20172304 2018-2019 "Java software architecture and data Structure" chapter I chapter II study summary

20172304 2018-2019 "Java software architecture and data Structure" chapter I chapter II Summary of learning contents

• Chapter I. Overview
  1.1 Software Quality:
  Several features of high-quality software:

  Correctness: To what extent the software meets its specific needs
  Reliability: Frequency and degree of risk of software failures
  Robustness: The extent to which an error can be handled appropriately
  Usability: How easy it is for users to learn and perform tasks
  Maintainability: How easy it is to make changes to the software
  Reusability: The ease with which software components can be reused for the development of other software systems
  Portability: How easy it is for software components to be used in multiple computer environments
  Operational efficiency: the degree to which the software accomplishes its objectives without wasting resources
  1.2 Data structures (how computers store and organize data)
  program = data structure + algorithm
  Software = Program Plus software engineering

• Chapter Two algorithm analysis
  2.1 Algorithm Efficiency analysis
  Algorithmic efficiency is typically expressed by CPU usage time
  Algorithm analysis is the analysis of the algorithm from the angle of efficiency
  Algorithmic analysis is the foundation of computer Science
  2.2 Growth function and Big O notation
  Growth function: Represents the relationship between the size of the problem (n) and the value we want to optimize the function represents the time complexity of the algorithm (CPU usage time) and space complexity (memory space)
  The asymptotic complexity of the algorithm (the general nature of the growth function as n grows)
  Focus on the main item of the function (that is, the fastest growing one)
  Progressive complexity is called the Order of the algorithm
  Comparison between the items of the growth function

the number of plates	15N2	15n	15n2+45n
1	15	45	60
2	60	90	150
5	375	225	600
10	1500	450	1950
100	150 000	4500	154500
1000	15 000 000	45000	15045000
10 000	1 500 000) 000 000	450 000	1 5000 450 000
100 000	150 000 000 000	4 500 000	150 004 500 000
1 000 000	15 000 000) 000 000	15 000 000	15 000 045) 000 000
10 000 000	1 500 000 000 000 000	450 000 000	1 500 000 450 000 000

Some growth functions and their asymptotic complexity
| Growth Function | order | mark
|t (n) =17| O (1) | Constant Type |
|t (n) =3log n| O (log n) |
|t (n) =20n-4| O (n) | linear |
|t (n) =12n log n+100n| O (n log n) |n log n|
|t (n) =3n2+5n-2| Square type |
|t (n) = 8n^3+3n2| O (n^3) | cubic type |
|t (n) =2^n+18n2+3n| O (2^n) | O (2^n) | index type |
If two algorithms complete the same task with different growth functions, such as O (N2), then the two are largely equal in terms of operational efficiency.

2.3 Comparison of growth functions

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20172304 2018-2019 "Java software architecture and data Structure" chapter I chapter II study summary