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UoP - Advance Software Engineer Certificate > CSS 422 - Software Architecture > Flashcards

CSS 422 - Software Architecture Flashcards

1
Q

Software Architecture is

A

description of the subsystems or components of a software system and the relationships between them

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2
Q

A more formal description of Software Architecture is

A

Architecture is the fundamental organization of a system embodied in its components, their relationships to each other, and to the environment, and the principles guiding its design and evolution

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3
Q

What four common, recurring themes make up software architecture?

A

System
Structure
Environment
Stakeholder

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4
Q

System

A

A collection of componenets organized in specific ways to achieve specific functionality

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5
Q

Environment

A

A context or circumstance in which a software system is built and has a direct influence on its architecture

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6
Q

Structure

A

A set of elements that are grouped or organized together according to a guiding rule or principle

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7
Q

Stakeholder

A

A person or group of persons who has an interest or concern in a system and its success

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8
Q

An Architecture of a system is best represented as

A

structural details of the system.

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9
Q

Deployment architecture is strongly connect to the quality attributes of

A

scalabililty, performance, security, and interoperability

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10
Q

An architecture

A

Defines a structure
Picks a core set of elements
Caputures early design decisions
Manages stakeholder requiremenets
Influences the organizational structure
Is influenced by its environment
Documents the system
Conforms to a pattern

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11
Q

Architectural patterns

A

certain patterns and sets of styles for system architecture that have had success in practice

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12
Q

Examples of architectural patterns

A

client-server
pipes
filters
data-based architectures

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13
Q

Why create a formal Architecture

A

Every system already has an architecture, whether formal or not, so you can’t have a system without also having an architecture

documenting the architecture allows it to be shared

Makes changes and iterative development possible

Makes the system extensible and modifiable

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14
Q

Technical architect

A

Technical architect is concerned with the core technology used in an organization

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15
Q

Security architect

A

a Technical architect that tunes the security strategy used in applications to fit the organizations information security goals

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16
Q

Information architect

A

A technical architect that comes up with the architecture to make information available to/from applications in a way tha tfacilitates the organizations business goals

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17
Q

System architect

A

is worried about how the core system architecture maps to the software and hardware architecture and the various details of human interatctions with the components in the system

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18
Q

Enterprise architect

A

is concerned with how the different elements in an organization and their interplay is tuend twoards achieveing the goals of the organization in an efficient manner.

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19
Q

Software design

A

A blueprint of the details at the implementation level of the various subsystems and components that make up those subsystems

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20
Q

Modifiability

A

a software quality attribute

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21
Q

quality attribute

A

A measurable and testable property of a system which can be used to evaluate the performance of a system within its prescribed environment with respect to its nonfunctional aspects

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22
Q

nonfunctional requirements

A

global constraints that describe how a system should operate.

ex: things like speed, reliability, data integrity, etc.

The opposite of functional requirements like.

like: this needs to be able to access the database and pull back information on billing accounts

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23
Q

quality attributes

A

modifiability
testability
scalability
availability
security
deployability

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24
Q

modifiability

A

the ease with which changes can be made to a system and the flexibility with which the system adapts to such changes

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25
Q

testability

A

how much a software system is amenable to demonstrating its faults through testing

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26
Q

scalability

A

a system’s capacity to accommodate increasing workload on demand

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27
Q

performance

A

the amount of work accompmliished by a system using a given unit of computing resource. higher the work/unit ratio, the higher the performance

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28
Q

availability

A

the property of readiness of a software system to carry out its operations when the need arises

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29
Q

security

A

the degree of ability of a system to avoid damage to its data and logic from unauthenticated access

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30
Q

deployability

A

the degree of ease with which software can be taken from the development to the production environment

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31
Q

coupling

A

the degree of interdependence between software modules

high coupling means that the modules are closely connected and changes in one module may affect other modules

it can also be defined as the number of dependencies that modules share between them

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32
Q

cohesion

A

the degree to which elements within a module work teogether to fulfill a single, well-defined process

the degree to which the elements belong together

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33
Q

the aspects of modifiability

A

readability
modularity
reusability
maintainability

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34
Q

readability

A

the ease with which a program’s logic can be followed and understood

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35
Q

modularity

A

the software is written in well-encapsulated modules which do very specific, well documented functions

36
Q

reusability

A

the number of parts of a system (including tools, designs, code, and others) that can be reused in other parts of the system

37
Q

maintainability

A

the ease and efficiency with which the system can be updated and kept working in a useful state by its stakeholders

38
Q

aspects of readability

A

well written - uses simple syntax and well known features. logic is clear and concise. module and variable names are meaningful

well documented - good comments (inline). also, represenets external documents for configuration or api usage

well formatted - follows style guidelines

39
Q

“Pythonic” =

A

keeping up with th eZen of Python

40
Q

antipatterns

A

coding practices that contribute to unreadable code

41
Q

list the antipatterns

A

code with little or no comments

code that breaks best practices for the language

programming antipatterns:
– spaghetti code
– big ball of mud
– copy-paste programming
– ego programming

python antipatterns
– mixed indentation
– mixed string literal types
–overuse of functional constructs (map(), reduce(), filter(), lambda(), etc)

42
Q

Ways to document code

A

Inline documentation
* comments
* function documentation
* module documentation

Extermal documentation
Readme, INSTALL, CHANGELOG

User manuals
* formal documents created bya adedicated person or team

43
Q

Ways to document in Python

A

Inline comments

Function docstring (with notes about the parameters): @params, @returns

Class docstrong

Module docstring

44
Q

PEP 8

A

Python Enhancement Proposal

A coding and style guide for Python

45
Q

cohesion

A

how much the code within a module is aligned; i.e., a measure of how well the code is about the same thing

46
Q

coupling

A

a measure of how much a module’s code relies on code from another module

47
Q

Interface

A

the modules functions classes or methods that provide a connection / interface to the module

Can be thought of as the API of the module

48
Q

python module variables that are not part of the module interface

A

If the variables are not meant to be accessed by an external program, they should be preceded by a double underscore: ___

49
Q

abstraction

A

Abstraction is used to hide the internal functionality of the function from the users
User is familiar with that “what function does” but they don’t know “how it does.”

so the
class Square():

can let you instantiate a square = Square()
object without having to understand how to draw it, or without having to tell the computer how to draw it for each step. The square class does it for you.

The square class has abstracted away the details

50
Q

abstract common services

A

so if module B uses services from module A, then those services are abstracted services from module A

Because Module B can’t see how those modules are built – it only knows how to use those services

51
Q

Using inheritance techniques

A

another strategy for modifiability

If there is code that is similar, it might be easier to user inheritance to pass along the common functions instead of keeping similar functions hard-coded into each successive class

super() method in Python

52
Q

Using Late Binding Techniques

A

another strategy for modifiability

plugin mechanisms
broker/registry lookup services
notification services
deployment time binding
using creational patterns

53
Q

Unit testing

A

the most fundamental type of testing performed by developers.

A unit test applies the most asic unit of software code - typically, functions or class methods - by using executable assertions which check the output of ht eunit being tested against an expected outcome

54
Q

python unit testing software

A

py.test - a full-featured, mature testing framework

nose2 - a third party unit-testing module for python

unittest - a unit testing module in the standard python library

55
Q

unittest module provides these object

A

Test cases
test fixtures
test suites
test runners
test results

56
Q

pytest

A

will automatically find tests with any function prefixed with the word “test_”

57
Q

nose 2

A

a third-party unit-testing module available for python

58
Q

code coverage

A

the degree to which the source code under test is covered by a specific test suite.

ideally test suites should aim for higher code coverage, as this would expose a larger percentage of the source code to tests and help to uncover bugs

59
Q

Lines of Code (LOC)

A

a percentage of the subroutines (functions) covered by a test suit

60
Q

how are code coverage metrics reported?

A

by lines of code (LOC)

61
Q

doctests

A

Inline tests in a function , class or module documentation which add a lot of value by combining code and tests in one place without having to develop or maintain separate test suites

62
Q

silent testing

A

you can’t see the details of the test, you only learn whether all of the tests passed - no output is produced

63
Q

white box testing

A

testing that involves knowing the exact way something is coded.

usually done by the develpers. i.e., they know exactly how something is coded - they have access to the source code, so they know what the code should do, and can find out immediately why it has broken

64
Q

Examples of Code Documentation

A

Inline documentation

External documentation

user Manuals

65
Q

Scale vertically or scale up

A

When the system scales by either adding or making better use of resources inside a computer node (such as CUP or RAM)

66
Q

Scale horizontally or scale out

A

when a system scales by adding more compute nodes to it

67
Q

Scalability

A

The degree to which a software system is able to scale when compute resources are added

68
Q

Concurrency

A

The amount of work that ets done simultaneously in a system

69
Q

Performance of a system

A

a function of its software and of its hardware capabilities

70
Q

performance

A

the degree to which a system is able to meet its throughput and/or latency requirements in terms of the number of transactions per second is known

71
Q

Software Performance Engineering

A

develops performance models early in the SDLC and uses results from those models to modify the software design and architecture to meet performance requirements in multiple iterations

72
Q

Performance Engineering Lifecycle

A

performance is a non-functional requirement

the PEL parallels the steps in the SDLC with every step the performance is improved

73
Q

Stress testing tools

A

these tools are used to supply workload to the system under test - simulating peak worloads in and burst loads (of very high traffic)

74
Q

Load generators

A

Stress testing tools

75
Q

Stress testing tools examples

A

httpperf
ApacheBench
Loadrunner
apache Jemeter
Locust

76
Q

Monitoring tools

A

these tools work with the application code to generate performance metrics such as the time and memory taken for functions to execute, number of functioncalls made per request-response loop and the average and peak times spent on each function

77
Q

Instrumentation tools

A

trace metrics and track events such as exceptions

track details as the line number where the exception occurred and the timestap, and environment fo the appplication

78
Q

code or application profiling tools

A

these tools generate statistics about functions, frequency of duration of calls, time spent on each function call

this allows the developer to find critical sections of code where the most time is spent and allowing them to optimize those sectionsP

79
Q

Python standard library instrumentation and profiling modules

A

profile
cProfile

80
Q

Performance complexity of code

A

How a routine or function responds to input size typically in terms of the time spent in executing the code

81
Q

Big O Notation AKA:

A

Backhmann-landau notation
Asymptomatic notation

82
Q

the letter O

A

the rate of growth of a function with respect to input size – also called th eorder of the function

83
Q

real time

A

the actual wall-clock time that elapsed for an operation

84
Q

user

A

the amount of actual CPU time spent within the process in user mode

85
Q

Sys

A

the amount of CPU time spent executing system calls within the kernel for the program

86
Q

Total CPU time =

A

user + sys time

87
Q

amortized analysis of algorithms

A

takes into account both the lower and upper end of the time taken for executing algorithms and gives the programmer a realistic estimate of the average time spent

UoP - Advance Software Engineer Certificate (4 decks)

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