Brainscape's Knowledge GenomeTM

Browse over 1 million classes created by top students, professors, publishers, and experts.


See full index

CMA1 > 20 Systems Development > Flashcards

20 Systems Development Flashcards

1
Q

Software Development Life Cycle 5 Steps

A
  1. Systems Strategy- Understand Needs
  2. Project Initiation - Proposals Assessed
  3. In House - chosen for unique needs
  4. Commercial - for common needs
  5. Maintenance and support
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
2
Q

Testing

Combinatorial Testing

A

Identifies Minimal number of tests to get coverage Developers want

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
3
Q

Testing

Static Testing

A

Examines: Programs Code and Documentation thru Review. Not actually executing the program

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
4
Q

Testing

Dynamic Testing

A

Executing the Code with Test Cases

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
5
Q

Testing

White Box Testing

A

Testing INTERNAL workings, not functionality of end user

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
6
Q

Testing

Black-Box Testing

A

How well the software Works. Tests FUNCTIONALITY without knowledge of Code

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
7
Q

Testing

Gray Box Testing

A

Both: knowledge of internal structure while testing functionality

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
8
Q

Four Levels of Tests

A

Unit Testing: functionality of specific section of code.

Integration Testing: Verify interfaces between Components. Expose defects in interfaces/Interactions of modules

System Testing: End to End. the completely integrated system meets requirements

Acceptance Testing: Whether meets org’s Needs/ready for Release

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
9
Q

Four Strategies for Conversion

A

Parallel: both new/old run. Safest/Most Expensive

Direct Changeover: shuts old/starts new. Riskiest/Least Expensive

Pilot: One branch/division at a time

Phased: One function at a time (A/P, A/R)

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
10
Q

Sandbox

A

When a program is to be changed, copy is saved to test area. Programmer makes change to this copy

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
11
Q

RPA Robotic Process Automation

A

Machine Learning Technology that acquire knowledge and mimic people tasks.

Benefits: perform continuously (no time off)
Eliminate human error

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
12
Q

AI

Types of Artificial Intelligence

A

Neural Networks: Processing Elements working together to mimic human brain including learning from previous

Case Based Reasoning: Learn from previous

Rule Based: set of rules to arrive at answer

Intelligent Agents: Apply knowledge base to execute specific, repetitive task

Expert System: attempts to imitate human brain, unstructured problems

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
13
Q

Benefits of AI to Accounting

A
  1. Automate Data Entry
  2. Reduce fraud by viewing vast amounts of documents
  3. Strengthen Expenditure Disbursements by viewing ENTIRE POPULATION of documents.
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
14
Q

Categories of Cloud Services

A

IASS Infrastructure as A Service

PASS Platform as a Service - provides software and hardware tools (typically for application development)

SAAS Software as a Service

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
15
Q

Business Intelligence

A

Collectin of applications, tools, best practices that

  • transform data into actionable info for
    a. managerial control
    b. strategic planning
    c. making better decisions
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
16
Q

Data Analytics

A
  • Qualitative and Quantitative Methods to retrieve data

- identify deviations, opportunities

17
Q

Data Analytics

5 stages

A
  1. Questions Defined
  2. Relevant Data obtained (Info Discovery)
  3. Clean Data- flushing out useless, ID missing
  4. Analyze Data
  5. Communicate Results
18
Q

Data Analytics
Types

DDPP ANT

A

Descriptive - most basic. reports actual Results

Diagnostic - provides insight into REASONS RESULTS OCCURRED.

Predictive - apply assumptions/predict future results

Prescriptive - what needs to be done to make predictive happen

Anomaly - detect unusual Patterns

Network Analysis - analyze network data for patterns

Text Analysis - Text mining to find patterns IN UNSTRUCTURED TEXT

19
Q

DATA MINING

Search for unexpected Relationship

A
  1. Anomalies detected
  2. Finding Relationships between variables
  3. Classifying Data
  4. Regression Analysis peformed
  5. Summarize Data
20
Q

Regression Analysis

Goodness of Fit

A

Whether sample is representative of POPULATION

  • Confidence Level - % of times sample expected to be representative of population
  • Confidence Interval - for given confidence level, range around sample expected to contain TRUE POPULATION
21
Q

SENSITIVITY ANALYSIS

A
  • Trial and Error to determine changes of variables or assumptions on final result
22
Q

Risk

Simulation

A

Computer used to generate many results based on various assumptions

23
Q

Risk

Monte Carlo

A

Randomly selecting values based on probability distribution

24
Q

Risk

Delphi Approach

A

opinions from experts
Summarize Results
Back to experts
Repeat until opinions converge

25
Q

Time Series Analysis (Trend Analysis)

A

Predict future trends based on PAST EXPERIENCES

Seasonal Pattern- within known periods repeats

Cyclical: rise/fall not on a fixed patterns
Usually at least a couple of years

26
Q

EDA Exploratory Data Analysis

A

Encourage data to reveal itself rather than prematurely applying a hypothesis or statistical method

27
Q

What-If Analysis (Goal Seeking)

A

determining outcome thru changed scenarios.

Goal seeking - when an outcome is wanted and needs to determine how it can be achieved

28
Q

Big Data

A
  • voluminous amount of Structured, Semi-structured or unstructered data

characterized by 4 V’s

  • Volume (large amount of data)
  • Variety - wide variety of File Types
  • Velocity - Speed at which it must be analyzed
  • Veracity - Trustworthiness of data

5th V - Value : only as valuable as biz outcomes made possible

Volume Based Value - More data biz have on Customers, greater insights

Variety Based Value -

Velocity Based Value - faster data processed, more time will have to ask questions and seek answers

29
Q

Key Technologies

A

Data Management: Data needs to be high quality to analyze

Data Mining: examining large amounts of data to discover patterns

Hadoop: Open sournce framwork that stores large amts of data

In-Memory Analytics: Delivers IMMEDIATE results by removing data preoparation

Predictive Analytics:

Text Mining: Analyzes data from web, comment fields, other text based to identify new topics and relationships

30
Q

Limitations of Big Data

A

User-Level results are INCOMPLETE: limited to data available from org’s website for example

Answer to ‘WHY?” difficult

Data subject to “noise” > useless info

user level data requires INTERPRETATION prior to use

31
Q

Systems Analysis

A

Learning how current system functions, Determining user needs. Developing logical Requirements of proposed system

32
Q

Feasibility Study

A

Technical, operational and Economic feasibility

33
Q

Systems Design

A

Developing Specifications for

  • Input/Output
  • Processing
  • Internal Controls
  • Programs/Procedures

CMA1 (2 decks)

Brainscape helps you reach your goals faster, through stronger study habits.
© 2024 Bold Learning Solutions. Terms and Conditions