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

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Tags: computer tags min. Computer Science Overview perception Usability xxxxx Equal

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

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