The Experts below are selected from a list of 315 Experts worldwide ranked by ideXlab platform
Francesco Merlo - One of the best experts on this subject based on the ideXlab platform.
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ICSM - The Economics of Open Source Software: An Empirical Analysis of Maintenance Costs
2007 IEEE International Conference on Software Maintenance, 2007Co-Authors: Eugenio Capra, Chiara Francalanci, Francesco MerloAbstract:A quality degradation effect of Proprietary Code has been observed as a consequence of maintenance. This quality degradation effect, called entropy, is a cause for higher maintenance costs. In the Open Source context, the quality of Code is a fundamental tenet of open software developers. As a consequence, the quality degradation principle measured by entropy cannot be assumed to be valid. The goal of the paper is to analyze the entropy of Open Source applications by measuring the evolution of maintenance costs over time. Analyses are based on cost data collected from a sample of 1251 Open Source application versions, compared with the costs estimated with a traditional model for Proprietary software. Findings indicate that Open Source applications are less subject to entropy, have lower maintenance costs and also a lower need for maintenance interventions aimed at restoring quality. Finally, results show that a lower entropy is favored by greater functional simplicity.
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The Economics of Open Source Software: An Empirical Analysis of Maintenance Costs
2007 IEEE International Conference on Software Maintenance, 2007Co-Authors: Eugenio Capra, Chiara Francalanci, Francesco MerloAbstract:A quality degradation effect of Proprietary Code has been observed as a consequence of maintenance. This quality degradation effect, called entropy, is a cause for higher maintenance costs. In the Open Source context, the quality of Code is a fundamental tenet of open software developers. As a consequence, the quality degradation principle measured by entropy cannot be assumed to be valid. The goal of the paper is to analyze the entropy of Open Source applications by measuring the evolution of maintenance costs over time. Analyses are based on cost data collected from a sample of 1251 Open Source application versions, compared with the costs estimated with a traditional model for Proprietary software. Findings indicate that Open Source applications are less subject to entropy, have lower maintenance costs and also a lower need for maintenance interventions aimed at restoring quality. Finally, results show that a lower entropy is favored by greater functional simplicity.
Ming Lu - One of the best experts on this subject based on the ideXlab platform.
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Winter Simulation Conference - Photo-based 3D modeling of construction resources for visualization of operations simulation: case of modeling a precast façade
2008 Winter Simulation Conference, 2008Co-Authors: Ming LuAbstract:3D models of building components or construction resources have been largely created by computer-aided-design (CAD) or by Proprietary Code for virtual reality development. Such 3D modeling methods entail accurate definition of points, lines and their relationships in the spatial coordinate system. Unlike CAD modeling, the surveying technique of photogrammetry takes a completely different approach by deriving metric information about an object through measurements conducted on photographs of the object. The very basic technique of photogrammetry is effective and computationally simple. With much less efforts, digital cameras and photogrammetry software have made possible 3D reconstruction of an object in digital form (coordinates and derived geometric elements). The resultant 3D models may well satisfy application needs in construction simulation visualization. In this paper, we introduce computing algorithms of photogrammetry and present an application of modeling a precast facade in 3D based on digital pictures taken at a building site.
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Photo-based 3D modeling of construction resources for visualization of operations simulation: Case of modeling a precast façade
2008 Winter Simulation Conference, 2008Co-Authors: Ming LuAbstract:3D models of building components or construction resources have been largely created by computer-aided-design (CAD) or by Proprietary Code for virtual reality development. Such 3D modeling methods entail accurate definition of points, lines and their relationships in the spatial coordinate system. Unlike CAD modeling, the surveying technique of photogrammetry takes a completely different approach by deriving metric information about an object through measurements conducted on photographs of the object. The very basic technique of photogrammetry is effective and computationally simple. With much less efforts, digital cameras and photogrammetry software have made possible 3D reconstruction of an object in digital form (coordinates and derived geometric elements). The resultant 3D models may well satisfy application needs in construction simulation visualization. In this paper, we introduce computing algorithms of photogrammetry and present an application of modeling a precast facade in 3D based on digital pictures taken at a building site.
Eugenio Capra - One of the best experts on this subject based on the ideXlab platform.
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ICSM - The Economics of Open Source Software: An Empirical Analysis of Maintenance Costs
2007 IEEE International Conference on Software Maintenance, 2007Co-Authors: Eugenio Capra, Chiara Francalanci, Francesco MerloAbstract:A quality degradation effect of Proprietary Code has been observed as a consequence of maintenance. This quality degradation effect, called entropy, is a cause for higher maintenance costs. In the Open Source context, the quality of Code is a fundamental tenet of open software developers. As a consequence, the quality degradation principle measured by entropy cannot be assumed to be valid. The goal of the paper is to analyze the entropy of Open Source applications by measuring the evolution of maintenance costs over time. Analyses are based on cost data collected from a sample of 1251 Open Source application versions, compared with the costs estimated with a traditional model for Proprietary software. Findings indicate that Open Source applications are less subject to entropy, have lower maintenance costs and also a lower need for maintenance interventions aimed at restoring quality. Finally, results show that a lower entropy is favored by greater functional simplicity.
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The Economics of Open Source Software: An Empirical Analysis of Maintenance Costs
2007 IEEE International Conference on Software Maintenance, 2007Co-Authors: Eugenio Capra, Chiara Francalanci, Francesco MerloAbstract:A quality degradation effect of Proprietary Code has been observed as a consequence of maintenance. This quality degradation effect, called entropy, is a cause for higher maintenance costs. In the Open Source context, the quality of Code is a fundamental tenet of open software developers. As a consequence, the quality degradation principle measured by entropy cannot be assumed to be valid. The goal of the paper is to analyze the entropy of Open Source applications by measuring the evolution of maintenance costs over time. Analyses are based on cost data collected from a sample of 1251 Open Source application versions, compared with the costs estimated with a traditional model for Proprietary software. Findings indicate that Open Source applications are less subject to entropy, have lower maintenance costs and also a lower need for maintenance interventions aimed at restoring quality. Finally, results show that a lower entropy is favored by greater functional simplicity.
A M Olney - One of the best experts on this subject based on the ideXlab platform.
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AAAI Fall Symposium: Cognitive and Metacognitive Educational Systems - GnuTutor: An Open Source Intelligent Tutoring System Based on AutoTutor
2009Co-Authors: A M OlneyAbstract:This paper presents GnuTutor, an open source intelligent tutoring system (ITS) inspired by the AutoTutor ITS. The goal of GnuTutor is to create a freely available, open source ITS platform that can be used by schools and researchers alike. To achieve this goal, significant departures from AutoTutor's current design were made so that GnuTutor would use a smaller, non-Proprietary Code base but have the major functionality of AutoTutor, including mixed-initiative dialogue, an animated agent, speech act classification, and natural language understanding using latent semantic analysis. This paper describes the GnuTutor system, its components, and the major differences between GnuTutor and AutoTutor.
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GnuTutor: An open source intelligent tutoring system based on AutoTutor
AAAI Fall Symposium - Technical Report, 2009Co-Authors: A M OlneyAbstract:This paper presents GnuTutor, an open source intelligent tutoring system (ITS) inspired by the AutoTutor ITS. The goal of GnuTutor is to create a freely available, open source ITS platform that can be used by schools and researchers alike. To achieve this goal, significant departures from AutoTutor's current design were made so that GnuTutor would use a smaller, non-Proprietary Code base but have the major functionality of AutoTutor, including mixed-initiative dialogue, an animated agent, speech act classification, and natural language understanding using latent semantic analysis. This paper describes the GnuTutor system, its components, and the major differences between GnuTutor and AutoTutor. Copyright © 2009, Association for the Advancement of Artificial Intelligence. All rights reserved.
Marc Mcconley - One of the best experts on this subject based on the ideXlab platform.
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ICMLA - Automated Vulnerability Detection in Source Code Using Deep Representation Learning
2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), 2018Co-Authors: Rebecca L. Russell, Jacob Harer, Onur Ozdemir, Lei Hamilton, Paul M. Ellingwood, Tomo Lazovich, Marc McconleyAbstract:Increasing numbers of software vulnerabilities are discovered every year whether they are reported publicly or discovered internally in Proprietary Code. These vulnerabilities can pose serious risk of exploit and result in system compromise, information leaks, or denial of service. We leveraged the wealth of C and C++ open-source Code available to develop a largescale function-level vulnerability detection system using machine learning. To supplement existing labeled vulnerability datasets, we compiled a vast dataset of millions of open-source functions and labeled it with carefully-selected findings from three different static analyzers that indicate potential exploits. Using these datasets, we developed a fast and scalable vulnerability detection tool based on deep feature representation learning that directly interprets lexed source Code. We evaluated our tool on Code from both real software packages and the NIST SATE IV benchmark dataset. Our results demonstrate that deep feature representation learning on source Code is a promising approach for automated software vulnerability detection.
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Automated Vulnerability Detection in Source Code Using Deep Representation Learning
arXiv: Learning, 2018Co-Authors: Rebecca L. Russell, Jacob Harer, Onur Ozdemir, Lei Hamilton, Paul M. Ellingwood, Tomo Lazovich, Marc McconleyAbstract:Increasing numbers of software vulnerabilities are discovered every year whether they are reported publicly or discovered internally in Proprietary Code. These vulnerabilities can pose serious risk of exploit and result in system compromise, information leaks, or denial of service. We leveraged the wealth of C and C++ open-source Code available to develop a large-scale function-level vulnerability detection system using machine learning. To supplement existing labeled vulnerability datasets, we compiled a vast dataset of millions of open-source functions and labeled it with carefully-selected findings from three different static analyzers that indicate potential exploits. The labeled dataset is available at: this https URL. Using these datasets, we developed a fast and scalable vulnerability detection tool based on deep feature representation learning that directly interprets lexed source Code. We evaluated our tool on Code from both real software packages and the NIST SATE IV benchmark dataset. Our results demonstrate that deep feature representation learning on source Code is a promising approach for automated software vulnerability detection.
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Automated Vulnerability Detection in Source Code Using Deep Representation Learning
2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), 2018Co-Authors: Rebecca Russell, Jacob Harer, Onur Ozdemir, Lei Hamilton, Paul M. Ellingwood, Tomo Lazovich, Marc McconleyAbstract:Increasing numbers of software vulnerabilities are discovered every year whether they are reported publicly or discovered internally in Proprietary Code. These vulnerabilities can pose serious risk of exploit and result in system compromise, information leaks, or denial of service. We leveraged the wealth of C and C++ open-source Code available to develop a largescale function-level vulnerability detection system using machine learning. To supplement existing labeled vulnerability datasets, we compiled a vast dataset of millions of open-source functions and labeled it with carefully-selected findings from three different static analyzers that indicate potential exploits. Using these datasets, we developed a fast and scalable vulnerability detection tool based on deep feature representation learning that directly interprets lexed source Code. We evaluated our tool on Code from both real software packages and the NIST SATE IV benchmark dataset. Our results demonstrate that deep feature representation learning on source Code is a promising approach for automated software vulnerability detection.
