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A Dynamic Evolutionary Optimization Method for Software Architecture

A technology of software architecture and dynamic evolution, applied in the computer field, can solve problems such as easy to fall into the local optimal solution, large number of software component groups, etc.

Inactive Publication Date: 2019-08-20
EAST CHINA UNIV OF TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] The complexity of modern software determines the large number of software component groups. When performing dynamic evolution of the software architecture, the traditional optimization algorithm iteratively seeks the optimal solution from a single initial value, which is easy to fall into the local optimal solution by mistake, and cannot be completed quickly and efficiently. global merit

Method used

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  • A Dynamic Evolutionary Optimization Method for Software Architecture
  • A Dynamic Evolutionary Optimization Method for Software Architecture
  • A Dynamic Evolutionary Optimization Method for Software Architecture

Examples

Experimental program
Comparison scheme
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Embodiment 1

[0051] Embodiment 1: as figure 1 As shown, the dynamic evolution optimization method of software architecture includes the following steps:

[0052] Step 100: Corresponding the software architecture to the chromosome, the components of the software architecture corresponding to the genes of the chromosome, and performing dynamic evolution coding on the original software architecture, the components of the original software architecture are the original components;

[0053] Step 101: Initialize the original software architecture, and randomly generate initial components corresponding to each original component;

[0054] Step 102: Construct a fitness function and calculate the fitness value of each initial component;

[0055] Step 103: Judging whether the condition is met: the fitness value of the initial component is greater than or equal to a set threshold, and the set threshold is set in advance;

[0056] Step 104: If not, perform crossover and mutation operations on the in...

Embodiment 2

[0108] Embodiment 2: as Figure 4 As shown, a smart home system 1 includes five subsystems, namely smoke detection 11, temperature adjustment 12, gas detection 13, automatic curtain 14 and humidity adjustment 15, each subsystem includes three functional components and a data component, wherein , the functional components use C f (i) indicates that the data component uses C d (i) said. The system is to timely and accurately alarm and deal with security incidents, so as to ensure the stability, reliability and real-time performance of the system. The system contains a total of 15 functional components C f (i) and 5 data components C d (j), where i∈(0,15], j∈(0,5], their specific parameter data are shown in Table 2 and Table 3 below:

[0109] Table 2 Functional component quality indicators of initial software architecture

[0110]

[0111]

[0112] Table 3 Data component quality indicators of initial software architecture

[0113]

[0114] Assuming that these subs...

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Abstract

The invention discloses a dynamic evolution and optimization method of a software systematic architecture. The dynamic evolution and optimization method comprises the steps of using an original software systematic architecture to conduct dynamic evolution coding, and defining original components constituting the original software systematic architecture to be Gi={ni, pi, fi, odi, idi, noi, Ii}; conducting initialization on the original software systematic architecture and randomly generating initial components; constructing a fitness function and calculating fitness values of all the initial components; selecting the initial components of which the fitness values are larger than or equal to a preset threshold value as target components, and conducting interlace operation and mutation operation on initial components corresponding to the fitness values smaller than or equal to the preset threshold value so as to renew initial components; generating a target software systematic architecture according to all the target components. According to the dynamic evolution and optimization method of the software systematic architecture, by conducting operating selection from the aspect of optimizing search through selecting operation, the components are optimized and evolved from one generation to one generation, and the components are closer and closer to an optimum component, and thus it is guaranteed that the software systematic architecture can quickly and efficiently complete overall situation preferential evolution.

Description

technical field [0001] The invention relates to the computer field, in particular to a method for dynamic evolution optimization of software architecture. Background technique [0002] With the continuous development of computer technology, software user needs and computing environments are also constantly changing. When faced with these changing needs and environments, software often needs to evolve continuously to enhance its vitality and survive the fittest. Software evolution describes the ability of a software system to adapt to future changes, and has become an important part of the current software life cycle. Software evolution can be divided into static evolution and dynamic evolution. Software that supports dynamic evolution can change the implementation of the system at runtime, including improving the function of the system, expanding it, changing the architecture, etc., without restarting or recompiling the system. Due to the advantages of continuous availabil...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F8/40G06N3/12
Inventor 徐洪珍付亮章鹤松刘自强王强
Owner EAST CHINA UNIV OF TECH