The Experts below are selected from a list of 48 Experts worldwide ranked by ideXlab platform
Evgeny Bobrov - One of the best experts on this subject based on the ideXlab platform.
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DEVOPS - Anomaly Detection in DevOps Toolchain
Software Engineering Aspects of Continuous Development and New Paradigms of Software Production and Deployment, 2020Co-Authors: Antonio Capizzi, Salvatore Distefano, Manuel Mazzara, Luiz Jonatã Pires De Araújo, Muhammad Ahmad, Evgeny BobrovAbstract:The tools employed in the DevOps Toolchain generates a large quantity of data that is typically ignored or inspected only on particular occasions, at most. However, the analysis of such data could enable the extraction of useful information about the status and evolution of the project. For example, metrics like the “lines of code added since the last release” or “failures detected in the Staging Environment” are good indicators for predicting potential risks in the incoming release. In order to prevent problems appearing in later stages of production, an anomaly detection system can operate in the Staging Environment to compare the current incoming release with previous ones according to predefined metrics. The analysis is conducted before going into production to identify anomalies which should be addressed by human operators that address false-positive and negatives that can appear. In this paper, we describe a prototypical implementation of the aforementioned idea in the form of a “proof of concept”. The current study effectively demonstrates the feasibility of the approach for a set of implemented functionalities.
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Anomaly Detection in DevOps Toolchain
arXiv: Software Engineering, 2019Co-Authors: Antonio Capizzi, Salvatore Distefano, Manuel Mazzara, Luiz Jonatã Pires De Araújo, Muhammad Ahmad, Evgeny BobrovAbstract:The tools employed in the DevOps Toolchain generates a large quantity of data that is typically ignored or inspected only in particular occasions, at most. However, the analysis of such data could enable the extraction of useful information about the status and evolution of the project. For example, metrics like the "lines of code added since the last release" or "failures detected in the Staging Environment" are good indicators for predicting potential risks in the incoming release. In order to prevent problems appearing in later stages of production, an anomaly detection system can operate in the Staging Environment to compare the current incoming release with previous ones according to predefined metrics. The analysis is conducted before going into production to identify anomalies which should be addressed by human operators that address false-positive and negatives that can appear. In this paper, we describe a prototypical implementation of the aforementioned idea in the form of a "proof of concept". The current study effectively demonstrates the feasibility of the approach for a set of implemented functionalities.
Antonio Capizzi - One of the best experts on this subject based on the ideXlab platform.
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DEVOPS - Anomaly Detection in DevOps Toolchain
Software Engineering Aspects of Continuous Development and New Paradigms of Software Production and Deployment, 2020Co-Authors: Antonio Capizzi, Salvatore Distefano, Manuel Mazzara, Luiz Jonatã Pires De Araújo, Muhammad Ahmad, Evgeny BobrovAbstract:The tools employed in the DevOps Toolchain generates a large quantity of data that is typically ignored or inspected only on particular occasions, at most. However, the analysis of such data could enable the extraction of useful information about the status and evolution of the project. For example, metrics like the “lines of code added since the last release” or “failures detected in the Staging Environment” are good indicators for predicting potential risks in the incoming release. In order to prevent problems appearing in later stages of production, an anomaly detection system can operate in the Staging Environment to compare the current incoming release with previous ones according to predefined metrics. The analysis is conducted before going into production to identify anomalies which should be addressed by human operators that address false-positive and negatives that can appear. In this paper, we describe a prototypical implementation of the aforementioned idea in the form of a “proof of concept”. The current study effectively demonstrates the feasibility of the approach for a set of implemented functionalities.
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Anomaly Detection in DevOps Toolchain
arXiv: Software Engineering, 2019Co-Authors: Antonio Capizzi, Salvatore Distefano, Manuel Mazzara, Luiz Jonatã Pires De Araújo, Muhammad Ahmad, Evgeny BobrovAbstract:The tools employed in the DevOps Toolchain generates a large quantity of data that is typically ignored or inspected only in particular occasions, at most. However, the analysis of such data could enable the extraction of useful information about the status and evolution of the project. For example, metrics like the "lines of code added since the last release" or "failures detected in the Staging Environment" are good indicators for predicting potential risks in the incoming release. In order to prevent problems appearing in later stages of production, an anomaly detection system can operate in the Staging Environment to compare the current incoming release with previous ones according to predefined metrics. The analysis is conducted before going into production to identify anomalies which should be addressed by human operators that address false-positive and negatives that can appear. In this paper, we describe a prototypical implementation of the aforementioned idea in the form of a "proof of concept". The current study effectively demonstrates the feasibility of the approach for a set of implemented functionalities.
Paul V Tartabini - One of the best experts on this subject based on the ideXlab platform.
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constraint force equation methodology for modeling multi body stage separation dynamics
46th AIAA Aerospace Sciences Meeting and Exhibit, 2008Co-Authors: Matthew D. Toniolo, Bandu N Pamadi, Paul V Tartabini, Nathaniel HotchkoAbstract:This paper discusses a generalized approach to the multi-body separation problems in a launch vehicle Staging Environment based on constraint force methodology and its implementation into the Program to Optimize Simulated Trajectories II (POST2), a widely used trajectory design and optimization tool. This development facilitates the inclusion of stage separation analysis into POST2 for seamless end-to-end simulations of launch vehicle trajectories, thus simplifying the overall implementation and providing a range of modeling and optimization capabilities that are standard features in POST2. Analysis and results are presented for two test cases that validate the constraint force equation methodology in a stand-alone mode and its implementation in POST2.
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Simulation and Analyses of Multi-Body Separation in Launch Vehicle Staging Environment
14th AIAA AHI Space Planes and Hypersonic Systems and Technologies Conference, 2006Co-Authors: Bandu N Pamadi, Nathaniel Hotchko, Jamshid A. Samareh, Peter F. Covell, Paul V TartabiniAbstract:The development of methodologies, techniques, and tools for analysis and simulation of multi-body separation is critically needed for successful design and operation of next generation launch vehicles. As a part of this activity, ConSep simulation tool is being developed. ConSep is a generic MATLAB-based front-and-back-end to the commercially available ADAMS solver, an industry standard package for solving multi-body dynamic problems. This paper discusses the 3-body separation capability in ConSep and its application to the separation of the Shuttle Solid Rocket Boosters (SRBs) from the External Tank (ET) and the Orbiter. The results are compared with STS-1 flight data.
Manuel Mazzara - One of the best experts on this subject based on the ideXlab platform.
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DEVOPS - Anomaly Detection in DevOps Toolchain
Software Engineering Aspects of Continuous Development and New Paradigms of Software Production and Deployment, 2020Co-Authors: Antonio Capizzi, Salvatore Distefano, Manuel Mazzara, Luiz Jonatã Pires De Araújo, Muhammad Ahmad, Evgeny BobrovAbstract:The tools employed in the DevOps Toolchain generates a large quantity of data that is typically ignored or inspected only on particular occasions, at most. However, the analysis of such data could enable the extraction of useful information about the status and evolution of the project. For example, metrics like the “lines of code added since the last release” or “failures detected in the Staging Environment” are good indicators for predicting potential risks in the incoming release. In order to prevent problems appearing in later stages of production, an anomaly detection system can operate in the Staging Environment to compare the current incoming release with previous ones according to predefined metrics. The analysis is conducted before going into production to identify anomalies which should be addressed by human operators that address false-positive and negatives that can appear. In this paper, we describe a prototypical implementation of the aforementioned idea in the form of a “proof of concept”. The current study effectively demonstrates the feasibility of the approach for a set of implemented functionalities.
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Anomaly Detection in DevOps Toolchain
arXiv: Software Engineering, 2019Co-Authors: Antonio Capizzi, Salvatore Distefano, Manuel Mazzara, Luiz Jonatã Pires De Araújo, Muhammad Ahmad, Evgeny BobrovAbstract:The tools employed in the DevOps Toolchain generates a large quantity of data that is typically ignored or inspected only in particular occasions, at most. However, the analysis of such data could enable the extraction of useful information about the status and evolution of the project. For example, metrics like the "lines of code added since the last release" or "failures detected in the Staging Environment" are good indicators for predicting potential risks in the incoming release. In order to prevent problems appearing in later stages of production, an anomaly detection system can operate in the Staging Environment to compare the current incoming release with previous ones according to predefined metrics. The analysis is conducted before going into production to identify anomalies which should be addressed by human operators that address false-positive and negatives that can appear. In this paper, we describe a prototypical implementation of the aforementioned idea in the form of a "proof of concept". The current study effectively demonstrates the feasibility of the approach for a set of implemented functionalities.
Muhammad Ahmad - One of the best experts on this subject based on the ideXlab platform.
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DEVOPS - Anomaly Detection in DevOps Toolchain
Software Engineering Aspects of Continuous Development and New Paradigms of Software Production and Deployment, 2020Co-Authors: Antonio Capizzi, Salvatore Distefano, Manuel Mazzara, Luiz Jonatã Pires De Araújo, Muhammad Ahmad, Evgeny BobrovAbstract:The tools employed in the DevOps Toolchain generates a large quantity of data that is typically ignored or inspected only on particular occasions, at most. However, the analysis of such data could enable the extraction of useful information about the status and evolution of the project. For example, metrics like the “lines of code added since the last release” or “failures detected in the Staging Environment” are good indicators for predicting potential risks in the incoming release. In order to prevent problems appearing in later stages of production, an anomaly detection system can operate in the Staging Environment to compare the current incoming release with previous ones according to predefined metrics. The analysis is conducted before going into production to identify anomalies which should be addressed by human operators that address false-positive and negatives that can appear. In this paper, we describe a prototypical implementation of the aforementioned idea in the form of a “proof of concept”. The current study effectively demonstrates the feasibility of the approach for a set of implemented functionalities.
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Anomaly Detection in DevOps Toolchain
arXiv: Software Engineering, 2019Co-Authors: Antonio Capizzi, Salvatore Distefano, Manuel Mazzara, Luiz Jonatã Pires De Araújo, Muhammad Ahmad, Evgeny BobrovAbstract:The tools employed in the DevOps Toolchain generates a large quantity of data that is typically ignored or inspected only in particular occasions, at most. However, the analysis of such data could enable the extraction of useful information about the status and evolution of the project. For example, metrics like the "lines of code added since the last release" or "failures detected in the Staging Environment" are good indicators for predicting potential risks in the incoming release. In order to prevent problems appearing in later stages of production, an anomaly detection system can operate in the Staging Environment to compare the current incoming release with previous ones according to predefined metrics. The analysis is conducted before going into production to identify anomalies which should be addressed by human operators that address false-positive and negatives that can appear. In this paper, we describe a prototypical implementation of the aforementioned idea in the form of a "proof of concept". The current study effectively demonstrates the feasibility of the approach for a set of implemented functionalities.