Environment, from 2003-2014

Computational simulation for concurrent engineering of aerospace propulsion systems

Results are summarized for an investigation to assess the infrastructure available and the technology readiness in order to develop computational simulation methods/software for concurrent engineering. These results demonstrate that development of computational simulation methods for concurrent engineering is timely. Extensive infrastructure, in terms of multi-discipline simulation, component-specific simulation, system simulators, fabrication process simulation, and simulation of uncertainties--fundamental to develop such methods, is available. An approach is recommended which can be used to develop computational simulation methods for concurrent engineering of propulsion systems and systems in general. Benefits and issues needing early attention in the development are outlined

Understanding Model-Based Reinforcement Learning and its Application in Safe Reinforcement Learning

Model-based reinforcement learning algorithms have been shown to achieve successful results on various continuous control benchmarks, but the understanding of model-based methods is limited. We try to interpret how model-based method works through novel experiments on state-of-the-art algorithms with an emphasis on the model learning part. We evaluate the role of the model learning in policy optimization and propose methods to learn a more accurate model. With a better understanding of model-based reinforcement learning, we then apply model-based methods to solve safe reinforcement learning (RL) problems with near-zero violation of hard constraints throughout training. Drawing an analogy with how humans and animals learn to perform safe actions, we break down the safe RL problem into three stages. First, we train agents in a constraint-free environment to learn a performant policy for reaching high rewards, and simultaneously learn a model of the dynamics. Second, we use model-based methods to plan safe actions and train a safeguarding policy from these actions through imitation. Finally, we propose a factored framework to train an overall policy that mixes the performant policy and the safeguarding policy. This three-step curriculum ensures near-zero violation of safety constraints at all times. As an advantage of model-based method, the sample complexity required at the second and third steps of the process is significantly lower than model-free methods and can enable online safe learning. We demonstrate the effectiveness of our methods in various continuous control problems and analyze the advantages over state-of-the-art approaches

Evaluating Workforce Programs: A Guide to What Policymakers Need to Know to Structure Effective, User-Friendly Evaluations

This brief discusses the value and purpose of program evaluations, highlights different evaluation tools and techniques, and illustrates how policy makers and program managers can structure and implement evaluations of workforce development programs

The Precautionary Principle (with Application to the Genetic Modification of Organisms)

We present a non-naive version of the Precautionary (PP) that allows us to avoid paranoia and paralysis by confining precaution to specific domains and problems. PP is intended to deal with uncertainty and risk in cases where the absence of evidence and the incompleteness of scientific knowledge carries profound implications and in the presence of risks of "black swans", unforeseen and unforeseable events of extreme consequence. We formalize PP, placing it within the statistical and probabilistic structure of ruin problems, in which a system is at risk of total failure, and in place of risk we use a formal fragility based approach. We make a central distinction between 1) thin and fat tails, 2) Local and systemic risks and place PP in the joint Fat Tails and systemic cases. We discuss the implications for GMOs (compared to Nuclear energy) and show that GMOs represent a public risk of global harm (while harm from nuclear energy is comparatively limited and better characterized). PP should be used to prescribe severe limits on GMOs

A critical comparison of using a probabilistic weather generator versus a change factor approach: irrigation reservoir planning under climate change

In the UK, there is a growing interest in constructing on-farm irrigation reservoirs, however deciding the optimum reservoir capacity is not simple. There are two distinct approaches to generating the future daily weather datasets needed to calculate future irrigation need. The change factor approach perturbs the observed record using monthly change factors derived from downscaled climate models. This assumes that whilst the climate changes, the day-to-day climate variability itself is stationary. Problems may arise where the instrumental record is insufficient or particularly suspect. Alternatively, probabilistic weather generators can be used to identify options which are considered more robust to climate change uncertainty because they consider non-stationary climate variability. This paper explores the difference between using the change factor approach and a probabilistic weather generator for informing farm reservoir design at three sites in the UK. Decision outcomes obtained using the current normal practice of 80% probability of non-exceedance rule and simple economic optimisations are also compared. Decision outcomes obtained using the change factor approach and probabilistic weather generators are significantly different; whether these differences translate to real-world differences is discussed. This study also found that using the 80% probability of non-exceedance rule could potentially result in maladaptation

Departures from cost-effectiveness recommendations: The impact of health system constraints on priority setting

The methods and application of cost-effectiveness analysis have reached an advanced stage of development. Many decision makers consider cost-effectiveness analysis to be a valid and feasible approach towards setting health priorities, and it has been extensively applied in evaluating interventions and developing evidence based clinical guidelines. However, the recommendations arising from cost-effectiveness analysis are often not implemented as intended. A fundamental reason for the failure to implement is that CEA assumes a single constraint, in the form of the budget constraint, whilst in reality decision-makers may be faced with numerous other constraints. The objective of this paper is to develop a typology of constraints that may act as barriers to implementation of cost-effectiveness recommendations. Six categories of constraints are considered: the design of the health system; costs of implementing change; system interactions between interventions; uncertainty in estimates of costs and benefits; weak governance; and political constraints. Where possible -and if applicable- for each class of constraint, the paper discusses ways in which these constraints can be taken into account by a decision maker wishing to pursue the principles of cost-effectiveness