A software class importance measurement method based on class multilayer network

A multi-layer network and measurement method technology, applied in the field of software importance measurement, can solve the problems of lack of software measurement, inaccurate software structure model, inaccurate software structure description, etc., to achieve the effect of improving code maintenance efficiency

Active Publication Date: 2019-03-08
ZHEJIANG GONGSHANG UNIVERSITY
View PDF4 Cites 2 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] (1) Existing work mainly focuses on measuring the complexity of the code elements themselves, and lacks the measurement of the importance of code elements
[0006] (2) Existing work is mainly aimed at element-level measurement, which often measures local features of software, such as measuring a method or a class. There is a lack of work on software measurement from an overall perspective, let alone measuring the importance of software elements from an overall perspective sex work
[0007] (3) The software structure model built by the existing work is not accurate enough, ignoring the multi-layered nature of the software structure, for example: there are multiple relationships between classes, and the existing work regards these multiple relationships as the same relationship, Resulting in an inaccurate description of the software structure

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • A software class importance measurement method based on class multilayer network
  • A software class importance measurement method based on class multilayer network
  • A software class importance measurement method based on class multilayer network

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0041] Below by embodiment and in conjunction with accompanying drawing, technical scheme of the present invention will be further described:

[0042] A kind of software class importance measurement method based on class multi-layer network that the present invention proposes, concrete steps are as follows:

[0043] (1) the source code of Java software is abstracted into class multi-layer network MCN={G at class granularity IN ,G IM ,G PA ,G GL ,G ME ,G LO ,G RE}. Among them, G i =(V,L i ,P i ) is a single-layer network in the class multi-layer network, corresponding to a certain interaction relationship i∈{IN,IM,PA,GL,ME,LO,RE} between classes; V is G i A node set representing all classes in the source code; L i is G i The undirected edge set of represents the dependency between classes; P i is a matrix of |V|×|V| (|V| returns the number of nodes in V), representing G i The weight matrix of the strength and weakness of the dependencies between classes in the lay...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention discloses a software class importance measurement method based on a class multi-layer network, comprising the following steps of abstracting the source code of a Java software into a class multi-layer network at a class granularity; calculating a weighted h-index of a class node in each layer of a class multilayer network; weighting the weighted h-exponent of each class in each layerby a statistical average method (expert scoring method), and then fusing the weighted h-exponent of each class in each layer into a global weighted h-exponent by linear weighting, and using the global weighted h-exponent of nodes as a measure of class importance. The present method basically ignores the multi-layer nature of the class granularity network, and the present invention makes up for the deficiency of the prior method, and introduces the class multi-layer network into the measurement of class importance for the first time, which is of great significance for more accurate understanding of the software structure and improving the efficiency of code maintenance.

Description

technical field [0001] The invention relates to a method for measuring the importance of a software class, in particular to a method for measuring the importance of a software class based on a class multi-layer network. Background technique [0002] Software has been closely connected with our lives, and has penetrated into all walks of life. Online shopping for clothes, ordering meals, entering the house, and taking the subway are all inseparable from software. Software is changing and will continue to change our lives. With the continuous development of software technology and the increasing complexity of people, the complexity of software is also increasing, which brings many difficulties to software development. At the same time, software is developed through a high degree of collaboration, which leads to the quality of software cannot be guaranteed. [0003] Evolution is one of the essential attributes of software. It must, like the creatures in nature, adapt to the e...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G06F8/75
CPCG06F8/75
Inventor 潘伟丰王家乐蒋海波姜波柴春来明华
Owner ZHEJIANG GONGSHANG UNIVERSITY
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products