Relevant bibliographies by topics / Estimation software

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Estimation software'

Author: Grafiati
Published: 4 June 2021
Last updated: 10 February 2022

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Journal articles on the topic "Estimation software"

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Capretz, Luiz Fernando, and Venus Marza. "Improving Effort Estimation by Voting Software Estimation Models." Advances in Software Engineering 2009 (September 1, 2009): 1–8. http://dx.doi.org/10.1155/2009/829725.

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Estimating software development effort is an important task in the management of large software projects. The task is challenging, and it has been receiving the attentions of researchers ever since software was developed for commercial purpose. A number of estimation models exist for effort prediction. However, there is a need for novel models to obtain more accurate estimations. The primary purpose of this study is to propose a precise method of estimation by selecting the most popular models in order to improve accuracy. Consequently, the final results are very precise and reliable when they are applied to a real dataset in a software project. Empirical validation of this approach uses the International Software Benchmarking Standards Group (ISBSG) Data Repository Version 10 to demonstrate the improvement in software estimation accuracy.
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Yücalar, Fatih, Deniz Kilinc, Emin Borandag, and Akin Ozcift. "Regression Analysis Based Software Effort Estimation Method." International Journal of Software Engineering and Knowledge Engineering 26, no. 05 (June 2016): 807–26. http://dx.doi.org/10.1142/s0218194016500261.

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Estimating the development effort of a software project in the early stages of the software life cycle is a significant task. Accurate estimates help project managers to overcome the problems regarding budget and time overruns. This paper proposes a new multiple linear regression analysis based effort estimation method, which has brought a different perspective to the software effort estimation methods and increased the success of software effort estimation processes. The proposed method is compared with standard Use Case Point (UCP) method, which is a well-known method in this area, and simple linear regression based effort estimation method developed by Nassif et al. In order to evaluate and compare the proposed method, the data of 10 software projects developed by four well-established software companies in Turkey were collected and datasets were created. When effort estimations obtained from datasets and actual efforts spent to complete the projects are compared with each other, it has been observed that the proposed method has higher effort estimation accuracy compared to the other methods.
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Bajaj, Nonika, Alok Tyagi, and Rakesh Agarwal. "Software estimation." ACM SIGSOFT Software Engineering Notes 31, no. 3 (May 2006): 1–5. http://dx.doi.org/10.1145/1127878.1127881.

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Ayyıldız, Tülin Erçelebi, and Hasan Can Terzi. "Case Study on Software Effort Estimation." International Journal of Information and Electronics Engineering 7, no. 3 (May 2017): 103–7. http://dx.doi.org/10.18178/ijiee.2017.7.3.670.

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Jain, Parita, Arun Sharma, and Laxmi Ahuja. "Software Maintainability Estimation in Agile Software Development." International Journal of Open Source Software and Processes 9, no. 4 (October 2018): 65–78. http://dx.doi.org/10.4018/ijossp.2018100104.

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Agile methodologies have gained wide acceptance for developing high-quality products with a quick and flexible approach. However, until now, the quality of the agile process has not been validated quantitatively. Quality being important for the software system, there is a need for measurement. Estimating different quality factors will lead to a quality product. Also, agile software development does not provide any precise models to evaluate maintainability. Therefore, there is a need for an algorithmic approach that can serve as the basis for estimation of maintainability. The article proposes an adaptive neuro-fuzzy inference system (ANFIS) model for estimating agile maintainability. Maintainability is one of the prominent quality factors in the case of agile development. The proposed model has been verified and found to be effective for assessing the maintainability of agile software.
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Shivakumar, Shailesh Kumar. "Software Estimation Framework for Packaged Products." International Journal of Project Management and Productivity Assessment 9, no. 1 (January 2021): 15–24. http://dx.doi.org/10.4018/ijpmpa.2021010102.

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Packaged products play a major role in successful implementation of various software projects. Many of the software solutions are built around packaged products. In this paper, the authors propose a novel “software packaged product estimation framework” for an end to end estimation framework for estimating effort for packaged products. The software packaged product estimation framework provides end to end estimation coverage for various project lifecycle stages and supporting activities. The software packaged product estimation framework was used to predict the effort for two projects with MMRE of 0.261 and pred(0.3) of 66.67%.
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., E. Karunakaran. "EXTREME SOFTWARE ESTIMATION (XSOFT ESTIMATION)." International Journal of Research in Engineering and Technology 02, no. 12 (December 25, 2013): 366–73. http://dx.doi.org/10.15623/ijret.2013.0212063.

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Ferdiana, Ridi, Paulus Insap Santoso, Lukito Edi Nugroho, and Ahmad Ashari. "USER STORY SOFTWARE ESTIMATION:A SIMPLIFICATION OF SOFTWARE ESTIMATION MODEL WITH DISTRIBUTED EXTREME PROGRAMMING ESTIMATION TECHNIQUE." JUTI: Jurnal Ilmiah Teknologi Informasi 9, no. 1 (January 1, 2011): 41. http://dx.doi.org/10.12962/j24068535.v9i1.a67.

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Chatzipetrou, Panagiota. "Software Cost Estimation." International Journal of Service Science, Management, Engineering, and Technology 10, no. 3 (July 2019): 14–31. http://dx.doi.org/10.4018/ijssmet.2019070102.

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Software cost estimation (SCE) is a critical phase in software development projects. A common problem in building software cost models is that the available datasets contain projects with lots of missing categorical data. There are several techniques for handling missing data in the context of SCE. The purpose of this article is to show a state-of-art statistical and visualization approach of evaluating and comparing the effect of missing data on the accuracy of cost estimation models. Five missing data techniques were used: multinomial logistic regression, listwise deletion, mean imputation, expectation maximization and regression imputation; and compared with respect to their effect on the prediction accuracy of a least squares regression cost model. The evaluation is based on various expressions of the prediction error. The comparisons are conducted using statistical tests, resampling techniques and visualization tools like the regression error characteristic curves.
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Deng, Jeremiah D., Martin Purvis, and Maryam Purvis. "Software Effort Estimation." International Journal of Intelligent Information Technologies 7, no. 3 (July 2011): 41–53. http://dx.doi.org/10.4018/jiit.2011070104.

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Software development effort estimation is important for quality management in the software development industry, yet its automation still remains a challenging issue. Applying machine learning algorithms alone often cannot achieve satisfactory results. This paper presents an integrated data mining framework that incorporates domain knowledge into a series of data analysis and modeling processes, including visualization, feature selection, and model validation. An empirical study on the software effort estimation problem using a benchmark dataset shows the necessity and effectiveness of the proposed approach.
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Dissertations / Theses on the topic "Estimation software"

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Jhunjhunwala, Manish. "Software tool for reliability estimation." Morgantown, W. Va. : [West Virginia University Libraries], 2001. http://etd.wvu.edu/templates/showETD.cfm?recnum=1801.

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Thesis (M.S.)--West Virginia University, 2001.
Title from document title page. Document formatted into pages; contains x, 125 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 72-74).
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Park, In Kyoung, Dan C. Boger, and Michael G. Sovereign. "Software cost estimation through Bayesian inference of software size." Thesis, Monterey, California. Naval Postgraduate School, 1985. http://hdl.handle.net/10945/21547.

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Wang, Zhihua 1970. "Value estimation for software development processes." Thesis, McGill University, 2004. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=81576.

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The management of software development processes is a continual challenge facing software development organizations. Previous studies used "flexible models" and empirical methods to optimize software development processes. In this thesis, the expected payoff is used to quantitatively evaluate processes. Payoff can be defined as the value of a team member's action, and the expected payoff combines the value of the payoff of a team member's action and the probability of taking that action. The mathematic models of a waterfall process and two flexible processes are evaluated in terms of total maximum expected payoff. The results show under which conditions which process is more valuable. An overview of this work and results will be presented in this seminar.
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Strike, Kevin D. "Software cost estimation with incomplete data." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/mq64461.pdf.

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Fernández, Ramil Juan Carlos. "Continual resource estimation for evolving software." Thesis, Imperial College London, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.404810.

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Hamdan, Khaled. "An investigation into software estimation methods." Thesis, University of Sunderland, 2009. http://sure.sunderland.ac.uk/3557/.

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There are currently no fully validated estimation approaches that can accurately predict the effort needed for developing a software system (Kitchenham, et al, 1995). Information gathered at the early stages of system development is not enough to provide precise effort estimates, even though similar software systems may have been developed in the past. Where similar systems have been developed, there are often inherent differences in the features of these systems and in the development process used. These differences are often sufficient to significantly reduce estimation accuracy. Historically, cost estimation focuses on project effort and duration. There are many estimation techniques, but none is consistently ‘best’ (Shepperd, 2003). Software project management has become a crucial field of research due to the increasing role of software in today’s world. Improving the functions of project management is a main concern in software development organisation. The purpose of this thesis is to develop a new model which incorporates cultural and leadership factors in the cost estimation model, and is based on Case-Based Reasoning. The thesis defines a new knowledge representation “ontology” to provide a common understanding of project parameters. The associated system uses a statistically simulated bootstrap method, which helps in tuning the analogy approach before application to real projects. This research also introduces a new application of Profile Theory, which takes a formal approach to the measurement of leadership capabilities. A pilot study was performed in order to understand the approaches used for cost estimation in the Gulf region. Based on this initial study, a questionnaire was further refined and tested. Consequently, further surveys were conducted in the United Arab Emirates. It was noticed that most of the software development projects failed in terms of cost estimate. This was due to the lack of a precise software estimation model. These studies also highlighted the importance of leadership and culture in software cost estimation. Effort was estimated using regression and analogy. The Bootstrap method was used to refine the estimate of effort based on analogy, with correction for bias. Due to the very different nature of the core and support systems, a separate model was developed for each of them. As a result of the study, a new model for identifying and analysing was developed. The model was then evaluated, and conclusions were drawn. These show the importance of the model and the factors of organisational culture and leadership in software project development and in cost estimation. Potential areas for future research were identified.
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Shepherd, Kristen Piggott. "A Comparison of Coalescent Estimation Software." Diss., CLICK HERE for online access, 2002. http://contentdm.lib.byu.edu/ETD/image/etd145.pdf.

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Oyamada, Marcio Seiji. "Software performance estimation in MPSoC design." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2007. http://hdl.handle.net/10183/12674.

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Atualmente, novas metodologias de projeto são necessárias devido a crescente complexidade dos sistemas embarcados. Metodologias no nível de sistema são propostas para auxiliar o projetista a lidar com a crescente complexidade, iniciando o projeto em um nível de abstração mais alto que o nível de transferência de registradores. Ferramentas de estimativa de desempenho são uma importante parte das metodologias no nível de sistema, visto que as mesmas auxiliam a exploração do espaço de projeto desde os estágios iniciais. O objetivo desta tese é definir uma metodologia integrada para estimativa de desempenho do software. Atualmente, nota-se a crescente utilização de software embarcado, inclusive utilizando múltiplos processadores, visando atender os requisitos de flexibilidade, desempenho e potência consumida. O desenvolvimento de estimadores de desempenho de software não é trivial, devido à utilização de processadores embarcados com arquiteturas avançadas. Para auxiliar a seleção do processador no nível da especificação do sistema, um novo modelo de estimador do desempenho do software baseado em redes neurais é proposto. Redes neurais mostraram-se uma solução adequada para uma rápida estimativa de desempenho em um estágio inicial do projeto. Para realizar a análise do desempenho do software no nível funcional do barramento, onde o mapeamento do hardware e software já está definido, é utilizado um modelo global de simulação, chamado de protótipo virtual. A metodologia de análise de desempenho proposta neste trabalho é integrada a um ambiente para refinamento de interfaces de hardware e software chamada ROSES. A metodologia proposta é avaliada através de um estudo de caso de uma arquitetura multiprocessada de um codificador MPEG4.
Nowadays, embedded system complexity requires new design methodologies. System-level methodologies are proposed to cope with this complexity, starting the design above the register-transfer level. Performance estimation tools are an important piece of system-level design methodologies, since they are used to aid design space exploration at an early design stage. The goal of this thesis is to define an integrated methodology for software performance estimation. Currently, embedded software usage is increasing, becoming multiprocessor system-on-chip a common solution to cope with flexibility, performance, and power requirements. The development of accurate software performance estimators is not trivial, due to the increased complexity of embedded processors. To drive processor selection at specification level, a novel analytic software performance estimator based on neural networks is proposed. The neural network enables a fast estimation at an early design stage. To target the software performance analysis at bus functional level, where mapping of the hardware and software components is already established, we use a global simulation model supporting performance profiling. The proposed software performance estimation methodology is linked to a hardware and software interface refinement environment named ROSES. The proposed methodology is evaluated through a case study of a multiprocessor MPEG4 encoder.
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Henry, Troy Steven. "Architecture-Centric Project Estimation." Thesis, Virginia Tech, 2007. http://hdl.handle.net/10919/32756.

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In recent years studies have been conducted which suggest that taking an architecture first approach to managing large software projects can reduce a significant amount of the uncertainty present in project estimates. As the project progresses, more concrete information is known about the planned system and less risk is present. However, the rate at which risk is alleviated varies across the life-cycle. Research suggests that there exists a significant drop off in risk when the architecture is developed. Software risk assessment techniques have been developed which attempt to quantify the amount of risk that varying uncertainties convey to a software project. These techniques can be applied to architecture specific issues to show that in many cases, conducting an architecture centric approach to development will remove more risk than the cost of developing the architecture. By committing to developing the architecture prior to the formal estimation process, specific risks can be more tightly bounded, or even removed from the project. The premise presented here is that through the process of architecture-centric management, it is possible to remove substantial risk from the project. This decrease in risk exceeds that at other phases of the life-cycle, especially in comparison of the effort involved. Notably, at architecture, a sufficient amount knowledge is gained by which effort estimations may be tightly bounded, yet the project is early enough in the life-cycle for proper planning and scheduling. Thus, risk is mitigated through the increase in knowledge and the ability to maintain options at an early point. Further, architecture development and evaluation has been shown to incorporate quality factors normally insufficiently considered in the system design. The approach taken here is to consider specific knowledge gained through the architecting process and how this is reflected in parametric effort estimation models. This added knowledge is directly reflected in risk reduction. Drawing on experience of architecture researchers as well as project managers employing this approach, this thesis considers what benefits to the software development process are gained by taking this approach. Noting a strong reluctance of owners to incorporate solid software engineering practices, the thesis concludes with an outline for an experiment which goes about proving the reduction in risk at architecture exceeds the cost of that development.
Master of Science
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Leinonen, J. (Juho). "Evaluating software development effort estimation process in agile software development context." Master's thesis, University of Oulu, 2016. http://urn.fi/URN:NBN:fi:oulu-201605221862.

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This thesis studied effort estimation in software development, focusing on task level estimation that is done in Scrum teams. The thesis was done at Nokia Networks and the motivation for this topic came from the poor estimation accuracy that has been found to be present in software development. The aim of this thesis was to provide an overview of what is the current state of the art in effort estimation, survey the current practices present in Scrum teams working on LTE L2 software component at Nokia Networks Oulu, and then present suggestions for improvement based on the findings. On the basis of the literature review, three main categories of effort estimation methods were found: expert estimation, algorithmic models and machine learning. Universally there did not seem to be a single best method, but instead the differences come from the context of use. Algorithmic models and machine learning require data sets, whereas expert estimation methods rely on previous experiences and intuition of the experts. While model based methods have received a lot of research attention, the industry has largely relied on expert estimation. The current state of effort estimation at Nokia Networks was studied by conducting a survey. This survey was built based on previous survey studies that were found by conducting a systematic literature review. The questions found in the previous studies were formulated into a questionnaire, which was then used to survey the current effort estimation practices present in the participating teams. 41 people out of 100 in the participating teams participated in the survey. Survey results showed that like much of the software industry, the teams in LTE L2 relied on expert estimation methods. Most respondents had encountered overruns in the last sprint and the most often provided reason was that testing related effort estimation was hard. Forgotten subtasks were encountered frequently and requirements were found to be both unclear and to change often. Very few had had any training on effort estimation. There were no common practices for effort data collection and as such, it was mostly not done. By analyzing the survey results and reflecting them on the previous research, five suggestions for improvements were found. These were training in effort estimation, improving the information that is used during effort estimation by collaborating with specification personnel, improving testing related effort estimation by splitting acceptance testing into their own tasks, collecting and using effort data, and using Planning Poker as an effort estimation method, as it fit the context of estimation present in the teams. The study shed light on how effort estimation is done in software industry. Another contribution was the improvement suggestions, which could potentially improve the situation in the teams that participated in the survey. A third contribution was the questionnaire built during this study, as it could potentially be used to survey the current state of effort estimation in also other contexts.
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Books on the topic "Estimation software"

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Abran, Alain. Software Project Estimation. Hoboken, NJ, USA: John Wiley & Sons, Inc, 2015. http://dx.doi.org/10.1002/9781118959312.

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Dimitrov, Dimitre. Software Project Estimation. Berkeley, CA: Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-5025-9.

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Marlow, A. R. Software performance estimation tool. Manchester: UMIST, 1994.

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Laird, Linda M. Software Measurement and Estimation. New York: John Wiley & Sons, Ltd., 2006.

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Trendowicz, Adam, and Ross Jeffery. Software Project Effort Estimation. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-03629-8.

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Laird, Linda M., and M. Carol Brennan. Software Measurement and Estimation. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2006. http://dx.doi.org/10.1002/0471792535.

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Londeix, Bernard. Cost estimation for software development. Wokingham, England: Addison-Wesley, 1987.

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1954-, Brennan M. Carol, ed. Practical estimation in software engineering. Hoboken, N.J: John Wiley & Sons, 2006.

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Park, In Kyoung. Software cost estimation through Bayesian inference of software size. Monterey, Calif: Naval Postgraduate School, 1985.

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Vesterinen, Petri. On effort estimation in software projects. Lappeenranta, Finland: Lappeenranta University of Technology, 2000.

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Book chapters on the topic "Estimation software"

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Dimitrov, Dimitre. "Assertions." In Software Project Estimation, 1–2. Berkeley, CA: Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-5025-9_1.

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Dimitrov, Dimitre. "The People in a Software Project." In Software Project Estimation, 3–11. Berkeley, CA: Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-5025-9_2.

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Dimitrov, Dimitre. "The Role of Simplification." In Software Project Estimation, 13–20. Berkeley, CA: Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-5025-9_3.

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Dimitrov, Dimitre. "Statistics and Probabilities." In Software Project Estimation, 21–30. Berkeley, CA: Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-5025-9_4.

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Dimitrov, Dimitre. "Forecasting Mechanics." In Software Project Estimation, 31–57. Berkeley, CA: Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-5025-9_5.

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Dimitrov, Dimitre. "Adjustments." In Software Project Estimation, 59–76. Berkeley, CA: Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-5025-9_6.

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Dimitrov, Dimitre. "Financial Performance and Managing Risks." In Software Project Estimation, 77–94. Berkeley, CA: Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-5025-9_7.

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Tuerlinckx, Francis, Frank Rijmen, Geert Molenberghs, Geert Verbeke, Derek Briggs, Wim Van den Noortgate, Michel Meulders, and Paul De Boeck. "Estimation and software." In Explanatory Item Response Models, 343–73. New York, NY: Springer New York, 2004. http://dx.doi.org/10.1007/978-1-4757-3990-9_12.

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Trendowicz, Adam, and Ross Jeffery. "Estimation Under Uncertainty." In Software Project Effort Estimation, 81–124. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-03629-8_4.

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Trendowicz, Adam, and Ross Jeffery. "Basic Estimation Strategies." In Software Project Effort Estimation, 125–52. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-03629-8_5.

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Conference papers on the topic "Estimation software"

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Pospieszny, Przemyslaw. "Software estimation." In IWSM/Mensura '17: 27th International Workshop on Software Measurement and 12th International Conference on Software Process and Product Measurement. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3143434.3143459.

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Boehm, Barry W. "Software Cost Estimation Meets Software Diversity." In 2017 IEEE/ACM 39th International Conference on Software Engineering Companion (ICSE-C). IEEE, 2017. http://dx.doi.org/10.1109/icse-c.2017.159.

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SCHULTZ, RICHARD, and RAYMOND KILE. "The software estimation process." In 30th Aerospace Sciences Meeting and Exhibit. Reston, Virigina: American Institute of Aeronautics and Astronautics, 1992. http://dx.doi.org/10.2514/6.1992-470.

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Langridge, A. "Software estimation - an introduction." In 2nd IEE Conference on Automotive Electronics. IEE, 2006. http://dx.doi.org/10.1049/ic:20060577.

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Liao, Hancheng. "Research on software development estimation." In 2009 IEEE International Conference on Granular Computing (GRC). IEEE, 2009. http://dx.doi.org/10.1109/grc.2009.5255090.

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Tariq, Sidra, Muhammad Usman, Raymond Wong, Yan Zhuang, and Simon Fong. "On Learning Software Effort Estimation." In 2015 3rd International Symposium on Computational and Business Intelligence (ISCBI). IEEE, 2015. http://dx.doi.org/10.1109/iscbi.2015.21.

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Yang, Ye, and Linda Laird. "Teaching Software Estimation through LEGOs." In 2016 IEEE 29th International Conference on Software Engineering Education and Training (CSEET). IEEE, 2016. http://dx.doi.org/10.1109/cseet.2016.22.

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Kopetschny, Chris, Morgan Ericsson, Welf Löwe, and Anna Wingkvist. "Optimization of Software Estimation Models." In 14th International Conference on Software Technologies. SCITEPRESS - Science and Technology Publications, 2019. http://dx.doi.org/10.5220/0008117701410150.

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Sarro, Federica, Alessio Petrozziello, and Mark Harman. "Multi-objective software effort estimation." In ICSE '16: 38th International Conference on Software Engineering. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2884781.2884830.

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Okamura, Hiroyuki, Yuki Takekoshi, and Tadashi Dohi. "Fine-Grained Software Reliability Estimation Using Software Testing Inputs." In 2015 IEEE International Conference on Software Quality, Reliability and Security (QRS). IEEE, 2015. http://dx.doi.org/10.1109/qrs.2015.22.

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Reports on the topic "Estimation software"

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SOFTWARE PRODUCTIVITY CONSORTIUM HERNDON VA. Software Estimation Program (SWEEP). Fort Belvoir, VA: Defense Technical Information Center, September 1994. http://dx.doi.org/10.21236/ada291137.

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Barrow, Dean, Susan Nilson, and Dawn Timberlake. Software Estimation Technology Report. Fort Belvoir, VA: Defense Technical Information Center, March 1993. http://dx.doi.org/10.21236/ada294366.

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Silver, Aaron N. Software Cost Estimation Study. Fort Belvoir, VA: Defense Technical Information Center, August 1990. http://dx.doi.org/10.21236/ada226777.

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Ross, S. M. Statistical Estimation of Software Reliability. Fort Belvoir, VA: Defense Technical Information Center, March 1985. http://dx.doi.org/10.21236/ada154097.

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Boehm, Barry, Jo Ann Lane, Bradford Clark, Thomas Tan, Ramin Moazeni, Ray Madachy, and Wilson Rosa. Software Intensive Systems Cost and Schedule Estimation. Fort Belvoir, VA: Defense Technical Information Center, June 2013. http://dx.doi.org/10.21236/ada608124.

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SOFTWARE PRODUCTIVITY CONSORTIUM HERNDON VA. Software Error Estimation Program (SWEEP) Version 02.00.00 User Manual. Fort Belvoir, VA: Defense Technical Information Center, December 1993. http://dx.doi.org/10.21236/ada274697.

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Fraley, Chris, and Adrian E. Raftery. MCLUST: Software for Model-Based Clustering, Density Estimation and Discriminant Analysis. Fort Belvoir, VA: Defense Technical Information Center, October 2002. http://dx.doi.org/10.21236/ada459792.

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Klema, V. Numerical Algorithms and Mathematical Software for Linear Control and Estimation Theory. Fort Belvoir, VA: Defense Technical Information Center, May 1985. http://dx.doi.org/10.21236/ada157525.

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Berman, R. G. Thermobarometry with estimation of equilibration state [TWEEQU]: an IBM-compatible software package. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2006. http://dx.doi.org/10.4095/222822.

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Keith, B., A. Apostolatos, A. Kodakkal, R. Rossi, R. Tosi, B. Wohlmuth, and C. Soriano. D2.3. Adjoint-based error estimation routines. Scipedia, 2021. http://dx.doi.org/10.23967/exaqute.2021.2.022.

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Abstract:
This document presents a simple and ecient strategy for adaptive mesh renement (AMR) and a posteriori error estimation for the transient incompressible Navier{Stokes equations. This strategy is informed by the work of Prudhomme and Oden [22, 23] as well as modern goal-oriented methods such as [5]. The methods described in this document have been implemented in the Kratos Multiphysics software and uploaded to https://zenodo.org [27].1 This document includes: A review of the state-of-the-art in solution-oriented and goal-oriented AMR. The description of a 2D benchmark model problem of immediate relevance to the objectives of the ExaQUte project. The denition and a brief mathematical summary of the error estimator(s). The results obtained. A description of the API.
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