We Know Less Than You Think (But We Do Know Something)
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A quick summary of empirical software engineering, followed by early results from a survey of how over 1900 scientists use computers in their research.
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Contents lists available at ScienceDirect
Simulation Modelling Practice and Theory
j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / s i m p a t
Insight Maker: A general-purpose tool for web-based modeling
& simulation
http://dx.doi.org/10.1016/j.simpat.2014.03.013
1569-190X/� 2014 The Author. Published by Elsevier B.V.
This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/3.0/).
E-mail address: [email protected]
1 The exact search query used was ‘’’modeling tool’’ OR ‘‘simulation tool’’’ in the Topic field.
Scott Fortmann-Roe
University of California, Berkeley, Department of Environmental Science, Policy, and Management, 130 Mulford Hall, Berkeley, CA 94720-3114, United States
a r t i c l e i n f o a b s t r a c t
Article history:
Received 29 April 2013
Received in revised form 23 March 2014
Accepted 26 March 2014
Available online 14 June 2014
Keywords:
Modeling
Simulation
Web-based technologies
System Dynamics
Agent-Based Modeling
A web-based, general-purpose simulation and modeling tool is presented in this paper. The
tool, Insight Maker, has been designed to make modeling and simulation accessible to a
wider audience of users. Insight Maker integrates three general modeling approaches –
System Dynamics, Agent-Based Modeling, and imperative programming – in a unified
modeling framework. The environment provides a graphical model construction interface
that is implemented purely in client-side code that runs on users’ machines. Advanced fea-
tures, such as model scripting and an optimization tool, are also described. Insight Maker,
under development for several years, has gained significant adoption with currently more
than 20,000 registered users. In addition to detailing the tool and its guiding philosophy,
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plex web-based simulation and modeling tool.
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license (http://creativecommons.org/licenses/by/3.0/).
1. Introduction
The field of modeling and simulation tools is diverse and emergent. General-purpose modeling tools (e.g. MATLAB’s
Simulink or the Modelica language [1]) sit beside highly focused and domain-specific applications (e.g. [2] for modeling
network control systems, [3] for simulating the behavior of wireless network routing protocols, or [4] for the simulation
and control of turbines). Interest in and published works on such tools has grown over time. The ISI Web of Knowledge
reports a substantial growth in papers published on modeling or simulation tools with 299 such papers published in the span
of 1985–1989, 1482 published from 1995 to 1999, and 3727 published from 2005 to 2009.1
For end-users, simulation and modeling tools are generally designed as executables to be run on a consumer operating
system such as W.
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Project Management (October - 2014) [CBSGS - Paper Solution] {Mumbai University}
.
,project management ,cbsgs - paper solution ,project management paper solution ,bscit ,bscit question paper ,bscit question papers 2018 ,bscit semester vi ,bscit smester 6 2018 ,cbsgs ,cbsgs 75-25 pattern ,may – 2018 ,mumbai university bscit ,mumbai university question papers ,project management cbsgs 2018 ,project management may - 2018 ,project management question paper ,question paper ,tybscitSoftware engineering the genesis
The document discusses the origins of software engineering as a discipline. It summarizes discussions from a conference in 1968 where the term "software engineering" was first used. Key points discussed included that testing is best done iteratively during design rather than after, that small groups tend to be more successful than large groups on software projects, and that an organizational structure is needed for communication and decision making in large groups. The document also discusses criticisms of the "waterfall" development model and advocates for an iterative approach.
Introduction to Systems Analysis and Design
The document is a PowerPoint presentation on systems analysis and design. It covers several topics:
- The definition and goals of systems analysis and design. Common reasons for project failures.
- An overview of the systems development life cycle (SDLC) process, including the planning, analysis, design, and implementation phases. Traditional and agile methodologies are discussed.
- Specific methodologies like waterfall, prototyping, scrum, and extreme programming (XP) are explained. Factors to consider when choosing a methodology are presented.
- Key principles of agile development are outlined, including an emphasis on valuing individuals, collaboration, and customer satisfaction over rigid adherence to plans.
Software Engineering: What is That?
Software Engineering: introductory lecture.
Relation between SE and Computer Science
Working Definition
Good and bad Design
ISPC03_Graphical_modeling
This document discusses the benefits of using graphical modeling over traditional software development techniques for developing wireless system simulation models. Graphical modeling involves representing models visually using blocks that can be connected to represent data and control flow. This approach clearly separates the simulation infrastructure from the system being modeled, allowing developers to focus on modeling algorithms rather than infrastructure. It also enables flexible design changes and reuse of third-party block libraries, reducing development time and costs compared to traditional techniques. While graphical modeling requires new skills and concepts, its benefits are argued to outweigh any costs or performance limitations for wireless system modeling.
Business Visit Report
The document summarizes a business visit report from Honeywell, a global aerospace company. It describes the different types of engineers used at Honeywell, including systems, software, and hardware engineers. It also discusses the skills and tools required for each engineering role. The document compares the skills taught in school to those needed for engineering careers and identifies gaps between the curriculum and industry needs.
Software engineering
The document provides an overview of a course on software engineering. It discusses key concepts like structured programming, object-oriented programming, design principles of abstraction and modularity. It also covers programming in languages like C and Matlab. The goals of the course are to understand basic program design techniques, produce well-structured programs, and have a basic understanding of object-oriented design.
Software engineering
The document describes a course on software engineering that covers basic design principles and techniques like structured programming, object-oriented programming, and data structures. It aims to give students an understanding of how to produce well-structured, maintainable code. Examples will be shown in MATLAB and C/C++. Key concepts covered include abstraction, modularity, procedural and object-oriented programming, functions, classes, and arrays.
ch01lect1.ppt
The document discusses object-oriented software engineering and modeling complex systems. It introduces three techniques for dealing with complexity: abstraction, decomposition, and hierarchy. It argues that object-oriented decomposition is generally a good methodology for breaking a system into classes, but notes there may be other valid decompositions depending on the system's purpose. The document reflects on the current state of modeling and suggests starting with use case functionality before proceeding to the object model, in line with the software development lifecycle.
Impact-Driven Research on Software Engineering Tooling
This document discusses impact-driven research on software engineering tooling. It provides examples of research that had impact on practice through commercial tools adopting the research results or startups being formed. It also discusses releasing open source tools and data to engage communities. The document advocates for access to real-world data and cooperation with industry to achieve research impact and leadership. It provides examples of the author's impactful research publications and outlines future directions like starting a startup or collaborating more closely with industry.
8Calling the Shot8Calling the ShotPractice.docx
8
Calling the Shot
8
Calling the Shot
Practice is the best of all instructors.
PUBUUUS
Experience is a dear teacher, but fools will learn at no
other.
POOR RICHARD'S ALMANAC
Douglass Crockwell, "Ruth calls his shot," World Series, 1932
Reproduced by permission of Esquire Magazine and Douglass Crockwell, © 1945
(renewed 1973) by Esquire, Inc., and courtesy of the National Baseball Museum.
87
88 Calling the Shot
How long will a system programming job take? How much effort
will be required? How does one estimate?
I have earlier suggested ratios that seem to apply to planning
time, coding, component test, and system test. First, one must say
that one does not estimate the entire task by estimating the coding
portion only and then applying the ratios. The coding is only
one-sixth or so of the problem, and errors in its estimate or in the
ratios could lead to ridiculous'results.
Second, one must say that data for building isolated small
programs are not applicable to programming systems products. For
a program averaging about 3200 words, for example, Sackman,
Erikson, and Grant report an average code-plus-debug time of
about 178 hours for a single programmer, a figure which would
extrapolate to give an annual productivity of 35,800 statements
per year. A program half that size took less than one-fourth as
long, and extrapolated productivity is almost 80,000 statements
per year.1 Planning, documentation, testing, system integration,
and training times must be added. The linear extrapolation of such
sprint figures is meaningless. Extrapolation of times for the hun-
dred-yard dash shows that a man can run a mile in under three
minutes.
Before dismissing them, however, let us note that these num-
bers, although not for strictly comparable problems, suggest that
effort goes as a power of size even when no communication is
involved except that of a man with his memories.
Figure 8.1 tells the sad story. It illustrates results reported from
a study done by Nanus and Farr2 at System Development Corpo-
ration. This shows an exponent of 1.5; that is,
effort = (constant) X (number of instructions)15.
Another SDC study reported by Weinwurm3 also shows an expo-
nent near 1.5.
A few studies on programmer productivity have been made,
and several estimating techniques have been proposed. Morin has
prepared a survey of the published data.4 Here I shall give only a
few items that seem especially illuminating.
Portman's Data 89
Fig. 8.1 Programming effort as a function of program size
Portman's Data
Charles Portman, manager of ICL's Software Division, Computer
Equipment Organization (Northwest) at Manchester, offers an-
other useful personal insight.5
He found his programming teams missing schedules by about
one-half—each job was taking approximately twice as long as
estimated. The estimates were very careful, done by experienced
teams estimating man-hours for several hundred subtasks on a
PERT chart. When the slippage pattern appeared, he ...
Accelerating the Pace of Engineering Education with Simulation, Hardware and ...
Presentation for MathWorks (www.mathworks.com) at World Engineering Education Forum 2014, Dubai.
Education is challenging. It always was challenging, and it always will be challenging, but every generation of educators and society has to find answers specific to their era. This talk addresses some of the challenges in engineering education in the 21st century.
Industry complains about the skills gap they face with graduates in engineering, for lack of project awareness, problem solving skills, applicable tool skills or applied science skills. Academia complains about students not bringing the necessary basic skills as engineering freshmen. Teachers complain about a lack of student engagement. Students complain about classes not engaging them and seeming irrelevant.
When putting this chain of challenges – industry, academia, school, students – on its head and starting with the student engagement, one method getting attention is Project-Based Learning. Students educate themselves on concepts they need, with the teacher facilitating the learning experience. Applying theory in practical ways with tools that are used in industry gives students first-hand experience on industry relevant methods as well as the why behind theory. The talk shows examples of programming, modeling and simulation to gain insight into theory and application.
Too often students and educators feel that topics throughout their education are not connected. Early on they lack understanding of the why they are learning something. Later they no longer see the connection of advanced theory to fundamental concepts. Reusing learning artifacts, skills and methods helps mapping out the story. Demonstrations illustrate how educators implement this re-use throughout teaching.
Consequent reuse leads to Integrated Curriculum, where methods and skills in each year build on previous ones. Evaluations in integrated curriculum enabled programs show a higher retention of know-how.
We all can make math, physics and engineering able to experience using simulation and hardware experiments. The tools and resources are there. Let's address our generation's engineering education challenges.
Ch01lect1 et
This document discusses an introduction to software engineering. It begins by defining what a computer program and software are, noting that software includes computer programs, configuration files, user documentation, support services, and system documentation. The document then discusses some key attributes of software like maintainability, dependability, efficiency, and usability. It notes that software engineering is needed for medium to large projects involving teams to deal with complexity and change. The document discusses why software development is difficult due to changes in requirements and technology, complex problem and solution domains, and difficult management of development processes. It describes how abstraction, decomposition, and hierarchy can be used to deal with complexity. Finally, it discusses different models used in software engineering like object, functional, dynamic
COCOMO Model For Effort Estimation
Using COCOMO Model for effort estimate, Example using Basic COCOMO, Example using Intermediate COCOMO, Software Engineering Lab, Exercise 5, JNTUK R20
Similar to We Know Less Than You Think (But We Do Know Something) (20)
Research-Based Innovation with Industry: Project Experience and Lessons Learned
Research-Based Innovation with Industry: Project Experience and Lessons Learned
Simulation Modelling Practice and Theory 47 (2014) 28–45Cont.docx
Simulation Modelling Practice and Theory 47 (2014) 28–45Cont.docx
Project Management (October - 2014) [CBSGS - Paper Solution] {Mumbai University}
Project Management (October - 2014) [CBSGS - Paper Solution] {Mumbai University}
Impact-Driven Research on Software Engineering Tooling
Impact-Driven Research on Software Engineering Tooling
Accelerating the Pace of Engineering Education with Simulation, Hardware and ...
Accelerating the Pace of Engineering Education with Simulation, Hardware and ...
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- 1. We Know Less Than You Think (But We Do Know Something) Copyright © 2009 Gregory V. Wilson. This presentation may be freely used and distributed with attribution. Prof. Greg Wilson Dept. of Computer Science University of Toronto [email_address]