133,536 research outputs found

    Supporting Defect Causal Analysis in Practice with Cross-Company Data on Causes of Requirements Engineering Problems

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    [Context] Defect Causal Analysis (DCA) represents an efficient practice to improve software processes. While knowledge on cause-effect relations is helpful to support DCA, collecting cause-effect data may require significant effort and time. [Goal] We propose and evaluate a new DCA approach that uses cross-company data to support the practical application of DCA. [Method] We collected cross-company data on causes of requirements engineering problems from 74 Brazilian organizations and built a Bayesian network. Our DCA approach uses the diagnostic inference of the Bayesian network to support DCA sessions. We evaluated our approach by applying a model for technology transfer to industry and conducted three consecutive evaluations: (i) in academia, (ii) with industry representatives of the Fraunhofer Project Center at UFBA, and (iii) in an industrial case study at the Brazilian National Development Bank (BNDES). [Results] We received positive feedback in all three evaluations and the cross-company data was considered helpful for determining main causes. [Conclusions] Our results strengthen our confidence in that supporting DCA with cross-company data is promising and should be further investigated.Comment: 10 pages, 8 figures, accepted for the 39th International Conference on Software Engineering (ICSE'17

    THE USEFULNESS OF ANALYTICAL TOOLS FOR SUSTAINABLE FUTURES

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    The aim of this study is to assess the usefulness of analytical tools for policy evaluation. The study focuses on a multi-method integrated toolkit, the so-called SMILE toolkit. This toolkit consist of the integration of three evaluation frameworks developed within an EU-funded consortium called Development and Comparison of Sustainability (DECOIN) and further applied within the consortium Synergies in Multi-Scale Inter-Linkages of Eco-social systems (SMILE). This toolkit is developed to provide reporting features that are required for monitoring policy-making. The sustainable development perspective is rather difficult to attempt due to its dynamism and its multi-dimensionality. Therefore, in this study, we aim to assess the usefulness of the SMILE toolkit to sustainable development issues on the basis of the critical factors of sustainable development. In other words, here, we will prove the usefulness of the toolkit to help policymakers to think about and work on sustainable developments in the future.

    A decision support methodology to enhance the competitiveness of the Turkish automotive industry

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    This is the post-print (final draft post-refereeing) version of the article. Copyright @ 2013 Elsevier B.V. All rights reserved.Three levels of competitiveness affect the success of business enterprises in a globally competitive environment: the competitiveness of the company, the competitiveness of the industry in which the company operates and the competitiveness of the country where the business is located. This study analyses the competitiveness of the automotive industry in association with the national competitiveness perspective using a methodology based on Bayesian Causal Networks. First, we structure the competitiveness problem of the automotive industry through a synthesis of expert knowledge in the light of the World Economic Forum’s competitiveness indicators. Second, we model the relationships among the variables identified in the problem structuring stage and analyse these relationships using a Bayesian Causal Network. Third, we develop policy suggestions under various scenarios to enhance the national competitive advantages of the automotive industry. We present an analysis of the Turkish automotive industry as a case study. It is possible to generalise the policy suggestions developed for the case of Turkish automotive industry to the automotive industries in other developing countries where country and industry competitiveness levels are similar to those of Turkey

    Embodied Artificial Intelligence through Distributed Adaptive Control: An Integrated Framework

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    In this paper, we argue that the future of Artificial Intelligence research resides in two keywords: integration and embodiment. We support this claim by analyzing the recent advances of the field. Regarding integration, we note that the most impactful recent contributions have been made possible through the integration of recent Machine Learning methods (based in particular on Deep Learning and Recurrent Neural Networks) with more traditional ones (e.g. Monte-Carlo tree search, goal babbling exploration or addressable memory systems). Regarding embodiment, we note that the traditional benchmark tasks (e.g. visual classification or board games) are becoming obsolete as state-of-the-art learning algorithms approach or even surpass human performance in most of them, having recently encouraged the development of first-person 3D game platforms embedding realistic physics. Building upon this analysis, we first propose an embodied cognitive architecture integrating heterogenous sub-fields of Artificial Intelligence into a unified framework. We demonstrate the utility of our approach by showing how major contributions of the field can be expressed within the proposed framework. We then claim that benchmarking environments need to reproduce ecologically-valid conditions for bootstrapping the acquisition of increasingly complex cognitive skills through the concept of a cognitive arms race between embodied agents.Comment: Updated version of the paper accepted to the ICDL-Epirob 2017 conference (Lisbon, Portugal

    Networking Innovation in the European Car Industry : Does the Open Innovation Model Fit?

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    The automobile industry is has entered an innovation race. Uncertain technological trends, long development cycles, highly capital intensive product development, saturated markets, and environmental and safety regulations have subjected the sector to major transformations. The technological and organizational innovations related to these transformations necessitate research that can enhance our understanding of the characteristics of the new systems and extrapolate the implications for companies as well as for the wider economy. Is the industry ready to change and accelerate the pace of its innovation and adaptability? Have the traditional supply chains transformed into supply networks and regional automobile ecosystems? The study investigates the applicability of the Open Innovation concept to a mature capital-intensive asset-based industry, which is preparing for a radical technological discontinuity - the European automobile industry - through interviewing purposely selected knowledgeable respondents across seven European countries. The findings contribute to the understanding of the OI concept by identifying key obstacles to the wider adoption of the OI model, and signalling the importance of intermediaries and large incumbents for driving network development and OI practices as well as the need of new competencies to be developed by all players.Peer reviewe
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