Download - January 12, 1999Chapter 1 1 Software Engineering CPSC 431 MW 1:50 – 2:40 TTR 2:20 – 3:10. BRIGHT 113 ZACHRY 105b W. M. Lively Rm 427C, H. R. Bright Bldg.

January 12, 1999 Chapter 11

Software Engineering CPSC 431MW 1:50 – 2:40 TTR 2:20 – 3:10.BRIGHT 113 ZACHRY 105b

W. M. Lively

Rm 427C, H. R. Bright Bldg.

Office Hours: 4 – 5 pm M - TTR

Li Han Ashar Khan -- TA’s Off: TTR 8:30-9:45; 3:15-4:00pm Off: MF 2:00-4:00pm

328B Bright; 845-5009 425E Bright 845-4306

[email protected] [email protected]

Sections 503, 504,506 Sections501, 502, 505

Textbook: “Software Engineering -- A Practitioner’s Approach,” 4th Edition by Roger S. Pressman

mailto:[email protected]

mailto:[email protected]


Software Engineering CPSC 431

Reference books -- at least one recommended “UML in a Nutshell” by Sinan Si Albir, O’Reilly “UML Distilled” by Martin Fowler and Kendall Scott,

Addison-Wesley “The Unified Modeling Language User Guide” by Grady

Booch, James Rumbaugh and Ivar Jacobson , Addison-Wesley

“Visual Modeling with Rational Rose and UML” by Terry Quatrani , Addison-Wesley

“UML Notation Guide” -- available from copy center or web at http://www.rational.com/uml/resources/documentation/notation/index.jtmpl



Grading Mid-term Exam (Mar. 6/7) 30% Final Exam 30% Laboratory Project 30% Homework 10%

Course OutlinePart 1 -- The Product and the Process

Chapters 1 & 2



Course OutlinePart 3 -- Conventional Methods

System Engineering - Chapter 10 Analysis Concepts and Principles - Chapter 11 Analysis Modeling - Chapter 12 Design Concepts and Principles - Chapter 13 Design Methods - Chapter 14 Real-Time Design - Chapter 15 Testing Methods and Strategies - Chapter 16 & 17 Metrics for Software - Chapter 18



Course OutlinePart 4 -- Object-Oriented SE

Object-Oriented Concepts - Chapter 19 Object-Oriented Analysis - Chapter 20 Object-Oriented Design - Chapter 21 Object-Oriented Testing - Chapter 22 Metrics for Object-Oriented Systems - Chapter 23



Course OutlineRe-ordered topics -- Testing

Testing Methods and Strategies - Chapter 16 & 17 Object-Oriented Testing - Chapter 22



Course OutlinePart 2 -- Software Management

Management Concepts -- Chapter 3 Software Process and Project Metrics -- Chapter 4 Project Planning -- Chapter 5 Risk Management -- Chapter 6 Scheduling and Tracking -- Chapter 7 Quality Assurance -- Chapter 8 Configuration Management -- Chapter 9



Course OutlinePart 5 -- Advanced Topics

Formal Methods -- Chapter 24 Cleanroom SE -- Chapter 25 Software Reuse -- Chapter 26 Re-engineering -- Chapter 27 Client/Server -- Chapter 28 Computer-Aided Software Engineering -- Ch. 29 The Future -- Chapter 30


Software Engineering — Introduction

What is Software Engineering (SE)? The process of building a software product.

Some questions to put SE in perspective: What are the sizes of some typical software products?

Maple.exe = 1.3 Mbytes.-- System over 3.8 MbytesNetscape.exe = 1.26 megabytes.Microsoft Office 97 > 180 megabytes.

How many people would it take to build these in 1 year? 2? What would you do if a bug could cost lives and $2 billion? What would you do if a delay could cost $100’s of millions?


Software Engineering — Introduction Some questions to put SE in perspective (con’t):

What is the impact of distributing buggy software? Why do we have so many software upgrades? What is the impact of software upgrades?

Why is it so difficult to measure software development progress?

What are some of the ethical issues in software development?

Why does it take so long to develop software? Why does software cost so much?

Why do people continue to use buggy and/or obsolete software?


Some Software Characteristics

Software is engineered or developed, not manufactured in the traditional sense.

Software does not wear out in the same sense as hardware.



In theory, software does not wear out at all.

BUT, Hardware upgrades. Software upgrades.



Thus, reality is more like this.

Most serious corporations control and constrain changes Most software is custom built, and customer never really knows what she/he wants.


Some General Approaches

Develop and use good engineering practices for building software.

Make heavy use of reusable software components. Use modern languages that support good software development

practices, e.g., Ada95, Java. Use 4th generation languages. But, almost everything is a two-edged sword.

Consider long term tool maintenance.Right now, this is a major problem for NASA.


Types of Software Applications

Systems Software Real-Time Software Business Software Engineering Software Embedded Software Artificial Intelligence Software Personal Computer Software


Software Myths

Myth: It’s in the software. So, we can easily change it. Reality: Requirements changes are a major cause of

software degradation. Myth: We can solve schedule problems by adding more programmers.

Reality: Maybe. It increases coordination efforts and may slow things down.

Myth: While we don’t have all requirements in writing yet, we know what we want and can start writing code.

Reality: Incomplete up-front definition is the major cause of software project failures.


Software Myths

Myth: Writing code is the major part of creating a software product. Reality: Coding may be as little as 10% of the effort, and

50 - 70% may occur after delivery.


Percent Maintenance Historgram

0%

5%

10%

15%

20%

25%

30%

35%

(0,15] (15,30] (30,45] (45,60] (60,75]


Software Myths

Myth: I can’t tell you how well we are doing until I get parts of it running.

Reality: Formal reviews of various types both can give good information and are critical to success in large projects.

Myth: The only deliverable that matters is working code. Reality: Documentation, test history, and program

configuration are critical parts of the delivery. Myth: I am a (super) programmer. Let me program it, and I will get it done.

Reality: A sign of immaturity. A formula for failure. Software projects are done by teams, not individuals, and success requires much more than just coding.


25%

31%

13%

8%

6%

0%

5%

10%

15%

20%

25%

30%

35%

<=2 (2.4] (4,6] (6,8] (8,10] >10

Estimate in weeks


SLOCs per Year Histogram

0%

5%

10%

15%

20%

25%

30%

35%

40%

(0,5k] (5k,10k] (10k,20k] (20k,50k] >50k

Series1