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of Foundations Software Design Fall 2002 Marti Hearst Lecture 7: Object-Oriented Design 1 Design Processes What is design? A creative problem solving process Key aspects of design Create use scenarios Consider several options Iterate, iterate, iterate (rarely right the first time) How to learn design? By seeing (lots of) examples By doing (lots of) examples 2 Key Software Design Goals Simplicity...
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of Foundations Software Design
Fall 2002 Marti Hearst
Lecture 7: Object-Oriented Design
1
Design Processes
What is design?
A creative problem solving process
Key aspects of design
Create use scenarios Consider several options Iterate, iterate, iterate (rarely right the first time)
How to learn design?
By seeing (lots of) examples By doing (lots of) examples
2
Key Software Design Goals
Simplicity
Often leads to elegance Easier to understand and debug the code Reduces errors
Reliability / Robustness
Can handle unexpected input Recovers from errors gracefully Doesnt damage anything
Effectiveness
Solves the right problems (intuitive for users) Efficient Compatible with other software
3
Complex vs. Simple Design
i) CPU using 3 chips (classes)
Chip1 Chip2
ii) CPU using 3 chips (classes)
Chip1 Chip2
ANDgates
ORgates
Registers
ALU ALU
Chip3
Shifter SHIFTER
Chip3
NOTga...
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Berkeley - IS - 255
Foundations of Software DesignFall 2002 Marti HearstLecture 9: Algorithms and Pseudo-code1The central role of algorithms in computer scienceFrom Brookshear; Copyright 2003 Pearson Education2What is an algorithm? The idea behind the comp
Berkeley - IS - 255
Foundations of Software DesignFall 2002 Marti HearstLecture 11: Analysis of Algorithms, cont.1Function Pecking Order In increasing orderlog(n) 1 2 3 4 5 6 7 8 9 10 n 2 4 8 16 32 64 128 256 512 1024 n^2 4 16 64 256 1024 4096 16384 65536 26214
Berkeley - IS - 255
Foundations of Software DesignFall 2002 Marti HearstLecture 12: Stacks and Queues1Lingering Question Why is the expected value of accessing an array of length n going to take time n/2? The expected value is the number of steps youd expect to
Berkeley - IS - 255
Foundations of Software DesignFall 2002 Marti HearstLecture 13: Queues and Vectors1Queueshttp:/www.lib.uconn.edu/DoddCenter/ASC/Wilcox/Students.htm2Queue Container of objects that are inserted and removed according to the principle of
Berkeley - IS - 255
Foundations of Software DesignFall 2002 Marti HearstLab 7 discussion topics: Casting, File I/O, Enumeration, and StacksJohn Fritch, Kaichi Sung, Leah Zagreus1Type Casting Assigning primitive data when it would result in loss of precision(a)
Berkeley - IS - 255
Foundations of Software DesignFall 2002 Marti HearstLecture 14: Intro to Recursion1RecursionAn algorithmic technique in which a function, in order to accomplish a task, calls itself with some part of the task.2Recursion A method that inv
Berkeley - IS - 255
Foundations of Software DesignFall 2002 Marti HearstLecture 15: Trees1Trees2Trees Trees are very important and useful They are usually drawn upside-down The allow us to represent hierarchy File system Book structure Employees in a bu
Berkeley - IS - 255
Foundations of Software DesignFall 2002 Marti HearstGuest Lecture: Good Programming PracticesMarat Boshernitsan Computer Science Division, EECS University of California, Berkeley1Whats So Hard About Programming? Knowing where to begin and wh
Berkeley - IS - 255
Foundations of Software DesignFall 2002 Marti HearstLecture 17: Binary Search Trees; Heaps1Our Binary Tree Defined recursively as consisting of Data (in this example: int and String, but can be anything!) A lefthand Binary Tree A righthand
Berkeley - IS - 255
Foundations of Software DesignFall 2002 Marti HearstLecture 18: Hash Tables1Unresolved Question on Heaps Q: What happens if there is more than one item to swap with? A: Swap with the larger one.2Removing the Top of the Heap45 Move the
Berkeley - IS - 255
Foundations of Software DesignFall 2002 Marti HearstLecture 19: B-Trees: Data Structures for Disk1Data Structures & Memory So far weve seen data structures stored in main memory What happens when you have a very large set of data? Too slow
Berkeley - IS - 255
Foundations of Software DesignFall 2002 Marti HearstLecture 20: Sorting1Sortingwww.naturalbrands.com2SortingPutting collections of things in order Numerical order Alphabetical order An order defined by the domain of use E.g. color,
Berkeley - IS - 255
Foundations of Software DesignFall 2002 Marti HearstLecture 21: Sorting, cont.1QuickSort A Divide-and-Conquer recursive algorithm Divide: If only one item in list, return it. Else choose a pivot, and Divide the list into 3 subsets Items
Berkeley - COURSES - 256
Foundations of Software DesignLecture 23: Finite Automata and Context-Free Grammars Marti HearstFall 20021Outline Finite State Automata Why regular expressions are not enough Context Free Grammars Next Time: Relationship to Programming Lang
Berkeley - IS - 255
Foundations of Software DesignLecture 23: Finite Automata and Context-Free Grammars Marti HearstFall 20021Outline Finite State Automata Why regular expressions are not enough Context Free Grammars Next Time: Relationship to Programming Lang
Berkeley - IS - 255
Foundations of Software DesignLecture 24: Compilers, Lexers, and Parsers; Intro to Graphs Marti HearstFall 20021How Do Computers Work (Revisited)?Programming Languages Compiler CPUAddress Space Code vs. DataAssembly Language Machine Instruc
Berkeley - IS - 255
Foundations of Software DesignFall 2002 Marti HearstLecture 25: Graph Traversal, DAGs, and Weighted Graphs1Graph Traversal2Slide adapted from Goodrich & Tamassia3Exploring a Labyrinth Without Getting Lost A depth-first search in an un
Berkeley - IS - 255
Foundations of Software DesignLecture 26: Text Processing, Tries, and Dynamic Programming Marti Hearst & Fredrik WallenbergFall 20021Problem: String Search Determine if, and where, a substring occurs within a string2Approaches/Algorithms:
Berkeley - IS - 255
Foundations of Software DesignLecture 27: Java Database Programming Marti HearstFall 20021Accessing DBMSs DBMS: DataBase Management System SQL: standard query language Typed at command line Forms-based interfaces Direct-manipulation interf
Berkeley - IS - 255
SIMS 255 Foundations of Software Design Complexity and NP-completenessMatt Welsh November 29, 2001mdw@cs.berkeley.edu1OutlineComplexity of algorithms Space and time complexity `Big O' notation Complexity hierarchies and algorithm examples
Berkeley - IS - 255
Foundations of Software DesignFall 2002 Marti HearstLecture 29: Computability, Turing Machines, Can Computers Think?1Computability Is there anything a computer cannot compute? Linked to the notion of what is an algorithm.2Alan Turing An
Berkeley - ECON - 242
Nonlinear IV Panel Unit Root TestsYoosoon Chang1 Department of Economics Rice UniversityAbstractThis paper presents the nonlinear IV methodology as an eective inferential basis for nonstationary panels. The nonlinear IV method resolves the infere
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Nonparametric Demand Systems and a Heterogeneous PopulationStefan Hoderlein Mannheim University September 2003 - Very Preliminary Abstract This paper is concerned with the econometric modelling of the demand behavior of a population with heterogeneo
Berkeley - ECON - 242
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Berkeley - ECON - 242
Fuhglw Frqvwudlqwv lq wkh Pdunhw iru Frqvxphu Gxudeohv= Hylghqfh iurp Plfur Gdwd rq Fdu OrdqvRud}lr S1 Dwwdqdvlr|Slqhorsl N1 Jrogehuj} Dsulo 5336Hndwhulql N|uld}lgrx{Devwudfw Zh lqyhvwljdwh wkh hpslulfdo vljqlfdqfh ri eruurzlqj frqvwudlqwv lq
Berkeley - ECON - 242
Estimating Default Correlations from Short Panels of Credit Rating Performance Data1Michael Gordy Erik HeiteldJanuary 29, 2002opinions expressed here are those of the authors, and do not reect the views of the Board of Governors or its sta. Mich
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An Empirical Examination of Corporate Tax NoncomplianceMichelle Hanlon University of Michigan Lillian Mills University of Arizona Joel Slemrod University of MichiganJune 24, 2005 Revised draftThis paper was prepared for the conference on Taxing
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The Changing Role of Auditors in Corporate Tax PlanningEdward L. Maydew University of North Carolina Douglas A. Shackelford University of North Carolina and NBERJune 2005Abstract This paper examines changes in the role that auditors play in cor
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Corporate Taxation and International CompetitionbyJames R. Hines Jr. University of Michigan and NBERJuly 2005I thank Justin Garosi and Claudia Martnez for excellent research assistance, and Jack Mintz and Jay Wilson for very helpful comments
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How Elastic is the Corporate Income Tax Base?Jonathan Gruber, MIT and NBER Joshua Rauh, University of Chicago and NBER June 2005Presented at Taxing Corporate Income in the 21st Century, May 5-6, 2005. We are grateful to Matt Levy for research assi
Berkeley - USERS - 05
On The Extent, Growth, and Efficiency Consequences of State Business Tax PlanningDonald Bruce,1 John Deskins,2 and William F. Fox11University of Tennessee and 2Creighton University June 2005ABSTRACT: Our focus in this essay is on the extent to
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Taxation and the Evolution of Aggregate Corporate Ownership ConcentrationMihir A. Desai Harvard University and NBER mdesai@hbs.edu Dhammika Dharmapala University of Connecticut dhammika.dharmapala@uconn.edu Winnie Fung Harvard University fung@fas.ha
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The 2003 Dividend Tax Cuts and the Value of the Firm:An Event StudyAlan J. Auerbach University of California, Berkeley and NBER Kevin A. Hassett American Enterprise Institute June 2005This paper was presented at the OTPR/Burch Center conference,
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Dissecting Dividend Decisions:Some Clues About the Eects of Dividend Taxation from Recent UK ReformsStephen R Bond Institute for Fiscal Studies and Nu eld College, Oxford Michael P Devereux University of Warwick and Institute for Fiscal Studies Ale
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Bargains-then-ripoffs: Innovation, pricing and lock-in in enterprise softwareIan Larkin Harvard Business School ilarkin@hbs.eduAbstractIn industries with quick innovation cycles and switching costs, vendor profitability is often driven by the ab
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STRUCTURAL HOLES, TECHNOLOGICAL RESOURCES, AND INNOVATION: A LONGITUDINAL STUDY OF AN INTERFIRM R&D NETWORKHANS T. W. FRANKORTUniversity of Maastricht Faculty of Economics and Business Administration METEOR / Department of Organization & Strategy
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Optimal Sharing Strategies in Dynamic Games of Research and Development1Nisvan Erkal2 and Deborah Minehart3 First version: January 2005 This version: September 2007The views expressed do not purport to represent the views of the United States Depa
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Organizing and Leading R&D Teams and the Effects on Firm PerformanceAnne H. KochThe need for an effective and rapid product development process for a firm seeking to achieve a competitive advantage has been recognized. In particular, previous lit
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Political influence through the veil of peer review: evidence from biomedical research fundingVersion dated: July 09, 2007 Preliminary & incomplete Please DO NOT cite or quote without the authors written consent Deepak Hegde Walter A. Haas School of
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Basic Welfare Economics and Optimal Tax TheoryThere are two criteria by which economists measure the outcomes of tax policy: 1. E ciency, which is traditionally the purview of economics, and does not involve ethical and normative judgments. E ciency
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Optimal ProgressivityTo this point, we have assumed that all individuals are the same. To consider the distributional impact of the tax system, we will have to alter that assumption. We have seen that dierential lump sum transfers can move the econo
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Incidence and DistributionTheory of Tax Incidence There are four principles in the analysis of tax incidence: 1. The entire burden of a tax must be traced to individuals. Businesses may remit taxes, but only individuals shareholders, employees, cust
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Corporate Income TaxationWe have stressed that tax incidence must be traced to people, since corporations cannot bear the burden of a tax. Why then tax corporations at all? There are several possible justications. First, there are valuable benets, s
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Statistical Evidence and InferenceBasic Methods of Analysis Understanding the methods used by economists requires some basic terminology regarding the distribution of random variables. The mean of a distribution is simply the arithmetic average of a
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Collateralized Asset MarketsJohn GeanakoplosYale UniversityWilliam R. ZameUniversity of California, Los Angeles Abstract Much of the lending in modern economies, including residential and commercial mortgages and corporate bonds, is secured by s
Berkeley - E - 208
Money Matters An Axiomatic Exploration of the Endowment Eect and the Preference Reversal PhenomenonRaphal Giraud e CRESE-University de Franche-Comt e First version: January 2007 This version: May 200711.11.1.1IntroductionMotivation and Aims o
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Repeated Games and Limited Information Processing Preliminary and IncompleteOlivier compte PSE, Paris Andrew Postlewaite University of Pennsylvania July, 2007Abstract Many important strategic problems are characterized by repeated interactions amo
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Foundations for Bayesian Updating Eran Shmaya Leeat YarivyzCurrent Version: September 21, 2007Abstract.We provide a simple characterization of updating rules that canbe rationalized as Bayesian. Namely, we consider a general setting in which
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Networks emerging in a volatile worldGeorge Ehrhardt The International Centre for Theoretical Physics, Trieste Matteo Marsili The International Centre for Theoretical Physics, Trieste Fernando Vega-Redondo European University Institute, Florence Fir
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Intertemporal Risk Aversion, Stationarity and DiscountingJobmarket Paper, October 2007 Christian P. TRAEGER Department of Agricultural & Resource Economics, UC Berkeley Department of Economics, UC Berkeley Abstract: The paper develops an axiomatic f
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Wouldnt it be Nice to Tell Whether Robinson is Risk Averse?September 2007 Christian P. TRAEGER Department of Agricultural & Resource Economics, UC Berkeley Department of Economcis, UC BerkeleyAbstract: The paper introduces a new notion of risk ave
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Knowing who speaks when: On commonly known consensusElias TsakasDepartment of Economics, Gteborg University, Sweden o Department of Economics, University of California, BerkeleyMark VoorneveldDepartment of Econometrics and Operations Research, T
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The Context of the GameAmanda Friedenberg Martin MeierOctober 2, 2007 Extremely PreliminaryIt seems to me that the basic reason why the theory of games with incomplete information has made so little progress so far lies in the fact that these ga
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Economics 162 The Chinese Economy Yingyi Qian Professor of EconomicsFeatures of the Course DescriptiveFacts What, when, where, who Not easyAnalyticalAbout the linkages among facts Why, how Even more difficultReasons for Study Intellectual c
Berkeley - ECON - 162
Economics 162 The Chinese Economy Yingyi Qian Professor of EconomicsReasons for Study Intellectual curiosity on China and its impact on the rest of the world Family, neighbor, friend, and classmate connection to China Doing business in or with C
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Geography Why economists think geography important to economic developmentTraditional subsistence economy: climate, bacteria, land, water, etc. Modern economy: trade, coastal lines, ports, roads, etc.Geography Jared Diamond, Guns, Germs, and St
Berkeley - ECON - 162
Geography Why economists think geography important to economic developmentTraditional subsistence economy: climate, bacteria, land, water, etc. Modern economy: trade, coastal lines, ports, roads, etc. Jared Diamond, Guns, Germs, and Steel: The Fa
Berkeley - ECON - 162
An Unusual Path of Transition from Plan to Market Uninterrupted growth since 1979 Unprecedented since 1840 Ex ante it was not clear if China could muddle throughDoubts in the late 1970s and early 1980s More doubts in 1989 and early 1990s Still mo
Berkeley - ECON - 162
An Unusual Path of Transition from Plan to Market Uninterrupted growth since 1979 Unprecedented since 1840 Ex ante it was not clear if China could muddle throughDoubts in the late 1970s and early 1980s More doubts in 1989 and early 1990s Still mo
Berkeley - ECON - 162
GDP: Data and Problems The Official dataAverage annual growth (1979-2004): 9.6% 2003: 10.0% 2004: 10.1% 2005: 10.2% 2006: 10.7% (preliminary)GDP: Data and ProblemsPossible problem (1)Miscalculation of the GDP deflator Growth rates could be over
Berkeley - ECON - 162
GDP: Data and Problems The Official dataAverage annual growth (1979-2004): 9.6% 2003: 10.0% 2004: 10.1% 2005: 10.2% 2006: 10.7% (preliminary)QuestionsIs the official data reliable? Is the growth rate overstated?Alternative method of estimation