Literatura académica sobre el tema "SOFTWARE PREDICTION MODELS"
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Artículos de revistas sobre el tema "SOFTWARE PREDICTION MODELS"
Balogun, A. O., A. O. Bajeh, H. A. Mojeed y A. G. Akintola. "Software defect prediction: A multi-criteria decision-making approach". Nigerian Journal of Technological Research 15, n.º 1 (30 de abril de 2020): 35–42. http://dx.doi.org/10.4314/njtr.v15i1.7.
Texto completoMalhotra, Ruchika y Juhi Jain. "Predicting Software Defects for Object-Oriented Software Using Search-based Techniques". International Journal of Software Engineering and Knowledge Engineering 31, n.º 02 (febrero de 2021): 193–215. http://dx.doi.org/10.1142/s0218194021500054.
Texto completoVandecruys, Olivier, David Martens, Bart Baesens, Christophe Mues, Manu De Backer y Raf Haesen. "Mining software repositories for comprehensible software fault prediction models". Journal of Systems and Software 81, n.º 5 (mayo de 2008): 823–39. http://dx.doi.org/10.1016/j.jss.2007.07.034.
Texto completoZaim, Amirul, Johanna Ahmad, Noor Hidayah Zakaria, Goh Eg Su y Hidra Amnur. "Software Defect Prediction Framework Using Hybrid Software Metric". JOIV : International Journal on Informatics Visualization 6, n.º 4 (31 de diciembre de 2022): 921. http://dx.doi.org/10.30630/joiv.6.4.1258.
Texto completoKalouptsoglou, Ilias, Miltiadis Siavvas, Dionysios Kehagias, Alexandros Chatzigeorgiou y Apostolos Ampatzoglou. "Examining the Capacity of Text Mining and Software Metrics in Vulnerability Prediction". Entropy 24, n.º 5 (5 de mayo de 2022): 651. http://dx.doi.org/10.3390/e24050651.
Texto completoShatnawi, Raed. "Software fault prediction using machine learning techniques with metric thresholds". International Journal of Knowledge-based and Intelligent Engineering Systems 25, n.º 2 (26 de julio de 2021): 159–72. http://dx.doi.org/10.3233/kes-210061.
Texto completoEldho, K. J. "Impact of Unbalanced Classification on the Performance of Software Defect Prediction Models". Indian Journal of Science and Technology 15, n.º 6 (15 de febrero de 2022): 237–42. http://dx.doi.org/10.17485/ijst/v15i6.2193.
Texto completoKarunanithi, N., D. Whitley y Y. K. Malaiya. "Prediction of software reliability using connectionist models". IEEE Transactions on Software Engineering 18, n.º 7 (julio de 1992): 563–74. http://dx.doi.org/10.1109/32.148475.
Texto completoFenton, N. E. y M. Neil. "A critique of software defect prediction models". IEEE Transactions on Software Engineering 25, n.º 5 (1999): 675–89. http://dx.doi.org/10.1109/32.815326.
Texto completoLawson, John S., Craig W. Wesselman y Del T. Scott. "Simple Plots Improve Software Reliability Prediction Models". Quality Engineering 15, n.º 3 (abril de 2003): 411–17. http://dx.doi.org/10.1081/qen-120018040.
Texto completoTesis sobre el tema "SOFTWARE PREDICTION MODELS"
Bowes, David Hutchinson. "Factors affecting the performance of trainable models for software defect prediction". Thesis, University of Hertfordshire, 2013. http://hdl.handle.net/2299/10978.
Texto completoAskari, Mina. "Information Theoretic Evaluation of Change Prediction Models for Large-Scale Software". Thesis, University of Waterloo, 2006. http://hdl.handle.net/10012/1139.
Texto completoIn this thesis, we first analyze the information generated during the development process, which can be obtained through mining the software repositories. We observe that the change data follows a Zipf distribution and exhibits self-similarity. Based on the extracted data, we then develop three probabilistic models to predict which files will have changes or bugs. One purpose of creating these models is to rank the files of the software that are most susceptible to having faults.
The first model is Maximum Likelihood Estimation (MLE), which simply counts the number of events i. e. , changes or bugs that occur in to each file, and normalizes the counts to compute a probability distribution. The second model is Reflexive Exponential Decay (RED), in which we postulate that the predictive rate of modification in a file is incremented by any modification to that file and decays exponentially. The result of a new bug occurring to that file is a new exponential effect added to the first one. The third model is called RED Co-Changes (REDCC). With each modification to a given file, the REDCC model not only increments its predictive rate, but also increments the rate for other files that are related to the given file through previous co-changes.
We then present an information-theoretic approach to evaluate the performance of different prediction models. In this approach, the closeness of model distribution to the actual unknown probability distribution of the system is measured using cross entropy. We evaluate our prediction models empirically using the proposed information-theoretic approach for six large open source systems. Based on this evaluation, we observe that of our three prediction models, the REDCC model predicts the distribution that is closest to the actual distribution for all the studied systems.
Tran, Qui Can Cuong. "Empirical evaluation of defect identification indicators and defect prediction models". Thesis, Blekinge Tekniska Högskola, Sektionen för datavetenskap och kommunikation, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-2553.
Texto completoLiu, Qin. "Optimal utilization of historical data sets for the construction of software cost prediction models". Thesis, Northumbria University, 2006. http://nrl.northumbria.ac.uk/2129/.
Texto completoBrosig, Fabian [Verfasser] y S. [Akademischer Betreuer] Kounev. "Architecture-Level Software Performance Models for Online Performance Prediction / Fabian Maria Konrad Brosig. Betreuer: S. Kounev". Karlsruhe : KIT-Bibliothek, 2014. http://d-nb.info/105980316X/34.
Texto completoChun, Zhang Jing. "Trigonometric polynomial high order neural network group models for financial data simulation & prediction /". [Campblelltown, N.S.W.] : The author, 1998. http://library.uws.edu.au/adt-NUWS/public/adt-NUWS20030721.152829/index.html.
Texto completoMcDonald, Simon Francis. "Better clinical decisions for less effort : building prediction software models to improve anti-coagulation care and prevent thrombosis and strokes". Thesis, Lancaster University, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.539665.
Texto completoHall, Otto. "Inference of buffer queue times in data processing systems using Gaussian Processes : An introduction to latency prediction for dynamic software optimization in high-end trading systems". Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-214791.
Texto completoDenna studie undersöker huruvida Gaussian Process Regression kan appliceras för att utvärdera buffer-kötider i storskaliga dataprocesseringssystem. Dessutom utforskas ifall dataströmsfrekvenser kan generaliseras till en liten delmängd av utfallsrymden. Medmålet att erhålla en grund för dynamisk mjukvaruoptimering introduceras en lovandestartpunkt för fortsatt forskning. Studien riktas mot Direct Market Access system för handel på finansiella marknader, somprocesserar enorma mängder marknadsdata dagligen. På grund av vissa begränsningar axlas ett naivt tillvägagångssätt och väntetider modelleras som en funktion av enbartdatagenomströmning i åtta små historiska tidsinterval. Tränings- och testdataset representeras från ren marknadsdata och pruning-tekniker används för att krympa dataseten med en ungefärlig faktor om 0.0005, för att uppnå beräkningsmässig genomförbarhet. Vidare tas fyra olika implementationer av Gaussian Process Regression i beaktning. De resulterande algorithmerna presterar bra på krympta dataset, med en medel R2 statisticpå 0.8399 över sex testdataset, alla av ungefär samma storlek som träningsdatasetet. Tester på icke krympta dataset indikerar vissa brister från pruning, där input vektorermotsvararande låga latenstider är associerade med mindre exakthet. Slutsatsen dras att beroende på applikation kan dessa brister göra modellen obrukbar. För studiens syftefinnes emellertid att latenstider kan sannerligen modelleras av regressionsalgoritmer. Slutligen diskuteras metoder för förbättrning med hänsyn till både pruning och GaussianProcess Regression, och det öppnas upp för lovande vidare forskning.
Vlad, Iulian Teodor. "Mathematical Methods to Predict the Dynamic Shape Evolution of Cancer Growth based on Spatio-Temporal Bayesian and Geometrical Models". Doctoral thesis, Universitat Jaume I, 2016. http://hdl.handle.net/10803/670303.
Texto completoEl objetivo de esta investigación es observar la dinámica de los tumores, desarrollar e implementarnuevos métodos y algoritmos para la predicción del crecimiento tumoral. Queremos ofrecer algunasherramientas para ayudar a los médicos a comprender y tratar esta enfermedad. Utilizando unmétodo de predicción , y comparándolo con la evolución real de un tumor, un médico puede constata si el tratamiento prescrito tiene el efecto deseado, y de acuerdo con ello, si es necesario, tomar la decisión de intervención quirúrgica. El plan de la tesis es el siguiente. En el primer capítulo recordamos brevemente algunaspropiedades y procesos de clasificación de procesos puntuales con algunos ejemplosespacio-temporales. El capítulo 2 presenta una breve descripción de la teoría de las bases de Levy y se da la integración con respecto a dicha base, recordamos resultados estándar sobre procesosespaciales de Cox, y finalmente proponemos diferentes tipos de modelos de crecimien to y un nuevo algoritmo, el Cobweb, que es presentado y desarrollado en base a la metodología propuesta. Los capítulos 3, 4 y 5 están dedicados a presentar nuevos métodos de predicción.
SARCIA', SALVATORE ALESSANDRO. "An Approach to improving parametric estimation models in the case of violation of assumptions based upon risk analysis". Doctoral thesis, Università degli Studi di Roma "Tor Vergata", 2009. http://hdl.handle.net/2108/1048.
Texto completoLibros sobre el tema "SOFTWARE PREDICTION MODELS"
Rauscher, Harold M. The microcomputer scientific software series 4: Testing prediction accuracy. St. Paul, Minn: U.S. Dept. of Agriculture, Forest Service, North Central Forest Experiment Station, 1986.
Buscar texto completoRamamurthy, Karthikeyan N. MATLAB software for the code excited linear prediction algorithm: The Federal Standard, 1016. San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA): Morgan & Claypool Publishers, 2010.
Buscar texto completoMetrics for process models: Empirical foundations of verification, error prediction, and guidelines for correctness. Berlin: Springer, 2008.
Buscar texto completoMatthew, O'Keefe, Kerr Christopher, United States. Dept. of Energy. Office of Biological and Environmental Research. y Goddard Space Flight Center, eds. Second International Workshop on Software Engineering and Code Design in Parallel Meteorological and Oceanographic Applications: Proceedings of a workshop sponsored by the U.S. Department of Energy, Office of Biological and Environmental Research; the Department of Defense, High Performance Computing and Modernization Office; and the NASA Goddard Space Flight Center, Seasonal-to-Interannual Prediction Project, and held at the Camelback Inn, Scottsdale, Arizona, June 15-18, 1998. Greenbelt, Md: National Aeronautics and Space Administration, Goddard Space Flight Center, 1998.
Buscar texto completoDennison, Thomas E. Fitting and prediction uncertainty for a software reliability model. Monterey, Calif: Naval Postgraduate School, 1992.
Buscar texto completoFernandez-Camacho, Eduardo. Model Predictive Control in the Process Industry. London: Springer London, 1995.
Buscar texto completoAhmad, Anees. Software to model AXAF-I image quality: Final report. [Washington, DC: National Aeronautics and Space Administration, 1995.
Buscar texto completoZhen, Feng y United States. National Aeronautics and Space Administration., eds. Software to model AXAF-I image quality: Final report. [Washington, DC: National Aeronautics and Space Administration, 1995.
Buscar texto completoChen, Feng y United States. National Aeronautics and Space Administration., eds. Software to model AXAF-I image quality: Final report. [Washington, DC: National Aeronautics and Space Administration, 1995.
Buscar texto completoGrigor'ev, Anatoliy y Evgeniy Isaev. Methods and algorithms of data processing. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1032305.
Texto completoCapítulos de libros sobre el tema "SOFTWARE PREDICTION MODELS"
Okumoto, Kazu. "Customer-Perceived Software Reliability Predictions: Beyond Defect Prediction Models". En Springer Series in Reliability Engineering, 219–49. London: Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-4971-2_11.
Texto completoSantos, Geanderson, Amanda Santana, Gustavo Vale y Eduardo Figueiredo. "Yet Another Model! A Study on Model’s Similarities for Defect and Code Smells". En Fundamental Approaches to Software Engineering, 282–305. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-30826-0_16.
Texto completoTanwar, Harshita y Misha Kakkar. "A Review of Software Defect Prediction Models". En Data Management, Analytics and Innovation, 89–97. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1402-5_7.
Texto completoBal, Pravas Ranjan, Nachiketa Jena y Durga Prasad Mohapatra. "Software Reliability Prediction Based on Ensemble Models". En Proceeding of International Conference on Intelligent Communication, Control and Devices, 895–902. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-1708-7_105.
Texto completoAwoke, Temesgen, Minakhi Rout, Lipika Mohanty y Suresh Chandra Satapathy. "Bitcoin Price Prediction and Analysis Using Deep Learning Models". En Communication Software and Networks, 631–40. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5397-4_63.
Texto completoDe Lucia, Andrea, Eugenio Pompella y Silvio Stefanucci. "Assessing Effort Prediction Models for Corrective Software Maintenance". En Enterprise Information Systems VI, 55–62. Dordrecht: Springer Netherlands, 2006. http://dx.doi.org/10.1007/1-4020-3675-2_7.
Texto completoKuperberg, Michael, Klaus Krogmann y Ralf Reussner. "Performance Prediction for Black-Box Components Using Reengineered Parametric Behaviour Models". En Component-Based Software Engineering, 48–63. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-87891-9_4.
Texto completoCzyczyn-Egird, Daniel y Adam Slowik. "Defect Prediction in Software Using Predictive Models Based on Historical Data". En Advances in Intelligent Systems and Computing, 96–103. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-99608-0_11.
Texto completoGallotti, Stefano, Carlo Ghezzi, Raffaela Mirandola y Giordano Tamburrelli. "Quality Prediction of Service Compositions through Probabilistic Model Checking". En Quality of Software Architectures. Models and Architectures, 119–34. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-87879-7_8.
Texto completoPohlkötter, Fabian J., Dominik Straubinger, Alexander M. Kuhn, Christian Imgrund y William Tekouo. "Unlocking the Potential of Digital Twins". En Advances in Automotive Production Technology – Towards Software-Defined Manufacturing and Resilient Supply Chains, 190–99. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-27933-1_18.
Texto completoActas de conferencias sobre el tema "SOFTWARE PREDICTION MODELS"
Ketata, Aymen, Carlos Moreno, Sebastian Fischmeister, Jia Liang y Krzysztof Czarnecki. "Performance prediction upon toolchain migration in model-based software". En 2015 ACM/IEEE 18th International Conference on Model Driven Engineering Languages and Systems (MODELS). IEEE, 2015. http://dx.doi.org/10.1109/models.2015.7338261.
Texto completoLincke, Rüdiger, Tobias Gutzmann y Welf Löwe. "Software Quality Prediction Models Compared". En 2010 10th International Conference on Quality Software (QSIC). IEEE, 2010. http://dx.doi.org/10.1109/qsic.2010.9.
Texto completoMockus, Audris. "Defect prediction and software risk". En PROMISE '14: The 10th International Conference on Predictive Models in Software Engineering. New York, NY, USA: ACM, 2014. http://dx.doi.org/10.1145/2639490.2639511.
Texto completoBluvband, Zigmund, Sergey Porotsky y Michael Talmor. "Advanced models for software reliability prediction". En Integrity (RAMS). IEEE, 2011. http://dx.doi.org/10.1109/rams.2011.5754487.
Texto completoShafiabady, Aida, Mohd Naz'ri Mahrin y Masoud Samadi. "Investigation of software maintainability prediction models". En 2016 18th International Conference on Advanced Communication Technology (ICACT). IEEE, 2016. http://dx.doi.org/10.1109/icact.2016.7423557.
Texto completoShafiabady, Aida, Mohd Naz'ri Mahrin y Masoud Samadi. "Investigation of software maintainability prediction models". En 2016 18th International Conference on Advanced Communication Technology (ICACT). IEEE, 2016. http://dx.doi.org/10.1109/icact.2016.7423558.
Texto completoWiese, Igor Scaliante, Filipe Roseiro Côgo, Reginaldo Ré, Igor Steinmacher y Marco Aurélio Gerosa. "Social metrics included in prediction models on software engineering". En PROMISE '14: The 10th International Conference on Predictive Models in Software Engineering. New York, NY, USA: ACM, 2014. http://dx.doi.org/10.1145/2639490.2639505.
Texto completoFenton, N., M. Neil, W. Marsh, P. Hearty, L. Radlinski y P. Krause. "Project Data Incorporating Qualitative Factors for Improved Software Defect Prediction". En 2007 3rd International Workshop on Predictor Models in Software Engineering. IEEE, 2007. http://dx.doi.org/10.1109/promise.2007.11.
Texto completoHu, Q. p., M. Xie y S. h. Ng. "Early Software Reliability Prediction with ANN Models". En 2006 12th Pacific Rim International Symposium on Dependable Computing (PRDC'06). IEEE, 2006. http://dx.doi.org/10.1109/prdc.2006.30.
Texto completoZeshan, Furkh y Radziah Mohamad. "Software architecture reliability prediction models: An overview". En 2011 5th Malaysian Conference in Software Engineering (MySEC). IEEE, 2011. http://dx.doi.org/10.1109/mysec.2011.6140654.
Texto completoInformes sobre el tema "SOFTWARE PREDICTION MODELS"
Johnson, G., D. Lawrence y H. Yu. Conceptual Software Reliability Prediction Models for Nuclear Power Plant Safety Systems. Office of Scientific and Technical Information (OSTI), abril de 2000. http://dx.doi.org/10.2172/791856.
Texto completoCheng y Wang. L52025 Calibration of the PRCI Thermal Analysis Model for Hot Tap Welding. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), enero de 2004. http://dx.doi.org/10.55274/r0010298.
Texto completoWang, Yingxuan, Cheng Yan y Liqin Zhao. The value of radiomics-based machine learning for hepatocellular carcinoma after TACE: a systematic evaluation and Meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, junio de 2022. http://dx.doi.org/10.37766/inplasy2022.6.0100.
Texto completoAbdolmaleki, Kourosh. PR453-205101-R01 Prediction of On-bottom Wave Kinematics in Shallow Water. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), mayo de 2022. http://dx.doi.org/10.55274/r0012225.
Texto completoLeis, B. N. y N. D. Ghadiali. L51720 Pipe Axial Flaw Failure Criteria - PAFFC Version 1.0 Users Manual and Software. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), mayo de 1994. http://dx.doi.org/10.55274/r0011357.
Texto completoHoward, Isaac, Thomas Allard, Ashley Carey, Matthew Priddy, Alta Knizley y Jameson Shannon. Development of CORPS-STIF 1.0 with application to ultra-high performance concrete (UHPC). Engineer Research and Development Center (U.S.), abril de 2021. http://dx.doi.org/10.21079/11681/40440.
Texto completoKirk. L51768 Pipeline Free Span Design-Volume 1 Design Guideline. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), abril de 1997. http://dx.doi.org/10.55274/r0011298.
Texto completoCacuci, Dan G., Ruixian Fang y Madalina C. Badea. MULTI-PRED: A Software Module for Predictive Modeling of Coupled Multi-Physics Systems: User's Manual. Office of Scientific and Technical Information (OSTI), febrero de 2018. http://dx.doi.org/10.2172/1503664.
Texto completoLeis, Brian. L51794A Failure Criterion for Residual Strength of Corrosion Defects in Moderate to High Toughness Pipe. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), enero de 2000. http://dx.doi.org/10.55274/r0011253.
Texto completoBruce y Yushanov. L52056 Enhancement of PRCI Thermal Analysis Model for Assessment of Attachments. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), agosto de 2004. http://dx.doi.org/10.55274/r0010436.
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