Data Storage - Fundamental of Computer - Handout | CECS 230, Study notes of Computer Science

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Chapter 1 – Data Storage  Boolean Operations o AND (^) o OR (v) o XOR o NOT  Flip-flop circuit = circuit built from gates that can store one bit  Mass Storage o Magnetic Discs  Constant angular velocity with track divided into sectors o CD-ROM  Constant linear velocity with one continuous track o Magnetic Tape  Sequential access – not random access o Flash Memory  Finite number of read/write cycles, not subject to magnetic fields  Binary Numbers o Binary: 11012 o Decimal: 1310 o 2’s Complement Notation  1st bit = sign bit (0: positive, 1: negative)  Toggle bits  Add 1  Then, transfer as usual o Overflow  Adding two positive numbers yield a negative number  Adding two negative numbers yield a positive number o Hexadecimal notation  Binary Hexadecimal Decimal 0000 0 0 0001 1 1 0010 2 2 0011 3 3 0100 4 4 0101 5 5 0110 6 6 0111 7 7 1000 8 8 1001 9 9 1010 A 10 1011 B 11 1100 C 12 1101 D 13 1110 E 14 1111 F 15 o Floating Point Notation  1st bit = sign bit (0 = positive, 1 = negative)  Bit 2,3,4 = exponent Bit Pattern Value 111 3 110 2 101 1 100 0 011 -1 010 -2 001 -3 000 -4  Bit 5,6,7,8 = mantissa CHAPTER 2 – Data Manipulation  Main Memory -> Bus -> Cache -> Registers (slowest -> fastest)  Easier to understand and more efficient o Recursive – call themselves, repeating the set of instructions as a sub-task of itself  Searches o Sequential – start at first element and query each consecutive element until you: 1) find target 2) reach end o Binary – requires an ordered list, start at center element and subdivide until: 1) 0 elements are left 2) find target o Binary search is generally more efficient  Big “O” Notation o Classifies algorithms according to complexity in time or space o f(x) = O(g(x)) if lim n→∞ |f (n)/g (n)|<∞ o Typical BigO Complexities (Best -> Worst)  O(1) -> getting specific element from list  O(log2n) -> Binary Search, dividing list in half  O(n) -> scanning a list  O(nlog2n) -> MergeSort  O(n2) -> Bubble Sort, Matrix/Vector multiply, Insertion Sort  O(nc) -> nested for loops(c levels)  O(cn)  O(n!) o Time to solve problem depends on data  Average-case time  Best-case time = data is well suited for algorithm  Worst-case time = data is such that algorithm performs poorly (time-wise) CHAPTER 7 – Software Engineering  Development Phases o Requirements Specifications  Determines business objectives  Understands customers’ needs and wants  Goal is to specify what services the proposed system will provide and identify any conditions on those services  Prototyping – visual side of program with no guts  UNDERSTAND PROBLEM BEFORE CODING o Design  Creating a plan for the construction of the proposed system  Modularity  Separate things into specific sections  Difficult to understand entire systems  Ideally only need to understand a few modules to correct a bug  Information Hiding  Limit access as much as possible  NO global variables unless constants  Limit access to variables, data, and internal logic  Coupling  Limit dependencies between modules  Control coupling – one module calling another  Data coupling – data being used in multiple locations  Minimize coupling -> more modularity  Cohesion  How focused the responsibilities of a module are  Logical cohesion – logically similar tasks in one module  Functional cohesion – all parts of a module are focused on a single activity  Increase cohesion -> decrease coupling o Implementation  Actual writing of the programs, creating of data files, and development of databases  Take the design specifications and code o Testing  Successful test identifies a bug  Impossible to test all paths  Equivalence classes  Similar inputs that vary (i.e. +/-) can lead to discovery of bugs  Use data relative and applicable to the use of the program  Boundary Testing  Values close to limits are most error prone  Check barely valid and invalid values  Black Box Testing  Testing is done supplying inputs to mysterious code  Performed from users point of view  Usually tested with actual users of the system and checked on system level and component level  White Box (Glass)  Testing values based on code  Applied to unit test  Spotting possible bugs based on the code  Basis Path Testing  Test every line of code  Develop inputs that will run through all possibilities that could exist within your code  No line should be left untouched or unexecuted after development  Test Coverage  Applications that attempt to test your code and run it through all possibilities to break the code  Alpha/Beta Testing  Alpha = early release within company or organization for testing  Beta = early release to public  Pros: Others find bugs, promotes product before official release  Waterfall Method:  Requirements  Design  Construction  Integration  Verification  Installation  Maintenance  Iterative Model  Initial Planning -> Planning -> Requirements -> Analysis & Design -> Implementation -> Testing -> Evaluation -> Deployment o BOLD: Repeated if necessary  Extreme Programming  Style of programming used to help develop, design, implement, test, and finalize programs  Simplicity  Making modules or functions simple and not adding extra “clunk” code in order to create simple yet effective designs  Pair Programming  Two programmers, one computer  One codes while the other watches and tries to catch for any bugs or issues that could arise  Slow, but less bugs in code  Unit Testing  Helps locate and narrow down manageable chunks of code to identify bugs  Regression tests prevent the bug from re-entering the code  Continuous Integration  Nightly builds are done on the code to ensure that no bugs are issues, no one can leave until it works  DOCUMENT, DOCUMENT, DOCUMENT CHAPTER 8 – Data Abstractions  Joins tables together in large database  EX. FROM table1 JOIN table2 ON table1.colX = table2.colY CHAPTER 11 – Artificial Intelligence (AI)  AI -> originally aimed at mimicking human intelligence  Search Trees o Depth First Search  Traverse child, before siblings o Breadth First Search  Traverse siblings first, then children  Heuristics o Two Requirements:  Efficient -> if the heuristic is slow, it may be more efficient to construct the entire tree and look at every state  Accurate -> if the accuracy is questionable, wrong paths more often will be taken  Machine Learning o It is finding patterns in large amounts of data  Artificial Neural Networks o Each input is multiplied by a weighting factor o Output is 1 if sum of weighted inputs exceeds the threshold value, 0 otherwise o Network is programmed by adjusting weights using feedback from examples  Nearest Neighbor o Most simple algorithm may be the Nearest Neighbor algorithm o Steps  Memorize every training example  Classification involves searching through all examples to find closest match  Output class of closest match o Very fast to train. Simply save the data set. o Can be very slow to classigy new examples, but very simple to implement OTHER AREAS TO STUDY  Student Presentations  ESSAY: In a hypothetical situation our university must close one of three departments: Accountancy, Emergency Medicine, or CECS. Defend the CECS department. Consider the claims of the other departments. Be sure to answer the underlying question: How does society benefit from a software engineer?