Open TURNS: An industrial software for uncertainty quantification in simulation

The needs to assess robust performances for complex systems and to answer tighter regulatory processes (security, safety, environmental control, and health impacts, etc.) have led to the emergence of a new industrial simulation challenge: to take uncertainties into account when dealing with complex numerical simulation frameworks. Therefore, a generic methodology has emerged from the joint effort of several industrial companies and academic institutions. EDF R&D, Airbus Group and Phimeca Engineering started a collaboration at the beginning of 2005, joined by IMACS in 2014, for the development of an Open Source software platform dedicated to uncertainty propagation by probabilistic methods, named OpenTURNS for Open source Treatment of Uncertainty, Risk 'N Statistics. OpenTURNS addresses the specific industrial challenges attached to uncertainties, which are transparency, genericity, modularity and multi-accessibility. This paper focuses on OpenTURNS and presents its main features: openTURNS is an open source software under the LGPL license, that presents itself as a C++ library and a Python TUI, and which works under Linux and Windows environment. All the methodological tools are described in the different sections of this paper: uncertainty quantification, uncertainty propagation, sensitivity analysis and metamodeling. A section also explains the generic wrappers way to link openTURNS to any external code. The paper illustrates as much as possible the methodological tools on an educational example that simulates the height of a river and compares it to the height of a dyke that protects industrial facilities. At last, it gives an overview of the main developments planned for the next few years

Video-Game-Like Engine for Depicting Spacecraft Trajectories

GoView is a video-game-like software engine, written in the C and C++ computing languages, that enables real-time, three-dimensional (3D)-appearing visual representation of spacecraft and trajectories (1) from any perspective; (2) at any spatial scale from spacecraft to Solar-system dimensions; (3) in user-selectable time scales; (4) in the past, present, and/or future; (5) with varying speeds; and (6) forward or backward in time. GoView constructs an interactive 3D world by use of spacecraft-mission data from pre-existing engineering software tools. GoView can also be used to produce distributable application programs for depicting NASA orbital missions on personal computers running the Windows XP, Mac OsX, and Linux operating systems. GoView enables seamless rendering of Cartesian coordinate spaces with programmable graphics hardware, whereas prior programs for depicting spacecraft trajectories variously require non-Cartesian coordinates and/or are not compatible with programmable hardware. GoView incorporates an algorithm for nonlinear interpolation between arbitrary reference frames, whereas the prior programs are restricted to special classes of inertial and non-inertial reference frames. Finally, whereas the prior programs present complex user interfaces requiring hours of training, the GoView interface provides guidance, enabling use without any training

Application of mutual information-based sequential feature selection to ISBSG mixed data

[EN] There is still little research work focused on feature selection (FS) techniques including both categorical and continuous features in Software Development Effort Estimation (SDEE) literature. This paper addresses the problem of selecting the most relevant features from ISBSG (International Software Benchmarking Standards Group) dataset to be used in SDEE. The aim is to show the usefulness of splitting the ranked list of features provided by a mutual information-based sequential FS approach in two, regarding categorical and continuous features. These lists are later recombined according to the accuracy of a case-based reasoning model. Thus, four FS algorithms are compared using a complete dataset with 621 projects and 12 features from ISBSG. On the one hand, two algorithms just consider the relevance, while the remaining two follow the criterion of maximizing relevance and also minimizing redundancy between any independent feature and the already selected features. On the other hand, the algorithms that do not discriminate between continuous and categorical features consider just one list, whereas those that differentiate them use two lists that are later combined. As a result, the algorithms that use two lists present better performance than those algorithms that use one list. Thus, it is meaningful to consider two different lists of features so that the categorical features may be selected more frequently. We also suggest promoting the usage of Application Group, Project Elapsed Time, and First Data Base System features with preference over the more frequently used Development Type, Language Type, and Development Platform.Fernández-Diego, M.; González-Ladrón-De-Guevara, F. (2018). Application of mutual information-based sequential feature selection to ISBSG mixed data. Software Quality Journal. 26(4):1299-1325. https://doi.org/10.1007/s11219-017-9391-5S12991325264Angelis, L., & Stamelos, I. (2000). A simulation tool for efficient analogy based cost estimation. Empirical Software Engineering, 5(1), 35–68. https://doi.org/10.1023/A:1009897800559 .Auer, M., Trendowicz, A., Graser, B., Haunschmid, E., & Biffl, S. (2006). Optimal project feature weights in analogy-based cost estimation: improvement and limitations. Software Engineering, IEEE Transactions on, 32(2), 83–92.Awada, W., Khoshgoftaar, T. M., Dittman, D., Wald, R., Napolitano, A. (2012). A review of the stability of feature selection techniques for bioinformatics data. In 2012 I.E. 13th International Conference on Information Reuse and Integration (IRI) (pp. 356–363). Presented at the 2012 I.E. 13th International Conference on Information Reuse and Integration (IRI). https://doi.org/10.1109/IRI.2012.6303031 .Battiti, R. (1994). Using mutual information for selecting features in supervised neural net learning. Neural Networks, IEEE Transactions, 5(4), 537–550.Bennasar, M., Hicks, Y., & Setchi, R. (2015). Feature selection using joint mutual information maximisation. Expert Systems with Applications, 42(22), 8520–8532. https://doi.org/10.1016/j.eswa.2015.07.007 .Bibi, S., Tsoumakas, G., Stamelos, I., & Vlahavas, I. (2008). Regression via classification applied on software defect estimation. Expert Systems with Applications, 34(3), 2091–2101. https://doi.org/10.1016/j.eswa.2007.02.012 .Chandrashekar, G., & Sahin, F. (2014). A survey on feature selection methods. Computers & Electrical Engineering, 40(1), 16–28.Chatzipetrou, P., Papatheocharous, E., Angelis, L., Andreou, A. S. (2012). An investigation of software effort phase distribution using compositional data analysis. In 2012 38th EUROMICRO Conference on Software Engineering and Advanced Applications (SEAA) (pp. 367–375). Presented at the 2012 38th EUROMICRO Conference on Software Engineering and Advanced Applications (SEAA). https://doi.org/10.1109/SEAA.2012.50 .Chen, Z., Menzies, T., Port, D., & Boehm, B. (2005). Feature subset selection can improve software cost estimation accuracy. In Proceedings of the 2005 workshop on predictor models in software engineering (pp. 1–6). New York: ACM. https://doi.org/10.1145/1082983.1083171 .Chiu, N.-H., & Huang, S.-J. (2007). The adjusted analogy-based software effort estimation based on similarity distances. Journal of Systems and Software, 80(4), 628–640.Dash, M., & Liu, H. (2003). Consistency-based search in feature selection. Artificial Intelligence, 151(1), 155–176.Dejaeger, K., Verbeke, W., Martens, D., & Baesens, B. (2012). Data mining techniques for software effort estimation: a comparative study. Software Engineering, IEEE Transactions on, 38(2), 375–397. https://doi.org/10.1109/TSE.2011.55 .Deng, K., & MacDonell, S. G. (2008). Maximising data retention from the ISBSG repository. In Proceedings of the 12th international conference on evaluation and assessment in software engineering (pp. 21–30). Swinton: British Computer Society http://dl.acm.org/citation.cfm?id=2227115.2227118 . Accessed 21 Jan 2014.Doquire, G., & Verleysen, M. (2011). An hybrid approach to feature selection for mixed categorical and continuous data. In International Conference on Knowledge Discovery and Information Retrieval. http://hdl.handle.net/2078.1/90765 . Accessed 2 Nov 2015.Dudani, S. A. (1976). The distance-weighted k-nearest-neighbor rule. IEEE Transactions on Systems, Man and Cybernetics, SMC, 6(4), 325–327. https://doi.org/10.1109/TSMC.1976.5408784 .Estévez, P. A., Tesmer, M., Perez, C. A., & Zurada, J. M. (2009). Normalized mutual information feature selection. IEEE Transactions on Neural Networks, 20(2), 189–201. https://doi.org/10.1109/TNN.2008.2005601 .Fayyad, U.M., & Irani, K.B. (1993). Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning. In Proceedings of the International Joint Conference on Uncertainty in AI (pp. 1022–1027). Presented at the International Joint Conference on Uncertainty in AI. https://www.researchgate.net/publication/220815890_Multi-Interval_Discretization_of_Continuous-Valued_Attributes_for_Classification_Learning . Accessed 22 June 2016.Fernández-Diego, M., & González-Ladrón-de-Guevara, F. (2014). Potential and limitations of the ISBSG dataset in enhancing software engineering research: a mapping review. Information and Software Technology, 56(6), 527–544. https://doi.org/10.1016/j.infsof.2014.01.003 .Ferreira, A., & Figueiredo, M. (2011). Unsupervised joint feature discretization and selection. In J. Vitrià, J. M. Sanches, & M. Hernández (Eds.), Pattern recognition and image analysis (Vol. 6669, pp. 200–207). Berlin, Heidelberg: Springer Berlin Heidelberg http://link.springer.com/10.1007/978-3-642-21257-4_25 . Accessed 4 Mar 2016.Fleuret, F. (2004). Fast binary feature selection with conditional mutual information. Journal of Machine Learning Research, 5, 1531–1555.González-Ladrón-de-Guevara, F., Fernández-Diego, M., & Lokan, C. (2016). The usage of ISBSG data fields in software effort estimation: a systematic mapping study. Journal of Systems and Software, 113, 188–215. https://doi.org/10.1016/j.jss.2015.11.040 .Gupta, P., Jain, S., & Jain, A. (2014). A review of fast clustering-based feature subset selection algorithm. International Journal of Scientific & Technology Research, 3(11), 86–91.Guyon, I., & Elisseeff, A. (2003). An introduction to variable and feature selection. The Journal of Machine Learning Research, 3, 1157–1182.Hall, M. A., & Holmes, G. (2003). Benchmarking attribute selection techniques for discrete class data mining. IEEE Transactions on Knowledge and Data Engineering, 15(6), 1437–1447. https://doi.org/10.1109/TKDE.2003.1245283 .Hausser, J., & Strimmer, K. (2009). Entropy inference and the James-Stein estimator, with application to nonlinear gene association networks. Journal of Machine Learning Research, 10(Jul), 1469–1484.Hill, P. (2010). Practical software project estimation: a toolkit for estimating software development effort & duration. McGraw Hill Professional.Hsu, H.-H., Hsieh, C.-W., & Lu, M.-D. (2011). Hybrid feature selection by combining filters and wrappers. Expert Systems with Applications, 38(7), 8144–8150.Huang, S.-J., & Chiu, N.-H. (2006). Optimization of analogy weights by genetic algorithm for software effort estimation. Information and Software Technology, 48(11), 1034–1045. https://doi.org/10.1016/j.infsof.2005.12.020 .Huang, S.-J., Chiu, N.-H., & Liu, Y.-J. (2008). A comparative evaluation on the accuracies of software effort estimates from clustered data. Information and Software Technology, 50(9–10), 879–888. https://doi.org/10.1016/j.infsof.2008.02.005 .Huang, J., Li, Y.-F., & Xie, M. (2015). An empirical analysis of data preprocessing for machine learning-based software cost estimation. Information and Software Technology, 67, 108–127. https://doi.org/10.1016/j.infsof.2015.07.004 .ISBSG. (2013a). ISBSG Dataset Release 12. ISBSG. http://isbsg.org/ . Accessed 1 Mar 2016.ISBSG. (2013b). ISBSG Guidelines Release 12.ISBSG. (2013c). ISBSG Data Demographics Release 12.Jeffery, R., Ruhe, M., Wieczorek, I. (2001). Using public domain metrics to estimate software development effort. In Software Metrics Symposium, 2001. METRICS 2001. Proceedings. Seventh International (pp. 16–27). https://doi.org/10.1109/METRIC.2001.915512 .Jiang, Z., & Comstock, C. (2007). The factors significant to software development productivity. In C. Ardil (Ed.), Proceedings of World Academy of Science, Engineering and Technology, Vol 19 (Vol. 19, pp. 160–164). Presented at the Conference of the World-Academy-of-Science-Engineering-and-Technology, Bangkok: World Acad Sci, Eng & Tech-Waset.Jørgensen, M., Indahl, U., & Sjøberg, D. (2003). Software effort estimation by analogy and ‘regression toward the mean’. Journal of Systems and Software, 68(3), 253–262. https://doi.org/10.1016/S0164-1212(03)00066-9 .Kabir, M. M., Shahjahan, M., & Murase, K. (2011). A new local search based hybrid genetic algorithm for feature selection. Neurocomputing, 74(17), 2914–2928.Kadoda, G., Cartwright, M., Chen, L., Shepperd, M. (2000). Experiences using case-based reasoning to predict software project effort. In EASE 2000 (pp. 2–3). Presented at the EASE 2000, Staffordshire, UK.Keung, J., Kocaguneli, E., & Menzies, T. (2012). Finding conclusion stability for selecting the best effort predictor in software effort estimation. Automated Software Engineering, 20(4), 543–567. https://doi.org/10.1007/s10515-012-0108-5 .Kirsopp, C., Shepperd, M. J., Hart, J. (2002). Search heuristics, case-based reasoning and software project effort prediction. In Proceedings of the Genetic and Evolutionary Computation Conference (pp. 9–13). New York, USA. http://v-scheiner.brunel.ac.uk/handle/2438/1554 . Accessed 27 Jan 2016.Kohavi, R., & John, G. H. (1997). Wrappers for feature subset selection. Artificial Intelligence, 97(1–2), 273–324. https://doi.org/10.1016/S0004-3702(97)00043-X .Kwak, N., & Choi, C.-H. (2002). Input feature selection for classification problems. IEEE Transactions on Neural Networks, 13(1), 143–159. https://doi.org/10.1109/72.977291 .Langdon, W. B., Dolado, J., Sarro, F., & Harman, M. (2016). Exact mean absolute error of baseline predictor, MARP0. Information and Software Technology, 73, 16–18. https://doi.org/10.1016/j.infsof.2016.01.003 .Li, Y. F., Xie, M., & Goh, T. N. (2009). A study of mutual information based feature selection for case based reasoning in software cost estimation. Expert Systems with Applications, 36(3), 5921–5931.Liu, H., & Motoda, H. (2012). Feature selection for knowledge discovery and data mining (Vol. 454). Springer Science & Business Media. https://books.google.es/books?hl=en&lr=&id=aaDbBwAAQBAJ&oi=fnd&pg=PP10&dq=Feature+selection+for+knowledge+discovery+and+data+mining&ots=iuMhcWZGcf&sig=KlmNEIcsBdDVs-m1HUuICfpYZiM . Accessed 25 Jan 2016.Liu, H., & Yu, L. (2005). Toward integrating feature selection algorithms for classification and clustering. IEEE Transactions on Knowledge and Data Engineering, 17(4), 491–502. https://doi.org/10.1109/TKDE.2005.66 .Liu, H., Wei, R., & Jiang, G. (2013). A hybrid feature selection scheme for mixed attributes data. Computational and Applied Mathematics, 32(1), 145–161. https://doi.org/10.1007/s40314-013-0019-5 .Liu, Q., Wang, J., Xiao, J., Zhu, H. (2014). Mutual information based feature selection for symbolic interval data. In International Conference on Software Intelligence Technologies and Applications International Conference on Frontiers of Internet of Things 2014 (pp. 62–69). Presented at the International Conference on Software Intelligence Technologies and Applications International Conference on Frontiers of Internet of Things 2014. https://doi.org/10.1049/cp.2014.1537 .Lokan, C. (2005). What should you optimize when building an estimation model? In Software Metrics, 2005. 11th IEEE International Symposium (pp. 1–10). https://doi.org/10.1109/METRICS.2005.55 .Lokan, C., & Mendes, E. (2009a). Investigating the use of chronological split for software effort estimation. Software, IET, 3(5), 422–434. https://doi.org/10.1049/iet-sen.2008.0107 .Lokan, C., & Mendes, E. (2009b). Applying moving windows to software effort estimation. In Proceedings of the 2009 3rd international symposium on empirical software engineering and measurement (pp. 111–122). Washington, DC: IEEE Computer Society. https://doi.org/10.1109/ESEM.2009.5316019 .Lokan, C., & Mendes, E. (2012). Investigating the use of duration-based moving windows to improve software effort prediction. In Software Engineering Conference (APSEC), 2012 19th Asia-Pacific (Vol. 1, pp. 818–827). Presented at the Software Engineering Conference (APSEC), 2012 19th Asia-Pacific. https://doi.org/10.1109/APSEC.2012.74 .Lustgarten, J.L., Visweswaran, S., Grover, H., Gopalakrishnan, V. (2008). An evaluation of discretization methods for learning rules from biomedical datasets. In BIOCOMP (pp. 527–532).Mandal, M., & Mukhopadhyay, A. (2013). An improved minimum redundancy maximum relevance approach for feature selection in gene expression data. Procedia Technology, 10, 20–27. https://doi.org/10.1016/j.protcy.2013.12.332 .Mendes, E., Watson, I., Triggs, C., Mosley, N., & Counsell, S. (2003). A comparative study of cost estimation models for web hypermedia applications. Empirical Software Engineering, 8(2), 163–196.Mendes, E., Lokan, C., Harrison, R., Triggs, C. (2005). A replicated comparison of cross-company and within-company effort estimation models using the ISBSG database. 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Fast branch & bound algorithms for optimal feature selection. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 26(7), 900–912.Song, Q., & Shepperd, M. (2007). A new imputation method for small software project data sets. Journal of Systems and Software, 80(1), 51–62.Top, O. O., Ozkan, B., Nabi, M., Demirors, O. (2011). Internal and External Software Benchmark Repository Utilization for Effort Estimation. In Software Measurement, 2011 Joint Conference of the 21st Int’l Workshop on and 6th Int’l Conference on Software Process and Product Measurement (IWSM-MENSURA) (pp. 302–307). https://doi.org/10.1109/IWSM-MENSURA.2011.41 .Vinh, L.T., Thang, N.D., Lee, Y.-K. (2010). An improved maximum relevance and minimum redundancy feature selection algorithm based on normalized mutual information. In 2010 10th IEEE/IPSJ International Symposium on Applications and the Internet (SAINT) (pp. 395–398). Presented at the 2010 10th IEEE/IPSJ International Symposium on Applications and the Internet (SAINT). https://doi.org/10.1109/SAINT.2010.50 .Witten, I.H., Frank, E., Hall, M.A., Pal, C.J. (2011). Data mining: Practical machine learning tools and techniques. Morgan Kaufmann

Práctica de desarrollo de interfaces hardware/software para la monitorización del estado de un PC

Este artículo presenta una práctica laboratorio impartida mediante una metodología de aprendizaje basado en proyectos (ABP) [1] para dotar de la capacidad de diseñar y desarrollar un monitor del estado de un ordenador, integrado en un sistema empotrado que se comunica con una aplicación de escritorio, a nuestros alumnos de la asignatura de Diseño de Microcontroladores (DM) en el contexto del Máster en Ingeniería de Computadores y Redes. Esta práctica abarca la comunicación Hardware/ Software entre un microcontrolador con un núcleo Cortex-M4 y una aplicación software escrita en lenguaje C# usando el entorno Visual Studio Community 2015 a través de puertos series virtuales (VCP). Esta práctica está enfocada como un proyecto que los alumnos han de ir realizando desde cero, avanzando mediante la consecución de hitos, hasta conseguir obtener un sistema final. El sistema a desarrollar se divide en dos partes, por un lado tenemos un PC con un sistema operativo de la familia Windows, en el que se construye una aplicación visual mediante Windows Forms, la cual obtiene información del sistema de forma periódica y la envía al microcontrolador mediante comandos usando el puerto serie (USB o comunicación Bluetooth). Por otro lado tenemos un microcontrolador de la familia STM32 que dispone de un display LCD ejecutando una plataforma completamente libre, .NET Micro Framework, la cual recibe a través del puerto serie la información obtenida gracias a la aplicación software del PC y la muestra en la pantalla, obteniendo así una herramienta de monitorización del PC sin tener que estar conectado físicamente a éste. El desarrollo de este tipo de proyectos se añade la dificultad de la necesidad del uso de diferentes herramientas para el desarrollo del firmware y del software en paralelo, de manera incremental, y enfocadas para ámbitos de uso muy distintos. Esta práctica ha tenido una gran acogida por parte de los alumnos, ya que les ha servido de ejemplo del desarrollo de firmware para un microcontrolador usando la plataforma .NET MF y de su comunicación con el PC por medio de una aplicación visual.This manuscript presents a practical laboratory session imparted using a project-based learning methodology (PBL) to provide the capacity of designing and developing a computer status monitoring device, integrated in an embedded system that communicates with a desktop software tool, to our students in the Computer Engineering Master’s Degree. This practice session encompasses Hardware/ Software communication between a microcontroller with a Cortex-M4 kernel and a desktop software application through virtual COM ports (VCP) written in C# using Visual Studio Community 2015. This lab session is focused as a project that students must be making from scratch by achieving and completing some milestones to obtain a final functional system. The project is divided into two different parts. First, we have a Windows PC where a visual software application that gathers information from the system and sends it periodically to the microcontroller (USB or Bluetooth) has to be built using Windows Forms. On the other hand, we have a microcontroller from the STM32 family that has a 2.4’ LCD display executing .NET Micro Framework that receives the information obtained from the PC through the serial port and displays it in the screen. This way, students create a computer status monitoring tool that does not need to be connected physically to it to receive the information. The development of this project is added to the need of using different tools for firmware and software development, focused to very different fields of use. This practice has been well received by the students, because it has served as an example of the firmware development for a microcontroller using the .NET MF platform as well as the communication between the PC and the microcontroller using a visual software application

Advancing CASE Productivity by Using Natural Language Processing and Computerized Ontologies: The ACAPULCO system

We present a new approach to software engineering which reduces the knowledge gap between user and development methodology by explicitly supporting concepts expressed in natural language. The tool uses a natural language description of a business process as input and transforms it into a process model. The system recognizes actors, objects, locations, relationships etc. referred to in the description and distinguishes different types of actions and conditions. The system uses multi-pass parsing and disambiguation NLP techniques and relies upon a custom-built dictionary of 23.000 English root words. The dictionary includes information about syntactic (e.g. noun, verb...) and semantic categories as well as word frequency. Currently 15 different semantic categories such as \u27tangible object\u27, \u27person\u27, \u27event\u27, etc. are distinguished. The ACAPULCO prototype, which runs on a standard PC under Windows 3.1 with 16 Mbytes of RAM, demonstrates a) that natural language processing for software engineeringis feasible, b) that this approach has potential of redefining the interaction and relationships between users, analysts and developers and c) that this approach is a powerful extension to traditional methods because it uses explicit knowledge about real-world business concepts

UNDERWATER DATA COMMUNICATION PACKAGE

This project concentrates on simplified and innovated technology to develop an efficient software package for Underwater Acoustic (UWA) communication nfrich helps researcher to have better understanding of the behavior of undemmter acoustic network, to cater for the UTP in-house research needs and to set up the relevant basic underwater acoustic communication laboratory based testbed. The existing simulation tool, particularly NS2 can give reseanchers some bosic uderstanding of underwater network, and this requires certain level of knowledge in C++, TCL and most importantly understating the infrastnrcture of the simulation Howwer, researchers will find out that they are not able to simularc the real underurer environment. This project would tackle problems existed in softunare development by utilizing Windows Foundation Presentation Technology and Model View ViewModel architecture which is an architectrral pat&ern mostly used in softnnare engineering that originated frrom Microsoft. The author believes that this softu/arc package will enable students/ reseachers to pcrform their studies and testing in areal lab based environment with a minimum amount of effor

The TSO Logic and G2 Software Product

This internship assignment for spring 2014 was at John F. Kennedy Space Center (KSC), in NASAs Engineering and Technology (NE) group in support of the Control and Data Systems Division (NE-C) within the Systems Hardware Engineering Branch. (NEC-4) The primary focus was in system integration and benchmarking utilizing two separate computer software products. The first half of this 2014 internship is spent in assisting NE-C4s Electronics and Embedded Systems Engineer, Kelvin Ruiz and fellow intern Scott Ditto with the evaluation of a newly piece of software, called G2. Its developed by the Gensym Corporation and introduced to the group as a tool used in monitoring launch environments. All fellow interns and employees of the G2 group have been working together in order to better understand the significance of the G2 application and how KSC can benefit from its capabilities. The second stage of this Spring project is to assist with an ongoing integration of a benchmarking tool, developed by a group of engineers from a Canadian based organization known as TSO Logic. Guided by NE-C4s Computer Engineer, Allen Villorin, NASA 2014 interns put forth great effort in helping to integrate TSOs software into the Spaceport Processing Systems Development Laboratory (SPSDL) for further testing and evaluating. The TSO Logic group claims that their software is designed for, monitoring and reducing energy consumption at in-house server farms and large data centers, allows data centers to control the power state of servers, without impacting availability or performance and without changes to infrastructure and the focus of the assignment is to test this theory. TSOs Aaron Rallo Founder and CEO, and Chris Tivel CTO, both came to KSC to assist with the installation of their software in the SPSDL laboratory. TSOs software is installed onto 24 individual workstations running three different operating systems. The workstations were divided into three groups of 8 with each group having its own operating system. The first group is comprised of Ubuntus Debian -based Linux the second group is windows 7 Professional and the third group ran Red Hat Linux. The highlight of this portion of the assignment is to compose documentation expressing the overall impression of the software and its capabilities