CMP 131 Introduction to Computer Programming Violetta CavalliSforza

CMP 131 Introduction to Computer Programming Violetta Cavalli-Sforza Week 2, Lecture 1

The trouble with being punctual is that nobody's there to appreciate it Franklin P. Jones

Homework #1 Due Thursday March 8 • Textbook pg. 25 (Exercises 1. 3) – – – – Exercise 1 Exercise 2 a and 2 b (not 2 c) Choose two of Exercise 3 a – 3 d (not 3 e) Exercise 4 for one of what you did for Exercise 3 Exercise 5 a Exercise 9 (state any assumptions you make) Exercise 10 a • Turn in at the beginning of class (lab)

Brief Review of Last Week • Logistics: – folder mechanism is in place for exchanging information – Stuff is up on my web page: http: //www. cs. cmu. edu/~violetta/ • Hardware and software trends • Hardware components • Hardware/Software interface – Layers of the Machine – Kinds of Software • Computer Languages • Syntax, Semantics, Grammars • What happens to your program? – Compilation, Linking, Execution – Program errors • Compilation vs. Interpretation etc.

Outline • Programming Languages – a typology and a brief history • Software Development process • Software Engineering steps • Homework assignment • Ideas from students (thank you …) • Some case studies (or next time)

What is a Computer Programming Language? • A special purpose and limited language • A set of rules and symbols used to construct a computer program • A language used to interact with the computer • Capable of expressing any computer program – Equivalent to a universal Turing machine – All PLs are equivalent in this sense

Differences among PLs • Not theoretical but practical: – How easily do they allow you to perform your task • The Sapir-Whorf hypothesis: – “the structure of language defines the boundaries of thought” – The wrong language prevents certain tasks from being accomplished? – Maybe not, but can certainly impede progress.

Different Ways of Categorizing PLs • By closeness/remoteness from the machine/human/application: – machine, assembly, high-level • By application type • By computational model

Machine Languages • • • “Natural language” of a particular computer Defined by hardware design of computer Generally consists of strings of numbers Are machine dependent Cumbersome for humans – Example: Adding overtime pay to base pay and storing the result in gross pay +1300042774 +1400593419 +1200274027 • Slow and tedious for most programmers

Assembly Languages • Programmers began using English-like abbreviations to substitute for machine languages • Represents elementary operations of computer • Translator programs called assemblers convert assembly-language to machine-language • Example: LOAD BASEPAY ADD OVERPAY STORE GROSSPAY

High-Level Languages • Single statements can be written to accomplish substantial tasks • Allow programmers to write instructions almost like every-day English (preferable!) • Example: gross. Pay = base. Pay + over. Time. Pay • Developed as computer usage increased, assembly language proved inadequate and time-consuming • Evolved hand-in hand with hardware and applications

PL Application Types • • • Numeric/scientific languages (FORTRAN) Business languages (COBOL) Artificial Intelligence languages (Lisp, Prolog) Systems languages (C and its ancestors) Multi-purpose languages (. . , Ada, Java, C++) Process/Scripting languages: view programs and files as primary data objexts to be manipulated (e. g. Perl, JCL, shell scripting languages) • Publishing/Document Description Languages (e. g. Scribe, Te. X, SGML, HTML, XML) • Other special purpose: APL, Snobol, BASIC, Visual BASIC, …

Computational Model Types • A PL supports one or more computational models • Computational models include – – Imperative Functional or Applicative Rule-Based or Logical Object-Oriented

Imperative PLs • Command-driven, statement-oriented • Data is modified by statement execution • Efficient • Structured programming: Pascal • Modular: Modula and successors

Functional or Applicative PLs • What is the function that must be applied to the initial machine state to provide the desired result? • Function composition – F (G (H (x))) – Average = divide ( sum (data), number (data) ) • No global data (in the extreme) • Example: – Lisp (used in Artificial Intelligence)

Rule-Based or Logical PLs • Rule-Based: – Execute an action if enabling conditions are satisfied – Logical-programming (Prolog) • Declarative programming: say what you know and what you want to know – Connection to expert systems in artificial intelligence • E. g. medical diagnosis, financial advisors – Example: • Rules: likes(john, X) : - likes(X, food). may_steal(X, Y) : - thief(X), likes(X, Y). • Facts: thief(john). likes(mary, food). • Conclusion? likes(john, mary) may_steal(john, mary)

Object-Oriented PLs • Attempt to merge the best of – imperative (efficiency) – functional (flexibility and reliability) • Ideas: • Data is encapsulated in objects • Actions happen by sending message to objects • E. g. send a message to a window to tell it to turn its background red • Small. Talk was the first OO language (Xerox PARC) • Examples: – Ada, C++, Java

Evolution of Imperative PLs FORTRAN IV (1950’s) COBOL (1960’s) PL/1 (1970’s) Algol (1960’s) Simula 67 Pascal (1970’s) B (1969 -70) Smalltalk (1970 s-80’s) Ada 83 Modula (1980’s) Java (1990’s) Ada 95 (B)CPL (1960’s) C (1971 -73) C++ (1990’s)

Factors Affecting PL Evolution • • Applications Hardware/Software trends Programming environments Language Standardization Internationalization Theoretical studies Economics

Hardware Trends • Every year or two computer power approximately doubles – Memory size (RAM) • Memory used to execute programs – Secondary storage (permanent storage) • E. g. disk storage, used to to hold programs and data over time – Processor speeds • Speed at which computers execute their programs

Hardware/Software Trends • Applications: – Rapidly increasing hardware power allows applications to get bigger and more complex • Costs – Hardware costs dropping – Software development costs rising • Software development complexity • Programmer salaries • Cost of slipping schedules – Unanticipated interactions in complex systems – Unpredictability of software development times

The PL Design Response STRUCTURED PROGRAMMING • Disciplined approach to writing computer programs • Control structures reflect the logic of the program – Eliminate/reduce use of GOTO instructions (spaghetti code) • Data structures reflect the types of data processed by the application • Clearer and easier to debug and modify than unstructured programs

“MODULAR” PROGRAMMING • • Large software system Multiple users Reusable components Divide program into modules (packages), each with – An interface, visible to the user of the module – An implementation or body, visible to programmer of module • Isolates users of modules from implementation changes.

OBJECT-ORIENTED PROGRAMMING • Objects : Reusable software components that model items in the real world – Created from class descriptors – Inherit and modify class descriptions • OOP makes software developers more productive – Object-oriented programs often easier to understand, correct and modify than older types of programs • Currently the main programming language paradigm – But it is built on the imperative programming model such as contained in Pascal

Our Language • Pascal – High-level, general-purpose programming language – Developed in 1971 by N. Wirth of ETH at Zurich, Switzerland – Popular programming language for teaching programming concepts – Easy-to-learn syntax – Allows writing structured programs that are easy to read and understand – Robust against programmer’s errors • Turbo Pascal – A dialect of Pascal developed by Borland International – The IDE shows its age…

The Software Development Process

SW Development Process • Software Lifecycle (from Online dictionary) – The phases a software product goes through between when it is conceived and when it is no longer available for use. The software life-cycle typically includes the following: • • Requirements analysis Design Construction (implementation) Testing (validation) Installation Operation Maintenance Retirement • • Quality assurance Marketing Sales Support. – The development process might need to iterate through these phases rather than linearly; – Other processes associated with a software product are:

SW Design as Problem Solving • Formulate a solution as an algorithm – Design solution alternatives • Data representation • Computational procedures – Evaluate and select solution • Several criteria and methods (next slide) – Implement solution • State solution in Programming Language • Sometimes need to prototype before selecting design

Evaluating a SW Solution • Correctness – mathematical verification techniques – testing • Efficiency – mathematical complexity calculations – informal and empirical evaluations • Style – elegance – clearness – conciseness • Reusability – generality – modularity

SW Development Method • Software Engineering (SE): (from Online dictionary) – Systematic approach to the analysis, design, implementation and maintenance of software. – Often involves the use of “Computer Aided Software Engineering” (CASE) tools. – Various models of the software life-cycle, and many methodologies for the different phases exist. • Computer Aided Software Engineering (CASE): – A technique for using computers to help with one or more phases of the software life-cycle. It involves software tools and training for the developers who will use them. • Top-Down Design (Or “Stepwise Refinement"): – Software design technique that describes functionality at a very high level, then partition it repeatedly into more detailed levels one level at a time until the detail is sufficient to allow coding

SW Development Method • Structured language: – A programming language in which a program could be broken down into modules (procedures or functions) that could be written without detailed knowledge of the inner workings of other modules. This allows implementing the top-down design approach. • Structured programming: – Any software development technique that includes structured design and results in the development of a structured program. – Resulting programs are easy to maintain, read, and understand. • We will consider the following “SW-Engineering Steps” to solve Pascal’s programming problems: 1. Problem statement 2. Analysis Language 3. Design independent 4. Implementation 5. Testing & verification Language 6. Maintenance dependent AND DOCUMENT THROUGHOUT!!!

SW-Engineering Steps: Problem Statement • Clearly state the problem in English. • Gain a clear understanding of what is required for its solution. – Become an expert in the domain – Interact a lot with experts/clients – The “Knowledge Engineer” figure in Artificial Intelligence • Try to eliminate unimportant aspects to the main problem. • If the problem is not totally defined, request more information.

SW-Engineering Steps: Analysis • Underline relevant phrases in the problem statement • Develop a list of problem variables & their relationships • Identify the problem input variables • Identify the desired output variables • Identify local or temporary variables • Determine type & units for the variables • Determine how should the output be displayed • Determine relevant formulas needed to transform input into output • Identify any additional requirements or constraints

SW-Engineering Steps: I/O Variables • Input Variables: – Found in the problem statement – Describes input data supplied to the problem from outside – Look for key words: • Accepts, reads, prompts, input – Example: Read value into Fahrenheit temperature • Output Variables: – Found in the problem statement – Describes information computed & displayed to user after processing – Look for key words: • Writes, prints, displays, output – Example: Print “Case Study 1” on the screen

SW-Engineering Steps: Design • The most difficult part in the problem-solving process. • Develop a list of steps (Algorithm) to solve the problem, using pseudocode, flowcharts, or IPO (Input/Process/Output chart) charts • Test if the algorithm will solve the problem as intended by using sample input and checking the output. • Top-Down design should be used. • Pseudocode: – – English-like statements Describes steps of an algorithm in the “Design” phase Not a programming language Example Repeat for N = 1 to 10 Calculate N Squared Calculate N Cubed Print N, Squared, Cubed

SW-Engineering Steps: Design • IPO (Input-Processing-Output) model – Input Requirements (declaration & action) – Processing Requirements (action) – Output Requirements (declaration & action) • Input & output declarations are covered by the analysis phase • I/O and processing actions are covered by the design phase by writing an algorithm – Write pseudocode for input – Write pseudocode for processing – Write pseudocode for output • Processing Requirements – Explains how is input transformed into output – Might need to use intermediate evaluation steps – Look for key words: • Calculate, convert, compare, count, evaluate, etc. – Example: Count the total number of characters Calculate squares and cubes for 1 through 10

Example on Board • Problem: Counting characters in a string • Input from terminal a word (character string) • Output to terminal how many characters it contains • Process (high-level): – Read one character at a time – Add one to the count for each character read – Repeat the above 2 steps until there are no more characters

• Process (refinement): Initialize Count to 0 REPEAT: Read one character at a time If a character is found, add 1 to Count UNTIL there are no more characters

SW-Engineering Steps • Implementation: – Implement the algorithm as a program using a particular programming language by converting each step in the algorithm into statements in the programming language. – Document the implementation all along… • Testing & Verification: – Desk-check the solution steps – Test the completed program by running it several times using several test data sets. – Use sample test data that covers • Correct possibilities • Incorrect possibilities – Repeat any of the steps if error is detected • Maintenance: – Modify the program • Remove previously undetected errors • Add new features • Change the program according to company or government policy changes.