This document describes an empirical study that compared the effectiveness of two variability management approaches for software product lines (SPLs) at the UML class level: PLUS and SMarty. The study found that PLUS was more effective at identifying and representing variabilities in class models. Based on participant feedback, guidelines were improved for SMarty and a new experiment is planned to evaluate the updated SMarty approach against PLUS. The results provide evidence that PLUS is currently more effective but further studies are needed to generalize findings and potentially improve SMarty's effectiveness.
Operations research originated during World War II when scientists applied scientific methods to military operations. It has since been applied to many domains including business, transportation, and public health. Some key OR techniques include linear programming, transportation models, assignment problems, queuing theory, simulation, and inventory control models. The OR process involves formulating the problem, developing a mathematical model, selecting data inputs, solving the model, validating the model, and implementing the solution. Models can be classified as deterministic or stochastic, descriptive, predictive, or prescriptive, static or dynamic, and analytical or simulation-based. OR aims to help management make better decisions through quantitative analysis and optimization of systems and processes.
This document summarizes a method for designing choice menus for mass customization. The method involves 3 phases: 1) Identifying relevant product attributes through focus groups and customer analysis. Attributes are organized in a table by customer segments if identified. 2) Obtaining attribute importance weights and formally clustering customers through a questionnaire. 3) Modeling customer preferences using stated preference techniques to design optimized choice menus tailored for different customer segments. The goal is to balance flexibility and complexity in the menus. A real-world case application in a utility company supported the method's ability to elicit customer preferences for choice menu design.
This document discusses conjoint analysis and provides an example using SPSS. It defines conjoint analysis as a technique used to understand how consumers develop preferences for product attributes. The key steps are identified as identifying the problem, attributes and levels, methodology, collecting responses, analysis, interpretation and application. Types include traditional, adaptive choice-based conjoint analysis. An example uses attributes of cars to identify preferred combinations through partial profile surveys and estimating utilities in SPSS. The results show price, fuel type and model have most importance in driving sales.
This document provides an overview of conjoint analysis, a research method used to determine how consumers value different attributes or features of a product. It discusses key aspects of conjoint analysis including: defining factors and values to study, creating profiles for respondents to rate, estimating utility values for each attribute from the ratings, and aggregating results across respondents. The example provided examines preferences for different attributes of dishwashing products like scent, packaging, and design.
This document outlines the syllabus for the course CS8592 - Object Oriented Analysis and Design (V-Semester). The objectives of the course are to understand object modeling fundamentals, the Unified Process approach, and designing with UML diagrams. The syllabus is divided into 5 units covering topics like the Unified Process, use case modeling, static UML diagrams, dynamic/implementation diagrams, design patterns, and testing. The outcomes are for students to be able to express software design with UML, identify scenarios based on requirements, transform designs into pattern-based designs, and understand various OO testing methodologies.
This document discusses object-oriented analysis and design (OOAD) and the unified process. It introduces OOAD and the unified process framework, which includes inception, elaboration, construction, and transition phases. It also covers the unified modeling language (UML), including use case diagrams, class diagrams, and other diagram types. Specific topics covered include identifying actors and use cases, drawing associations and relationships between actors and use cases, class notation, and an example use case diagram for an alarm clock system.
Operations research originated during World War II when scientists applied scientific methods to military operations. It has since been applied to many domains including business, transportation, and public health. Some key OR techniques include linear programming, transportation models, assignment problems, queuing theory, simulation, and inventory control models. The OR process involves formulating the problem, developing a mathematical model, selecting data inputs, solving the model, validating the model, and implementing the solution. Models can be classified as deterministic or stochastic, descriptive, predictive, or prescriptive, static or dynamic, and analytical or simulation-based. OR aims to help management make better decisions through quantitative analysis and optimization of systems and processes.
This document summarizes a method for designing choice menus for mass customization. The method involves 3 phases: 1) Identifying relevant product attributes through focus groups and customer analysis. Attributes are organized in a table by customer segments if identified. 2) Obtaining attribute importance weights and formally clustering customers through a questionnaire. 3) Modeling customer preferences using stated preference techniques to design optimized choice menus tailored for different customer segments. The goal is to balance flexibility and complexity in the menus. A real-world case application in a utility company supported the method's ability to elicit customer preferences for choice menu design.
This document discusses conjoint analysis and provides an example using SPSS. It defines conjoint analysis as a technique used to understand how consumers develop preferences for product attributes. The key steps are identified as identifying the problem, attributes and levels, methodology, collecting responses, analysis, interpretation and application. Types include traditional, adaptive choice-based conjoint analysis. An example uses attributes of cars to identify preferred combinations through partial profile surveys and estimating utilities in SPSS. The results show price, fuel type and model have most importance in driving sales.
This document provides an overview of conjoint analysis, a research method used to determine how consumers value different attributes or features of a product. It discusses key aspects of conjoint analysis including: defining factors and values to study, creating profiles for respondents to rate, estimating utility values for each attribute from the ratings, and aggregating results across respondents. The example provided examines preferences for different attributes of dishwashing products like scent, packaging, and design.
This document outlines the syllabus for the course CS8592 - Object Oriented Analysis and Design (V-Semester). The objectives of the course are to understand object modeling fundamentals, the Unified Process approach, and designing with UML diagrams. The syllabus is divided into 5 units covering topics like the Unified Process, use case modeling, static UML diagrams, dynamic/implementation diagrams, design patterns, and testing. The outcomes are for students to be able to express software design with UML, identify scenarios based on requirements, transform designs into pattern-based designs, and understand various OO testing methodologies.
This document discusses object-oriented analysis and design (OOAD) and the unified process. It introduces OOAD and the unified process framework, which includes inception, elaboration, construction, and transition phases. It also covers the unified modeling language (UML), including use case diagrams, class diagrams, and other diagram types. Specific topics covered include identifying actors and use cases, drawing associations and relationships between actors and use cases, class notation, and an example use case diagram for an alarm clock system.
O documento descreve o desenvolvimento de um sistema de gerenciamento de clínicas de psicologia usando o Java EE Web Profile. O sistema permite agendamento de pacientes, registro de consultas e controle financeiro, melhorando sobre o método anterior de uso de papel. O sistema foi construído usando JPA, EJB, JSF e implementou padrões como camada de domínio, negócios e apresentação.
UM ESTUDO SOBRE GERENCIAMENTO DE VARIABLIDADES EM LINHAS DE PROCESSO DE SOFTWAREEdson Oliveira Junior
Este documento apresenta um estudo sobre gerenciamento de variabilidades em linhas de processo de software. Ele propõe uma abordagem chamada SMartySPEM que combina as abordagens SMarty e SPEM 2.0 para representar variabilidades em linhas de processo utilizando a notação UML. O documento também descreve uma revisão sistemática realizada sobre o tema e apresenta um exemplo de aplicação da abordagem proposta.
Apresentação do Artigo de Joyce Mathias no FITEM 2012 - Métodos e Técnicas de Desenvolvimento de Linha de Produto de Software para Sistemas E-Commerce: um Mapeeamento Sistemático
Proposta de uma Abordagem Formal para o Gerenciamento de Variabilidades em Mo...Edson Oliveira Junior
A proposta discute uma abordagem formal para o gerenciamento de variabilidades em modelos UML usando a Object Constraint Language (OCL). O trabalho revisa conceitos como linhas de produto de software, diagrama de interação, package merge e OCL e propõe objetivos como estender a abordagem SMarty e aplicar OCL para validar modelos UML no gerenciamento de variabilidade.
Extensão da Abordagem SMarty de Gerenciamento de Variabilidade para Sistemas ...Edson Oliveira Junior
Este documento discute a extensão da abordagem SMarty de gerenciamento de variabilidade para sistemas embarcados modelados com SysML, adicionando novos estereótipos ao perfil SMartyProfile e diretrizes ao processo SMartyProcess. O trabalho tem como objetivo geral estender a abordagem SMarty para representar e gerenciar variabilidade em linhas de produtos de software para sistemas embarcados.
O documento define revisão sistemática e descreve os principais passos para realizar uma, incluindo: definir uma pergunta norteadora, buscar evidências de forma sistemática, selecionar estudos de acordo com critérios de inclusão e exclusão, avaliar a qualidade metodológica dos estudos selecionados e apresentar os resultados. Além disso, fornece exemplos de revisões sistemáticas realizadas em engenharia de software e ciência da computação.
Um Protótipo Web do Módulo de Planejamento de Avaliações de Linha de Produto ...Edson Oliveira Junior
Este documento descreve um protótipo web para planejamento de avaliações de linha de produto de software usando o método SystEM-PLA. O protótipo permite visualizar modelos de características, variabilidades e relacionamentos entre artefatos usando o parser SMartyParser. Ele é construído com o framework GWT para permitir acesso aos dados extraídos de arquivos XMI sobre a linha de produto AGM.
This document provides an overview of a project report on simulating a single server queuing problem. The report includes an introduction to operations research, simulation, and the queuing problem. It discusses the research methodology, which involves defining the problem, developing a simulation model, validating the model, analyzing the data, and presenting findings and recommendations. The goal is to use simulation to provide optimal solutions to the queuing problem under study.
Understanding the Applicability of Linear & Non-Linear Models Using a Case-Ba...ijaia
This paper uses a case based study – “product sales estimation” on real-time data to help us understand
the applicability of linear and non-linear models in machine learning and data mining. A systematic
approach has been used here to address the given problem statement of sales estimation for a particular set
of products in multiple categories by applying both linear and non-linear machine learning techniques on
a data set of selected features from the original data set. Feature selection is a process that reduces the
dimensionality of the data set by excluding those features which contribute minimal to the prediction of the
dependent variable. The next step in this process is training the model that is done using multiple
techniques from linear & non-linear domains, one of the best ones in their respective areas. Data Remodeling
has then been done to extract new features from the data set by changing the structure of the
dataset & the performance of the models is checked again. Data Remodeling often plays a very crucial and
important role in boosting classifier accuracies by changing the properties of the given dataset. We then try
to explore and analyze the various reasons due to which one model performs better than the other & hence
try and develop an understanding about the applicability of linear & non-linear machine learning models.
The target mentioned above being our primary goal, we also aim to find the classifier with the best possible
accuracy for product sales estimation in the given scenario.
This document provides an overview of modeling and simulation. It defines modeling as representing a system to enable predicting the effects of changes. Simulation involves running experiments on a model. The key steps in modeling and simulation projects are: 1) identifying the problem, 2) formulating and developing the model, 3) validating the model, 4) designing simulation experiments, 5) performing simulations, and 6) analyzing and presenting results. Modeling and simulation can be used for a variety of purposes including education, design evaluation, forecasting, and risk assessment.
LNCS 5050 - Bilevel Optimization and Machine Learningbutest
This document discusses using bilevel optimization and machine learning techniques to improve model selection in machine learning problems. It proposes framing machine learning model selection as a bilevel optimization problem, where the inner level problems involve optimizing models on training data and the outer level problem selects hyperparameters to minimize error on test data. This bilevel framing allows for systematic optimization of hyperparameters and enables novel machine learning approaches. The document illustrates the approach for support vector regression, formulating model selection as a Stackelberg game and solving the resulting mathematical program with equilibrium constraints.
The document provides an introduction to structural equation modeling (SEM). It discusses key concepts such as latent and observed variables, and measurement models. It also presents examples of confirmatory factor analysis output to illustrate model fitting and interpretation. Specifically, it analyzes a four-factor CFA model with academic self-concept variables and reports various goodness-of-fit statistics and parameter estimates to assess how well the hypothesized model fits the sample data.
The document proposes a Response Aware Probabilistic Matrix Factorization (RAPMF) framework to address limitations in existing collaborative filtering recommendation systems. RAPMF incorporates users' response patterns into probabilistic matrix factorization by modeling responses as a Bernoulli distribution for observed ratings and a step function for unobserved ratings. This allows marginalizing missing responses. The authors also develop a mini-batch implementation of RAPMF to reduce computational costs from O(N×M) to O(B2) for mini-batches of B users and items. Experimental evaluation on synthetic and real-world datasets demonstrates the merits of RAPMF, including improved performance and reduced training time compared to other methods.
Operational research (OR) is the scientific approach to problem solving and decision making. It involves modeling complex real-world situations and using analytical methods to evaluate solutions and help decision makers choose optimal alternatives. Some key OR techniques include linear programming, simulation, and data analysis. OR has been successfully applied in many fields like transportation, manufacturing, healthcare, and the airline industry to improve efficiency, maximize profits, and aid strategic planning. The document provides an overview of OR methodology, history, applications, and examples of its use.
EXTRACTING THE MINIMIZED TEST SUITE FOR REVISED SIMULINK/STATEFLOW MODELijaia
Test case generation techniques are successfully employed to generate test cases from a formal model. A problem is that as the model evolves, test suites tend to grow in size, making it too costly to execute entire test suites. This paper aims to propose a practical approach to reduce the size of test suites for modified Simulink/Stateflow (SL/SF) model, which is popularly used for modeling software behavior in many industries like automobile manufacturers. The model for describing a system is frequently modified until it is fixed. The proposed technique is capable of extracting the minimized sized test suite in terms of test coverage, by taking into account both the modified and the affected portion of revised SL/SF model. Two real models for the ECUs deployed in a commercial car are used for an empirical study.
Bio-Inspired Requirements Variability Modeling with use Case ijseajournal
Background.Feature Model (FM) is the most important technique used to manage the variability through products in Software Product Lines (SPLs). Often, the SPLs requirements variability is by using variable use case modelwhich is a real challenge inactual approaches: large gap between their concepts and those of real world leading to bad quality, poor supporting FM, and the variability does not cover all requirements modeling levels. Aims. This paper proposes a bio-inspired use case variability modeling methodology dealing with the above shortages.
Method. The methodology is carried out through variable business domain use case meta modeling,
variable applications family use case meta modeling, and variable specific application use case generating.
Results. This methodology has leaded to integrated solutions to the above challenges: it decreases the gap
between computing concepts and real world ones. It supports use case variability modeling by introducing
versions and revisions features and related relations. The variability is supported at three meta levels
covering business domain, applications family, and specific application requirements.
Conclusion. A comparative evaluation with the closest recent works, upon some meaningful criteria in the
domain, shows the conceptual and practical great value of the proposed methodology and leads to
promising research perspectives
Introduction to modeling_and_simulationAysun Duran
1. This document provides an introduction to modeling and simulation. It discusses what modeling and simulation are, and the types of problems they can address.
2. Simulation involves operating a model of a system to study the system's properties without actually changing the real system. It allows experimenting with different configurations to evaluate and optimize system performance.
3. Developing a simulation model involves identifying the system components and relationships between them. Validating the model and performing simulation experiments then allows making recommendations to improve the real system.
This document provides an overview of modeling and simulation. It discusses what modeling and simulation are, the steps in developing a simulation model, designing a simulation experiment, and analyzing simulation output. It also provides an example of simulating a machine shop with multiple work stations to determine resource utilization under different arrival patterns. The key steps in a simulation study are problem formulation, model development, experiment design, output analysis and recommendations.
Calibration and Validation of Micro-Simulation ModelsWSP
Calibration and Validation of Micro-Simulation Models is a presentation delivered by François Bélisle, Eng., B.Sc., M.Sc., WSP | Parsons Brinckerhoff, Laurent Gauthier, Polytechnique Montréal and Nicolas Saunier, Polytechnique Montréal at the 2015 Transportation Association of Canada (TAC) Conference & Exhibition, from September 27 to 30.
Conceptualization of a Domain Specific Simulator for Requirements Prioritizationresearchinventy
This paper conceptualizes a domain specific simulator for requirements prioritization; its aims at helping to identify appropriate prioritization strategies for a project in hand. The possible existing scenarios are difficult to analyze; they involve different variables, like the selection of: stakeholders (their availability, expertise, and importance); prioritization criteria; and prioritization methods. To demonstrate the feasibility of the proposed simulator elements, a well established general purpose simulator, called Arena, was used. The results demonstrate that, it is possible to build the suggested scenarios in order to study and make inferences about the prioritization strategies.
O documento descreve o desenvolvimento de um sistema de gerenciamento de clínicas de psicologia usando o Java EE Web Profile. O sistema permite agendamento de pacientes, registro de consultas e controle financeiro, melhorando sobre o método anterior de uso de papel. O sistema foi construído usando JPA, EJB, JSF e implementou padrões como camada de domínio, negócios e apresentação.
UM ESTUDO SOBRE GERENCIAMENTO DE VARIABLIDADES EM LINHAS DE PROCESSO DE SOFTWAREEdson Oliveira Junior
Este documento apresenta um estudo sobre gerenciamento de variabilidades em linhas de processo de software. Ele propõe uma abordagem chamada SMartySPEM que combina as abordagens SMarty e SPEM 2.0 para representar variabilidades em linhas de processo utilizando a notação UML. O documento também descreve uma revisão sistemática realizada sobre o tema e apresenta um exemplo de aplicação da abordagem proposta.
Apresentação do Artigo de Joyce Mathias no FITEM 2012 - Métodos e Técnicas de Desenvolvimento de Linha de Produto de Software para Sistemas E-Commerce: um Mapeeamento Sistemático
Proposta de uma Abordagem Formal para o Gerenciamento de Variabilidades em Mo...Edson Oliveira Junior
A proposta discute uma abordagem formal para o gerenciamento de variabilidades em modelos UML usando a Object Constraint Language (OCL). O trabalho revisa conceitos como linhas de produto de software, diagrama de interação, package merge e OCL e propõe objetivos como estender a abordagem SMarty e aplicar OCL para validar modelos UML no gerenciamento de variabilidade.
Extensão da Abordagem SMarty de Gerenciamento de Variabilidade para Sistemas ...Edson Oliveira Junior
Este documento discute a extensão da abordagem SMarty de gerenciamento de variabilidade para sistemas embarcados modelados com SysML, adicionando novos estereótipos ao perfil SMartyProfile e diretrizes ao processo SMartyProcess. O trabalho tem como objetivo geral estender a abordagem SMarty para representar e gerenciar variabilidade em linhas de produtos de software para sistemas embarcados.
O documento define revisão sistemática e descreve os principais passos para realizar uma, incluindo: definir uma pergunta norteadora, buscar evidências de forma sistemática, selecionar estudos de acordo com critérios de inclusão e exclusão, avaliar a qualidade metodológica dos estudos selecionados e apresentar os resultados. Além disso, fornece exemplos de revisões sistemáticas realizadas em engenharia de software e ciência da computação.
Um Protótipo Web do Módulo de Planejamento de Avaliações de Linha de Produto ...Edson Oliveira Junior
Este documento descreve um protótipo web para planejamento de avaliações de linha de produto de software usando o método SystEM-PLA. O protótipo permite visualizar modelos de características, variabilidades e relacionamentos entre artefatos usando o parser SMartyParser. Ele é construído com o framework GWT para permitir acesso aos dados extraídos de arquivos XMI sobre a linha de produto AGM.
This document provides an overview of a project report on simulating a single server queuing problem. The report includes an introduction to operations research, simulation, and the queuing problem. It discusses the research methodology, which involves defining the problem, developing a simulation model, validating the model, analyzing the data, and presenting findings and recommendations. The goal is to use simulation to provide optimal solutions to the queuing problem under study.
Understanding the Applicability of Linear & Non-Linear Models Using a Case-Ba...ijaia
This paper uses a case based study – “product sales estimation” on real-time data to help us understand
the applicability of linear and non-linear models in machine learning and data mining. A systematic
approach has been used here to address the given problem statement of sales estimation for a particular set
of products in multiple categories by applying both linear and non-linear machine learning techniques on
a data set of selected features from the original data set. Feature selection is a process that reduces the
dimensionality of the data set by excluding those features which contribute minimal to the prediction of the
dependent variable. The next step in this process is training the model that is done using multiple
techniques from linear & non-linear domains, one of the best ones in their respective areas. Data Remodeling
has then been done to extract new features from the data set by changing the structure of the
dataset & the performance of the models is checked again. Data Remodeling often plays a very crucial and
important role in boosting classifier accuracies by changing the properties of the given dataset. We then try
to explore and analyze the various reasons due to which one model performs better than the other & hence
try and develop an understanding about the applicability of linear & non-linear machine learning models.
The target mentioned above being our primary goal, we also aim to find the classifier with the best possible
accuracy for product sales estimation in the given scenario.
This document provides an overview of modeling and simulation. It defines modeling as representing a system to enable predicting the effects of changes. Simulation involves running experiments on a model. The key steps in modeling and simulation projects are: 1) identifying the problem, 2) formulating and developing the model, 3) validating the model, 4) designing simulation experiments, 5) performing simulations, and 6) analyzing and presenting results. Modeling and simulation can be used for a variety of purposes including education, design evaluation, forecasting, and risk assessment.
LNCS 5050 - Bilevel Optimization and Machine Learningbutest
This document discusses using bilevel optimization and machine learning techniques to improve model selection in machine learning problems. It proposes framing machine learning model selection as a bilevel optimization problem, where the inner level problems involve optimizing models on training data and the outer level problem selects hyperparameters to minimize error on test data. This bilevel framing allows for systematic optimization of hyperparameters and enables novel machine learning approaches. The document illustrates the approach for support vector regression, formulating model selection as a Stackelberg game and solving the resulting mathematical program with equilibrium constraints.
The document provides an introduction to structural equation modeling (SEM). It discusses key concepts such as latent and observed variables, and measurement models. It also presents examples of confirmatory factor analysis output to illustrate model fitting and interpretation. Specifically, it analyzes a four-factor CFA model with academic self-concept variables and reports various goodness-of-fit statistics and parameter estimates to assess how well the hypothesized model fits the sample data.
The document proposes a Response Aware Probabilistic Matrix Factorization (RAPMF) framework to address limitations in existing collaborative filtering recommendation systems. RAPMF incorporates users' response patterns into probabilistic matrix factorization by modeling responses as a Bernoulli distribution for observed ratings and a step function for unobserved ratings. This allows marginalizing missing responses. The authors also develop a mini-batch implementation of RAPMF to reduce computational costs from O(N×M) to O(B2) for mini-batches of B users and items. Experimental evaluation on synthetic and real-world datasets demonstrates the merits of RAPMF, including improved performance and reduced training time compared to other methods.
Operational research (OR) is the scientific approach to problem solving and decision making. It involves modeling complex real-world situations and using analytical methods to evaluate solutions and help decision makers choose optimal alternatives. Some key OR techniques include linear programming, simulation, and data analysis. OR has been successfully applied in many fields like transportation, manufacturing, healthcare, and the airline industry to improve efficiency, maximize profits, and aid strategic planning. The document provides an overview of OR methodology, history, applications, and examples of its use.
EXTRACTING THE MINIMIZED TEST SUITE FOR REVISED SIMULINK/STATEFLOW MODELijaia
Test case generation techniques are successfully employed to generate test cases from a formal model. A problem is that as the model evolves, test suites tend to grow in size, making it too costly to execute entire test suites. This paper aims to propose a practical approach to reduce the size of test suites for modified Simulink/Stateflow (SL/SF) model, which is popularly used for modeling software behavior in many industries like automobile manufacturers. The model for describing a system is frequently modified until it is fixed. The proposed technique is capable of extracting the minimized sized test suite in terms of test coverage, by taking into account both the modified and the affected portion of revised SL/SF model. Two real models for the ECUs deployed in a commercial car are used for an empirical study.
Bio-Inspired Requirements Variability Modeling with use Case ijseajournal
Background.Feature Model (FM) is the most important technique used to manage the variability through products in Software Product Lines (SPLs). Often, the SPLs requirements variability is by using variable use case modelwhich is a real challenge inactual approaches: large gap between their concepts and those of real world leading to bad quality, poor supporting FM, and the variability does not cover all requirements modeling levels. Aims. This paper proposes a bio-inspired use case variability modeling methodology dealing with the above shortages.
Method. The methodology is carried out through variable business domain use case meta modeling,
variable applications family use case meta modeling, and variable specific application use case generating.
Results. This methodology has leaded to integrated solutions to the above challenges: it decreases the gap
between computing concepts and real world ones. It supports use case variability modeling by introducing
versions and revisions features and related relations. The variability is supported at three meta levels
covering business domain, applications family, and specific application requirements.
Conclusion. A comparative evaluation with the closest recent works, upon some meaningful criteria in the
domain, shows the conceptual and practical great value of the proposed methodology and leads to
promising research perspectives
Introduction to modeling_and_simulationAysun Duran
1. This document provides an introduction to modeling and simulation. It discusses what modeling and simulation are, and the types of problems they can address.
2. Simulation involves operating a model of a system to study the system's properties without actually changing the real system. It allows experimenting with different configurations to evaluate and optimize system performance.
3. Developing a simulation model involves identifying the system components and relationships between them. Validating the model and performing simulation experiments then allows making recommendations to improve the real system.
This document provides an overview of modeling and simulation. It discusses what modeling and simulation are, the steps in developing a simulation model, designing a simulation experiment, and analyzing simulation output. It also provides an example of simulating a machine shop with multiple work stations to determine resource utilization under different arrival patterns. The key steps in a simulation study are problem formulation, model development, experiment design, output analysis and recommendations.
Calibration and Validation of Micro-Simulation ModelsWSP
Calibration and Validation of Micro-Simulation Models is a presentation delivered by François Bélisle, Eng., B.Sc., M.Sc., WSP | Parsons Brinckerhoff, Laurent Gauthier, Polytechnique Montréal and Nicolas Saunier, Polytechnique Montréal at the 2015 Transportation Association of Canada (TAC) Conference & Exhibition, from September 27 to 30.
Conceptualization of a Domain Specific Simulator for Requirements Prioritizationresearchinventy
This paper conceptualizes a domain specific simulator for requirements prioritization; its aims at helping to identify appropriate prioritization strategies for a project in hand. The possible existing scenarios are difficult to analyze; they involve different variables, like the selection of: stakeholders (their availability, expertise, and importance); prioritization criteria; and prioritization methods. To demonstrate the feasibility of the proposed simulator elements, a well established general purpose simulator, called Arena, was used. The results demonstrate that, it is possible to build the suggested scenarios in order to study and make inferences about the prioritization strategies.
BIO-INSPIRED REQUIREMENTS VARIABILITY MODELING WITH USE CASE mathsjournal
Background.Feature Model (FM) is the most important technique used to manage the variability through
products in Software Product Lines (SPLs). Often, the SPLs requirements variability is by using variable
use case modelwhich is a real challenge inactual approaches: large gap between their concepts and those of
real world leading to bad quality, poor supporting FM, and the variability does not cover all requirements
modeling levels.
1) The document introduces modeling and simulation through an introductory lecture. It discusses the goals of modeling, simulation, and the course.
2) It describes what models and simulations are, and provides examples of different types of models and applications of simulation.
3) The key steps in building a simulation model are outlined, including defining the goal, collecting input data, verifying and validating the model, and analyzing output.
The information in this slide is very useful for me to do the assignment regarding the simulation in which we have to report together with the presentation...
This document provides an overview of a machine learning course. It outlines the course prerequisites, description, learning outcomes, structure, grading breakdown, and topics to be covered. The course aims to develop practical machine learning and data science skills by covering theoretical concepts and applying them to programming assignments. It will be conducted online and involve lectures, assessments, a group project, and final exam. Key machine learning topics to be covered include supervised learning, unsupervised learning, and applications.
This document summarizes a presentation about machine learning and predictive analytics. It discusses formal definitions of machine learning, the differences between supervised and unsupervised learning, examples of machine learning applications, and evaluation metrics for predictive models like lift, sensitivity, and accuracy. Key machine learning algorithms mentioned include logistic regression and different types of modeling. The presentation provides an overview of concepts in machine learning and predictive analytics.
Operational research is the scientific approach to problem solving and decision making. It involves formulating problems mathematically and using scientific techniques like simulation, optimization, and data analysis to solve complex real-world problems. Some key applications of operational research include supply chain management, transportation and logistics, production scheduling, and resource allocation in industries like airlines, manufacturing, and healthcare. The goal is to help decision makers identify optimal solutions and improve performance.
The operation research book that involves all units including the lpp problems, integer programming problem, queuing theory, simulation Monte Carlo and more is covered in this digital material.
How to Fix the Import Error in the Odoo 17Celine George
An import error occurs when a program fails to import a module or library, disrupting its execution. In languages like Python, this issue arises when the specified module cannot be found or accessed, hindering the program's functionality. Resolving import errors is crucial for maintaining smooth software operation and uninterrupted development processes.
Walmart Business+ and Spark Good for Nonprofits.pdfTechSoup
"Learn about all the ways Walmart supports nonprofit organizations.
You will hear from Liz Willett, the Head of Nonprofits, and hear about what Walmart is doing to help nonprofits, including Walmart Business and Spark Good. Walmart Business+ is a new offer for nonprofits that offers discounts and also streamlines nonprofits order and expense tracking, saving time and money.
The webinar may also give some examples on how nonprofits can best leverage Walmart Business+.
The event will cover the following::
Walmart Business + (https://business.walmart.com/plus) is a new shopping experience for nonprofits, schools, and local business customers that connects an exclusive online shopping experience to stores. Benefits include free delivery and shipping, a 'Spend Analytics” feature, special discounts, deals and tax-exempt shopping.
Special TechSoup offer for a free 180 days membership, and up to $150 in discounts on eligible orders.
Spark Good (walmart.com/sparkgood) is a charitable platform that enables nonprofits to receive donations directly from customers and associates.
Answers about how you can do more with Walmart!"
This presentation was provided by Steph Pollock of The American Psychological Association’s Journals Program, and Damita Snow, of The American Society of Civil Engineers (ASCE), for the initial session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session One: 'Setting Expectations: a DEIA Primer,' was held June 6, 2024.
A review of the growth of the Israel Genealogy Research Association Database Collection for the last 12 months. Our collection is now passed the 3 million mark and still growing. See which archives have contributed the most. See the different types of records we have, and which years have had records added. You can also see what we have for the future.
This slide is special for master students (MIBS & MIFB) in UUM. Also useful for readers who are interested in the topic of contemporary Islamic banking.
How to Setup Warehouse & Location in Odoo 17 InventoryCeline George
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Chapter wise All Notes of First year Basic Civil Engineering.pptxDenish Jangid
Chapter wise All Notes of First year Basic Civil Engineering
Syllabus
Chapter-1
Introduction to objective, scope and outcome the subject
Chapter 2
Introduction: Scope and Specialization of Civil Engineering, Role of civil Engineer in Society, Impact of infrastructural development on economy of country.
Chapter 3
Surveying: Object Principles & Types of Surveying; Site Plans, Plans & Maps; Scales & Unit of different Measurements.
Linear Measurements: Instruments used. Linear Measurement by Tape, Ranging out Survey Lines and overcoming Obstructions; Measurements on sloping ground; Tape corrections, conventional symbols. Angular Measurements: Instruments used; Introduction to Compass Surveying, Bearings and Longitude & Latitude of a Line, Introduction to total station.
Levelling: Instrument used Object of levelling, Methods of levelling in brief, and Contour maps.
Chapter 4
Buildings: Selection of site for Buildings, Layout of Building Plan, Types of buildings, Plinth area, carpet area, floor space index, Introduction to building byelaws, concept of sun light & ventilation. Components of Buildings & their functions, Basic concept of R.C.C., Introduction to types of foundation
Chapter 5
Transportation: Introduction to Transportation Engineering; Traffic and Road Safety: Types and Characteristics of Various Modes of Transportation; Various Road Traffic Signs, Causes of Accidents and Road Safety Measures.
Chapter 6
Environmental Engineering: Environmental Pollution, Environmental Acts and Regulations, Functional Concepts of Ecology, Basics of Species, Biodiversity, Ecosystem, Hydrological Cycle; Chemical Cycles: Carbon, Nitrogen & Phosphorus; Energy Flow in Ecosystems.
Water Pollution: Water Quality standards, Introduction to Treatment & Disposal of Waste Water. Reuse and Saving of Water, Rain Water Harvesting. Solid Waste Management: Classification of Solid Waste, Collection, Transportation and Disposal of Solid. Recycling of Solid Waste: Energy Recovery, Sanitary Landfill, On-Site Sanitation. Air & Noise Pollution: Primary and Secondary air pollutants, Harmful effects of Air Pollution, Control of Air Pollution. . Noise Pollution Harmful Effects of noise pollution, control of noise pollution, Global warming & Climate Change, Ozone depletion, Greenhouse effect
Text Books:
1. Palancharmy, Basic Civil Engineering, McGraw Hill publishers.
2. Satheesh Gopi, Basic Civil Engineering, Pearson Publishers.
3. Ketki Rangwala Dalal, Essentials of Civil Engineering, Charotar Publishing House.
4. BCP, Surveying volume 1
Strategies for Effective Upskilling is a presentation by Chinwendu Peace in a Your Skill Boost Masterclass organisation by the Excellence Foundation for South Sudan on 08th and 09th June 2024 from 1 PM to 3 PM on each day.
Beyond Degrees - Empowering the Workforce in the Context of Skills-First.pptxEduSkills OECD
Iván Bornacelly, Policy Analyst at the OECD Centre for Skills, OECD, presents at the webinar 'Tackling job market gaps with a skills-first approach' on 12 June 2024
1. Empirically Based Evolution of a
Variability Management Approach
at UML Class Level
Anderson Marcolino, M.Sc.
State University of Maringá (DIN-UEM) – Brazil
Edson OliveiraJr, Ph.D.
edson@din.uem.br
State University of Maringá (DIN-UEM) - Brazil
Itana Gimenes, Ph.D.
State University of Maringá (DIN-UEM) – Brazil
Ellen Barbosa, Ph.D.
University of São Paulo (ICMC-USP) – Brazil
2. 2
Agenda
Introduction
Background
SPL and Variability Management
The Gomaa Method
The SMarty Approach
SMarty Evolution based on Empirical Study
Conclusion and Future Works
3. 3
Introduction
Software Product Line (SPL) is a consolidated reuse technique,
in which several products share similar features and
variabilities
allowing mass customization for specific market needs
Variability represents how such products differ one another,
thus
Variability Management (VM) is a key issue for the success of SPLs
Literature presents several well-known approaches for VM,
especially object-oriented and UML-based.
However, their effectiveness was not experimentally analyzed, which can
make technology transfer feasible
4. 4
Introduction
The SPL approach encompasses three main
activities:
Domain Engineering
Application Engineering
Management (Organizational and Technical)
Variability management is one of the most important
SPL management activities
5. 5
Introduction
Four main concepts are taken into consideration
for VM:
Variability, Variation Point and Variants
PLUS and SMarty are two well-known
approaches for VM in UML-based SPLs
However, PLUS has been taken as a basis for several UML-
based SPL projects
6. 6
Objectives of this work
1. Comparing the effectiveness of SMarty and
PLUS with regard to the identification and
representation of variability in UML classes
2. Allowing the SMarty evolution in order to
increase its effectiveness against PLUS
7. 7
Background - PLUS
The PLUS method
It encompasses a variability management activity with regard to
use cases and classes, using stereotypes with no guidelines to
apply them / Domain experts must use their knowledge for VM /
Variant constraints are not available.
Use Case?
<<kernel>> Used to represent mandatory elements. Yes.
<<optional>>
Used to represent optional elements. It represents an
element that can be selected or not in a specific product. Yes.
<<alternative>>
Used to represent alternative elements, mutually exclusive
elements. Yes.
Stereotype Description
Does
Use
cases
only!!!
Use
cases
only!!!
8. 8
Background - PLUS
An example of the PLUS variability representation for
classes:
types
<<kernel>>
SortingElement
<<optional>>
NumericElement
<<optional>>
StringElement
-sortingElements
1..*
9. 9
Background - SMarty
Stereotype-based Management of Variability
(SMarty) approach
It is composed of:
an UML 2 profile, the SMartyProfile with stereotypes
for VM in use case, class, component, sequence, and
activity diagrams; and
a process, the SMartyProcess, with guidelines to
support variability identification and representation
10. 10
Background - SMarty
SMartyProfile comprises the following stereotypes, which
can be applied to UML models:
<<variability>> represents the concept of PL variability;
<<variant>> this abstract stereotype is specialized in four other non-
abstract stereotypes which are: <<mandatory>>, <<optional>>,
<<alternative_OR>>, and <<alternative_XOR>>;
<<mutex>> is a mutually exclusive relationship between two variants;
and
<<requires>> is a relationship between two variants in which the
selected variant requires the presence of another specific variant.
11. 11
Background - SMarty
An example of the
SMarty variability
representation for
classes:
types
<<madatory>>
<<variationpoint>>
SortingElement
<<alternative_OR>>
NumericElement
<<alternative_OR>>
StringElement
-sortingElements
<<comment>>
{allow sAddingVar = false,
bindingTime = DESIGN_TIME,
maxSelection = 2,
minSelection = 1,
name = "sorting element",
variants = "NumericElement, StringElement"}
1..*
12. 12
The Experimental Study
Aim (Basili’s GQM template)
Compare PLUS and SMarty, for the purpose of characterize
the most effectiveness, with respect to the capability of
identification and representation of variabilities in Software
Product Line class models, from the point of view of software
product line architects, in the context of master and Ph.D.
students of the Software Engineering area from the University of
São Paulo (ICMC/USP) and Federal University of São Carlos
(UFSCar).
14. 14
The Experimental Study
Planning
Hypotheses Formulation
H0 : µ (effectiveness(X)) = µ (effectiveness(Y));
H1 : µ (effectiveness(X)) < µ (effectiveness(Y)); and
H2 : µ (effectiveness(X)) > µ (effectiveness(Y)).
Where X = PLUS and Y = SMarty.
15. 15
The Experimental Study
Planning
Dependent Variables: the effectiveness calculated for each
variability management approach (X and Y) as follows:
16. 16
The Experimental Study
Planning
Independent Variables:
the variability management approach, a factor with
two treatments (X and Y); and
the SPL, a factor with two treatments (E-commerce
and AGM SPLs).
18. 18
The Experimental Study
Effectiveness of the Approaches
Collected Data Normality Test: the Shapiro-Wilk normality test
was applied to the E-commerce and AGM samples providing the
following results:
sample X was considered non-normal and
sample Y was considered normal.
19. 19
The Experimental Study
Box Plot of Effectiveness (Class)
Spreadsheet1 10v*12c
Median; Box: 25%-75%; Whisker: Non-Outlier Range
Median
25%-75%
Non-Outlier Range
Outliers
Extremes
Effectiveness Y Approach.
Effectiveness X Approach.
-5
0
5
10
15
20
25
30
20. 20
The Experimental Study
Effectiveness of the Approaches
Mann-Whitney-Wilcoxon for SampleX and SampleY:
Detailed explanation on samples is in the paper
There is evidence that the X approach (PLUS) is more effective in
identifying and representing variability in class models than the Y
approach (SMarty)
This result corroborates to reject the null hypothesis (H0)
of this study: PLUS is more effective than SMarty for
SPLs class models and subjects taken into account.
21. 21
The Experimental Study
Threats to Validity
Conclusion Validity
Sample size is a major concern, which must be increased in
prospective studies
Random capacity was not applied to subjects selection, thus
generalization of results could not be inferred
Construct Validity
Independent variable variability modeling approach was
guaranteed by the pilot project undertaken
22. 22
The Experimental Study
Threats to Validity
Internal Validity
Differences among subjects – variations on skills were reduced by
performing training session and tasks in the same order
Fatigue effects – on average the experiment took 80 minutes, thus
fatigue was not considered a problem for the study
Influence among subjects – could not be really controlled. They took
the experiment under the supervision of a human observer.
23. 23
The Experimental Study
Threats to Validity
External Validity
Instrumentation – failing to use real class models, as the e-commerce
and AGM are not commercial SPLs. More experiments must be
carried out with real SPLs.
Subjects – we could take advantages of the benefits of taking into
account students for performing experiments as pointed out by
Carver et al. (2003)
24. 24
Empirical Evolution of SMarty
Improvements for the SMarty approach based on the
feedback of the subjects:
E-commerce SPL
Subjects reported difficulties on the application of SMarty to the e-
commerce SPL class model as it has no elements of class modeling,
such as, inheritance, aggregation, and generalization E-
commerce was analyzed to improve SMarty
Training session
Subjects indicated that they need more time for training and
application of SMarty
25. 25
Empirical Evolution of SMarty
Improvements for the SMarty approach based on the
feedback of the subjects:
Amount of stereotypes
Subjects questioned the difference between PLUS and SMarty as
the later has several more stereotypes
Arrangement of the SPL models:
E-commerce class models are mode complex than AGM
E-commerce models were the first models given to the subjects
26. 26
Empirical Evolution of SMarty
Based on such subjects feedback, the following
improvements were made:
New guidelines were added to SMarty, encompassing the level of
abstraction and elements of the e-commerce class models
CL2. Class models elements, related with associations in which their
attributes have aggregationKind as none, or, do not represent
aggregation or composition, suggest mandatory or optional variants.
CL2.1. For the identification of possible optional variants classes
related through associations where the multiplicity in one of the ends
of an association, to each object class found at the opposite end
match * (zero or more) or 0..n where n is any integer different from
zero; suggest an optional class
27. 27
Empirical Evolution of SMarty
Based on such subjects feedback, the following
improvements were made:
We realized that with a few less stereotypes, PLUS is easier to
apply. However, such an “easibility” might jeopardize the
generation of specific products as we can observe ambiguity in
PLUS class models
28. 28
Revisiting objectives of this work…
1. Comparing the effectiveness of SMarty and PLUS with
regard to the identification and representation of
variability in UML classes
PLUS is more effective
1. Allowing the SMarty evolution in order to increase its
effectiveness against PLUS
New guidelines for class models need a new experiment
Reduce stereotypes or avoid ambiguity during specific products
generation? New experiment is being carried out
29. 29
Conclusion
Industry needs that the scientific community tests existing and new
technologies, such as SMarty, identifying their effectiveness to make
technology transfer easier and reliable.
The experimental study presented in this paper demonstrates the
ability to use variability management approaches. Their
effectiveness were analyzed by modeling variability in class models
of two SPLs.
Empirical based improvements could be made to SMarty in order to
incorporate support for different class model elements by means of
new guidelines
30. 30
Conclusion
Shapiro-Wilk normality test was applied to both samples
Mann-Whitney-Wilcoxon test analyzed the effectiveness of PLUS
and SMarty
Obtained results provide evidence that the PLUS is more effective
than SMarty for modeling variability in UML class models, taking into
account the E-commerce and the AGM SPLs for this study.
31. 31
Future Works
New experimental studies and replications must be planned and
conducted to make it possible to reduce the threats, increasing the
effectiveness of SMarty towards generalizing the results.
As new experiments, we are:
planning a new experiment for characterize the improved version of
SMarty effective against PLUS; and
planning an experiment to analyze the effectiveness of PLUS and
SMarty and their impact on the generation of specific products.