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Flow The Psychology Of Optimal Experience

斜向妥当化(Cross-Validation)の必要と方法

Context Software effort estimation (SEE) is most crucial activity in the field of software engineering. Vast research has been conducted in SEE resulting into a tremendous increase in literature. Thus it is of utmost importance to identify the core research areas and trends in SEE which may lead the researchers to understand and discern the research patterns in large literature dataset. Objective To identify unobserved research patterns through natural language processing from a large set of research articles on SEE published during the period 1996 to 2016. Method A generative statistical method, called Latent Dirichlet Allocation (LDA), applied on a literature dataset of 1178 articles published on SEE. Results As many as twelve core research areas and sixty research trends have been revealed; and the identified research trends have been semantically mapped to associate core research areas. Conclusions This study summarises the research trends in SEE based upon a corpus of 1178 articles. The patterns and trends identified through this research can help in finding the potential research areas.

Research patterns and trends in software effort estimation

From the Publisher: 
This new work from Watts Humphrey, author of the influential book, Managing the Software Process, broadens his orderly view of software process management, and lays the foundation for a disciplined approach to software engineering. In his earlier book, the author developed concrete methods for managing software development and maintenance. These methods, now commonly practiced in industry, provide programmers and managers with specific steps they can take to evaluate and improve their software capabilities. In this new book, Humphrey scales those methods down to a personal level, helping software engineers develop the skills and habits needed to plan, track, and analyze large, complex projects. Humphrey and others have used material from this book to train professionals and students around the world in a projects-oriented software engineering course. First establishing the need for discipline in software engineering, and the benefits to practitioners of learning how to manage their personal software process, Humphrey then develops a model that they can use to monitor, test, and improve their work. Examples drawn from industry enhance the practical focus of the book, while project exercises give readers the opportunity to practice software process management as they learn it. Features: presents concepts and methods for a disciplined software engineering process; scales down industrial practices for planning, tracking, analysis, and defect management to fit the needs of small-scale program development; and shows how small project disciplines provide a solid base for larger projects.

Discipline for Software Engineering a

Thank you for reading practical software measurement objective information for decision makers. As you may know, people have look hundreds times for their favorite readings like this practical software measurement objective information for decision makers, but end up in malicious downloads. Rather than enjoying a good book with a cup of tea in the afternoon, instead they are facing with some harmful virus inside their computer.

Practical Software Measurement Objective Information For Decision Makers

Requirements validation is a critical task in any engineering project. The confrontation of stakeholders with static requirements models is not enough, since stakeholders with non-computer science education are not able to discover all the inter-dependencies between the elicited requirements. Even with simple unified modelling language (UML) requirements models, it is not easy for the development team to get confidence on the stakeholders’ requirements validation. This paper describes an approach, based on the construction of executable interactive prototypes, to support the validation of workflow requirements, where the system to be built must explicitly support the interaction between people within a pervasive cooperative workflow execution. A case study from a real project is used to illustrate the proposed approach.

/pdf/requirements-validation-execution-of-uml-models-with-cpn-30o4tkyjtu.pdf

Requirements Validation: Execution of UML Models with CPN Tools

Paper aims This paper presents teachers and students perspectives of integration Science, Technology, Engineering and Mathematics (STEM) courses contents into the first year of Industrial Engineering and Management (IEM) program through Project-Based Learning (PBL). Originality These perspectives provide strategies that IEM teachers could use to challenge students to apply STEM contents to design a product and its production system by developing a semester interdisciplinary project. Research method Grounded on project deliveries and an on-line questionnaire, the teachers and students’ work and perceptions were analysed in order to understand and to evaluate the effectiveness of such strategies. Main findings Results pointed out a general satisfaction of the teachers with the way teams integrate the STEM contents to design the production system and the product and students proud of their projects. Implications for theory and practice PBL is recognized as an active learning methodology that engages students in their own learning process and allows them to realize the relevance of all courses and the role of each to design a product/production system. Nevertheless, is fundamental a good knowledge of the PBL process to achieve this.

/pdf/integrating-science-technology-engineering-and-mathematics-4acp8aoymv.pdf

Integrating Science, Technology, Engineering and Mathematics contents through PBL in an Industrial Engineering and Management first year program

Empirical studies are important in software engineering to evaluate new tools, techniques, methods and technologies in a structured way before they are introduced in the industrial (real) software process. Perform empirical studies in a real context is very difficult due to various obstacles. An interesting alternative is perform empirical studies in an educational context using students as subjects and share the results with the academia and the industry. This paper describes a case study with two teams that developed a software system (Web application) for a real customer. In this study we used a model based on Function Points Analysis (FPA) to estimate the size and complexity of software system.

/pdf/an-empirical-study-on-the-estimation-of-size-and-complexity-124vgpi3dr.pdf

An Empirical Study on the Estimation of Size and Complexity of Software Applications with Function Points Analysis

The occurrence of Barotrauma is identified as a major concern for health professionals, since it can be fatal for patients. In order to support the decision process and to predict the risk of occurring barotrauma Data Mining models were induced. Based on this p rinciple, the present study addresses the Data Mining process aiming to provide hourly probability of a patient has Barotrauma. T he process of discovering implicit knowledge in data collected from Intensive Car e Units patients was achieved through the standard process Cross Industry Standard Process for Data Mining. With the goal of making predictions according to the classi fication approach they several DM techniques were selected: Decision Trees, Naive B ayes and Support Vector Machine. The study was focused on identifying the validity and viability to predict a composite variable. To predict the Barotrauma two classe s were created: "ri sk" and “no risk". Such target come from combining two variables: Plateau Pressure and��2�%�1 �6 . The best models presented a sensitivity between 96.19% and 100%. In terms of accuracy the values varied between 87.5% and 100%. This study and the achieved results demonstrated the feasibility of predicting th e risk of a patient having Barotrauma by presenting the p robability associated.

https://repositorium.sdum.uminho.pt/bitstream/1822/39280/1/2015%20-%20Elsevier%20-%20Intelligent%20Decision%20Support%20to%20Predict%20Patient%20Barotrauma%20Risk%20in%20Intensive%20Care%20Units.pdf

Intelligent decision support to predict patient barotrauma risk in intensive care units

It is clear that the failures found in hospital management are usually related to the lack of information and insufficient resources management. The use of Data Mining (DM) can contribute to overcome these limitations in order to identify relevant data on patient’s management and providing important information for managers to support their decisions.

/pdf/predictive-models-for-hospital-bed-management-using-data-31gvnyjwtj.pdf

Sérgio Manuel Costa Oliveira

Papers

Requirements Validation: Execution of UML Models with CPN Tools

Integrating Science, Technology, Engineering and Mathematics contents through PBL in an Industrial Engineering and Management first year program

An Empirical Study on the Estimation of Size and Complexity of Software Applications with Function Points Analysis

Intelligent decision support to predict patient barotrauma risk in intensive care units

Predictive models for hospital bed management using data mining techniques