A new test framework for communications-critical large scale systems

None of today’s large scale systems could function without the reliable availability of a varied range of network communications capabilities. Whilst software, hardware and communications technologies have been advancing throughout the past two decades, the methods commonly used by industry for testing large scale systems which incorporate critical communications interfaces have not kept pace. This paper argues for the need for a specifically tailored framework to achieve effective and precise testing of communications-critical large scale systems (CCLSSs). The paper briefly discusses how generic test approaches are leading to inefficient and costly test activities in industry. The paper then outlines the features of an alternative CCLSS domain-specific test framework, and then provides an example based on a real case study. The paper concludes with an evaluation of the benefits observed during the case study and an outline of the available evidence that such benefits can be realized with other comparable systems

Forecasting Recharging Demand to Integrate Electric Vehicle Fleets in Smart Grids

Electric vehicle fleets and smart grids are two growing technologies. These technologies provided new possibilities to reduce pollution and increase energy efficiency. In this sense, electric vehicles are used as mobile loads in the power grid. A distributed charging prioritization methodology is proposed in this paper. The solution is based on the concept of virtual power plants and the usage of evolutionary computation algorithms. Additionally, the comparison of several evolutionary algorithms, genetic algorithm, genetic algorithm with evolution control, particle swarm optimization, and hybrid solution are shown in order to evaluate the proposed architecture. The proposed solution is presented to prevent the overload of the power grid

Budgetary institutions and expenditure outcomes : binding governments to fiscal performance

The authors examine how institutional arrangements affect incentives that govern the size, allocation, and use of budgetary resources. They use a diagnostic questionnaire to elicit the relative strengths and weaknesses of specific systems in terms of instilling fiscal discipline, strategically assigning spending priorities, and making the best use of limited resources. In applying their methodology to a sample of seven countries (Australia, Ghana, Indonesia, Malawi, New Zealand, Thailand and Uganda) they also examine how donor assistance affects expenditure outcomes. In New Zealand, reform focused on achieving general fiscal discipline and technical efficiency. In Australia, reform focused on strategic priorities and a shift from central to line agencies. The two countries took different paths, but both sought to alter incentives that affect the size, allocation, and use of resources and to improve transparency and accountability. Systems in Indonesia and Thailand were reasonably effective in instilling fiscal discipline, butIndonesia seemed better at allocating resources to protect basic social services and alleviate poverty during fiscal austerity periods. Thailand's overcentralized system did not capitalize on useful information from line agencies and lower levels of government. Donors play a central role in spending outcome in the three African countries. Donors provided incentives for short-term fiscal discipline, but the imposed spending cuts impeded the prioritizing of expenditures and multiple donor projects fragmented budgets. Donor conditionality on the composition of expenditures and donor-driven attempts to improve technical efficiency, were ineffective. Lack of transparency and accountability meant rules were not enforced and budgets were often remade in an ad hoc, centralized way, so that the flow of resources to line agencies was unpredictable.Business Environment,Decentralization,Environmental Economics&Policies,Public Sector Economics&Finance,Health Economics&Finance,Poverty Assessment,Health Economics&Finance,National Governance,Public Sector Economics&Finance,Environmental Economics&Policies

Where Are My Intelligent Assistant's Mistakes? A Systematic Testing Approach

Intelligent assistants are handling increasingly critical tasks, but until now, end users have had no way to systematically assess where their assistants make mistakes. For some intelligent assistants, this is a serious problem: if the assistant is doing work that is important, such as assisting with qualitative research or monitoring an elderly parent’s safety, the user may pay a high cost for unnoticed mistakes. This paper addresses the problem with WYSIWYT/ML (What You See Is What You Test for Machine Learning), a human/computer partnership that enables end users to systematically test intelligent assistants. Our empirical evaluation shows that WYSIWYT/ML helped end users find assistants’ mistakes significantly more effectively than ad hoc testing. Not only did it allow users to assess an assistant’s work on an average of 117 predictions in only 10 minutes, it also scaled to a much larger data set, assessing an assistant’s work on 623 out of 1,448 predictions using only the users’ original 10 minutes’ testing effort

Search algorithms for regression test case prioritization

Regression testing is an expensive, but important, process. Unfortunately, there may be insufficient resources to allow for the re-execution of all test cases during regression testing. In this situation, test case prioritisation techniques aim to improve the effectiveness of regression testing, by ordering the test cases so that the most beneficial are executed first. Previous work on regression test case prioritisation has focused on Greedy Algorithms. However, it is known that these algorithms may produce sub-optimal results, because they may construct results that denote only local minima within the search space. By contrast, meta-heuristic and evolutionary search algorithms aim to avoid such problems. This paper presents results from an empirical study of the application of several greedy, meta-heuristic and evolutionary search algorithms to six programs, ranging from 374 to 11,148 lines of code for 3 choices of fitness metric. The paper addresses the problems of choice of fitness metric, characterisation of landscape modality and determination of the most suitable search technique to apply. The empirical results replicate previous results concerning Greedy Algorithms. They shed light on the nature of the regression testing search space, indicating that it is multi-modal. The results also show that Genetic Algorithms perform well, although Greedy approaches are surprisingly effective, given the multi-modal nature of the landscape