|
|
|
By Jaime Nińo and Fred Hosch |
|
|
|
Slides by Darwin Baines and Robert Burton |
|
|
|
|
|
|
Computing as a science. |
|
Analysis of large software systems. |
|
Fundamental hardware and software concepts. |
|
Basic notions necessary to survive. |
|
|
|
|
|
|
|
|
|
|
Computer science is a science of abstraction –
creating the right model for a problem and devising the appropriate
mechanizable technique to solve it. |
|
A. Aho and J. Ullman |
|
Not the “science of computers.” |
|
Many concepts were framed and studied before the
electronic computer. |
|
To the mathematicians and logicians of the ‘20’s
and ‘30’s, a computer was a person with pencil and paper. |
|
|
|
|
|
|
Automated problem solving. |
|
Automated systems that produce solutions. |
|
Methods to develop solution strategies for these
systems. |
|
Application areas for automatic problem solving. |
|
|
|
|
|
Fundamental mathematical and logical structures
for |
|
Understanding computing. |
|
Analyzing and verifying the correctness of
software and hardware. |
|
|
|
|
computer architecture: organization and
structure of hardware. |
|
operating systems: fundamental resource
management software. |
|
distributed systems:connecting computers
together in order to share resources and information (networking). |
|
|
|
|
|
There are numerous applications of computer
science. |
|
Artificial intelligence |
|
Data base management |
|
Computer graphics |
|
Etc. |
|
|
|
|
Design and assessment of techniques and software
tools for creating automated problem solutions. |
|
programming language: a precise, formal notation
for expressing and analyzing a natural, correct, efficient program
solution. |
|
|
|
|
software engineering: the study of methods for
producing and evaluating software. This is the primary topic of the course. |
|
|
|
|
|
|
|
|
|
Sometimes called a program or application. |
|
A temporary solution to a changing problem. |
|
2 major attributes inherent in Software Systems: |
|
dynamics |
|
complexity |
|
|
|
|
|
|
composite structure: a software system composed
of manageable pieces. |
|
The smaller the component, the easier it is to
build and understand. |
|
The more parts, the more possible interactions
there are between parts and the more complex the resulting structure. |
|
|
|
|
Balance between simplicity and interaction
minimization. |
|
Example: An audio system is easy to manage
because each component has a carefully specified function and the
components are easily integrated
i.e. The speakers are easily connected to the amplifier. |
|
|
|
|
The function of a “Rube Goldberg” device is not
obvious, modification is almost unthinkable, the parts lack an intrinsic
relationship to the problem the device solves, and the complexity is high. |
|
|
|
|
|
|
|
|
Dealing with system components and their
interactions without worrying about specific details. |
|
Not “vague” or “imprecise.” |
|
Limitation of view to a few properties. |
|
Elimination of the irrelevant and amplification
of the essential. |
|
Capturing commonality between different things. |
|
|
|
|
|
|
data: the information the program deals with. |
|
functionality: what the program does with the
data. |
|
|
|
|
data descriptions: the type of data to be
stored; fixed for a program. |
|
data values: actual values stored for each
descriptor. Variable each time the program is run. |
|
Example: |
|
Descriptor: Name Value:John |
|
|
|
|
computation: Performance of some sequence of
goal-directed actions with a set of data values. |
|
processor: Whatever performs the actions. |
|
algorithm: A set of instructions describing a
pattern of behavior guaranteed to achieve some goal. |
|
|
|
|
|
|
Composite, modular constructions, built using
abstraction, consisting of components, and organized around the data. |
|
model: an abstraction of a real-world problem
that can be represented and manipulated with a computing system. |
|
software system: a collection of abstractions
that work together to solve problems. |
|
|
|
|
These abstractions are called objects. |
|
Each object has its own properties and functionality. |
|
Reusable components are the key to the efficient
production of reliable systems. |
|
|
|
|
Input/Output:communication with the outside
world. |
|
processor: performs computation using the instruction
set. |
|
memory: stores the data and algorithms the
system is using, i.e. RAM. |
|
file system: stores data and algorithms not
currently being used. i.e. disks. |
|
network: several connected computers and other
devices. |
|
|
|
|
|
|
|
Data encoding schemes: letters, digits,
punctuation marks, etc. often are encoded as 8-bit sequences. |
|
“A” might be represented as 01000001. |
|
Memory is divided into fixed size locations,
each with a unique address. |
|
Data and instructions are accessed using their
addresses and by knowing their sizes. |
|
|
|
|
|
|
operating system: fundamental hardware-resource
management software. |
|
programming language: the formal expression of
data descriptions and algorithms. |
|
text editor: the means provided for us to write
and edit programs. |
|
|
|
|
compiler: a program that translates programs
written in a specific programming language into a a more machine-like
language. |
|
interpreter: software that reads and executes a
machine-independent intermediate language (called “byte-code” in the case
of Java). |
|
|
|
|
|
|
MS-DOS, Windows NT, Solaris, etc. |
|
Performs functions such as verifying user names
and passwords, deciding where in memory a program will be loaded, loading
programs from disk, etc. |
|
|
|
|
Java, C++, Pascal, etc. |
|
syntax: the set of grammatical and punctuation
rules for the language. |
|
semantics: the set of rules that specify the
meaning of syntactically legal constructs. |
|
Each legal construct has exactly one meaning. |
|
|
|
|
error: a mistake made in the design,
programming, or use of a system. |
|
failure: inability of the system to perform its
intended function. |
|
Errors cause failures. |
|
|
|
|
|
Computer systems fail because of |
|
conceptual errors |
|
data errors |
|
hardware failures |
|
software errors |
|
Conceptual errors: misunderstanding of the
system. |
|
|
|
|
|
data errors: incorrect data. |
|
software errors: |
|
Syntactic errors: writing something that the
programming language doesn’t understand.
They are found during compilation. |
|
Logical errors: syntactically correct but not
programmed logically (“wok your dog”) |
|
|
|
|
|
|
Large systems are complex and change over time. |
|
To address this, we build systems that are
modular, with composite construction, and make extensive use of
abstraction. |
|
We also covered, a conceptual model of the
supporting hardware system; |
|
And a basic understanding of the purpose of
fundamental software such as operating systems, compilers, and
interpreters. |
|
|
|
|
|
|
|
|
|
|
|
|
|
Notes