CN114510407A - Open source software ecosystem index calculation method - Google Patents
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Abstract
A method for calculating indexes of an open source software ecosystem comprises the steps of crawling and analyzing version information, community information and author information of open source software projects to obtain basic data of open source software; calculating to obtain each index score of the open source software ecosystem by a linear regression mode; and obtaining the weight of each index by adopting a DEMATEL-ANP comprehensive analysis method, and obtaining the total evaluation index by weighting. The invention combines a comprehensive analysis method and a regression analysis method, obtains the basic information of the open-source software through the crawler, realizes subjective and objective combination, and effectively evaluates the quality of the open-source software ecosystem.
Description
Technical Field
The invention relates to a technology in the field of information control, in particular to an index calculation method for an open source software ecosystem.
Background
With the development of the internet, open source software is gradually becoming a way for many software companies to produce software. The open source software ecosystem comprises: the resulting interrelationship between the software vendor and the platform owner, as well as other related roles. The open source software ecosystem comes up when the platform owner decides to allow developers outside the organizational boundary to use their platform. In general, the motivation for platform owners to open their platforms is that relying on the manpower and material resources inside the company cannot meet the needs of all users, and the "winner takes all" principle drives the company to attract more customers, thus requiring a large number of external developers to participate in the development of common platform functions. For external developers, the interest of the market has become a reason to attract them to participate in the evolution of open source software ecosystems.
The existing open source software ecosystem lacks the evaluation of other elements of the software. With the development of open source software, the relationship between the software and the relevant external environment factors becomes more and more complex. The invention aims at the interaction relationship among various elements such as software, a platform, an internal developer and an external developer in a software ecosystem, improves the technical problem that only the quality of the software is concerned and other elements are ignored in the traditional software quality model, and constructs an open source software ecosystem index model. In addition, the invention creatively combines the comprehensive analysis method with the machine learning mode, and the software ecosystem index model carries out qualitative and quantitative evaluation.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides an open source software ecosystem index calculation method, which combines a comprehensive analysis method and a regression analysis method, obtains basic information of open source software through a crawler, achieves subjective and objective combination and effectively evaluates the quality of the open source software ecosystem.
The invention is realized by the following technical scheme:
the basic data of the open source software is obtained by crawling and analyzing the version information, the community information and the author information of the open source software project; calculating to obtain each index score of the open source software ecosystem by a linear regression mode; and obtaining the weight of each index by adopting a DEMATEL-ANP comprehensive analysis method, and obtaining the overall evaluation index by weighting.
Crawling and analyzing are realized by formulating a crawler scheme, and specifically the method comprises the following steps: and acquiring the open source software project website data by using a distributed crawler system, extracting the structured information from the HTML webpage by using a wrapper, reducing the structured information into structured data and storing the structured data into a database.
The distributed crawler system comprises: the system comprises a distributed cloud acquisition platform supporting acquisition with high expansibility and rapid deployment and a high available network agent system.
The wrapper means: the tool for reading and converting data on the webpage by using the access interface is generated by adopting an inductive method, and the tool learns the rule of information extraction from the labeled training sample set based on a manual method and then extracts data of other webpages with the same template.
The linear regression mode is as follows: index mnScore function
Wherein: the number of indexes is k, the nth index is mnIndex mnRelevant data is X'nLet Xn＝(X'n1), actual score YnIs obtained by solving by using a least square method
Solving by using a random gradient descent method, stopping training when the maximum training times is set or the error is smaller than a certain threshold value, and when the ith sample is selected, performing gradient descent according to the formula
The overall evaluation index is specifically as follows: activity index, diversity index, regulation and control index, sustainability index, wherein: activity indicators include, but are not limited to: the communication and conversion functions between the inside and the outside of the software ecosystem comprise system activity, community activity, code life cycle, developers, attention and company conditions participating in software development; diversity indicators include, but are not limited to: the role diversity index and the project diversity index in the software ecosystem comprise a role diversity index and a system attribute diversity index; regulatory indicators include, but are not limited to: the software ecosystem has the resistance when being interfered and the recovery capability after being interfered, wherein the resistance when being interfered and the recovery capability when being interfered are included; sustainability indicators include, but are not limited to: the software ecosystem provides users with the ability to continue to provide value, requires that the ecosystem be able to continue to maintain users and developers on a certain scale, and ensures a certain market share, including functionality, performance, security, extensibility, reliability, availability, document quality, collaborative quality, operation and maintenance services, standardization, and compatibility.
The DEMATEL-ANP comprehensive analysis method comprises the following steps: the method comprises the following steps of adopting an ANP model to utilize pairwise comparison of all indexes of a software ecosystem, adopting a DEMATEL graph theory to analyze the mutual influence between the indexes of the software ecosystem, and adopting DEMATEL + ANP to evaluate the index weight according to an open source software ecosystem index model, wherein the method specifically comprises the following steps:
dividing an open source software ecosystem index model into a target layer, a control layer and a network layer;
constructing a super matrix with control layer element of p1,p2,…pmTarget layer element is t1,t2,…tn. Fixed control layer element pk(k∈[1,m]) To p belongs tokComparing every two network layer elements to obtain pkJudgment matrix A under the criterion2k. The elements in the matrix satisfy
aijThe larger the value, the larger the influence of the element i on the element fixed by the control layer than the element j. Calculation of A2kTo obtain pkWeight vector w under criterion2k. Obtaining a judgment matrix A of a control layer under the target in the same way1kAnd a weight vector w1. Constructing a weighted hypermatrix of a network layer below a control layer
Constructing a DEMATEL model, wherein the vertex of a directed graph represents a specific index, a directed edge represents the index to influence the index, and the weight of the edge represents the influence degree of the index to the index;
step four, constructing the direct influence matrix Z, Z ═ Zij)m×m，zijAnd the weight of the directed edge in the relation graph is represented, wherein the weight is the influence degree of the index i on the index j. Provision of zij∈{0,1,2,3}，zijThe larger the value is, the larger the influence degree of the index i on the index j is, and the diagonal element is defined as 0;
step five, constructing a normalized direct relation matrix
Step (I-X) of constructing comprehensive influence matrix T ═ X-1Wherein: the comprehensive influence matrix of the first-layer indexes (diversity index, activity index, sustainability and regulation index) is recorded as T1The composite influence matrix of the second layer index is denoted as T2；
Step (c) constructing a super matrix S ═ S based on a network structure using the results obtained by DEMATEL and ANPij)(m+n+1)×(m+n+1)Wherein: w is a1And W2Is a weight vector and a weight matrix between layers calculated according to ANP, T1And T2For the total relation matrix calculated according to DEMATAL
Step eight, calculating a weighted supermtrix: will T1And T2Each element in (1) is respectively related to w1And W2Multiplying the unique elements of the same row but different from 0 to obtain T'1And T'2. Finally, mixing T'2Index of (1) is T'1Multiplying the elements of the middle membership to obtain a weighted super matrix H (H) of all indexesi)n×n；
Ninthly, determining weight: multiplying the weighted super matrix H by multiple times, normalizing the column vector of the matrix before each multiplication to obtain stable additionPost-computation of weights for weight-over-matrix
The invention relates to a system for realizing the method, which comprises the following steps: a crawler unit, a linear regression unit, and a DEMATEL-ANP unit, wherein: the crawler unit is connected with the linear regression unit and transmits basic data of the software project, the linear regression unit is connected with the DEMATEL-ANP unit and transmits index scores, and finally index weighting summation is carried out through the DEMATEL-ANP unit to obtain a comprehensive index result.
Technical effects
The invention integrally solves the technical problems that the prior art can not integrate the environment elements related to the software into the software quality model and evaluate the quality of the open-source software from the perspective of the whole ecosystem where the software is located.
Compared with the current software quality model which only concerns the quality of the software, the method provided by the invention carries out modeling on the open-source software ecosystem according to the community environment of the open-source software, internal and external developers and other elements. Through analyzing resource elements of various environments and developers, the mutual connection between the roles of an open source software ecosystem software provider and a platform owner is researched. When the quality evaluation method is used for evaluating the quality of the open-source software project, the quality evaluation method not only comprises elements in the traditional software quality model, but also comprises a plurality of elements in a software ecosystem. In addition, the method is based on index weight obtained by a DEMATEL-ANP comprehensive evaluation method marked by experts and index score obtained by a data-based regression analysis method, and the open source software ecosystem is evaluated by combining subjectivity and objectivity.
Drawings
FIG. 1 is a flow chart of the present invention.
FIG. 2 is an architecture diagram of an exemplary open source software ecosystem index model;
FIG. 3 is a diagram illustrating the achievement of an index score according to an embodiment.
Detailed Description
As shown in fig. 1, the present embodiment relates to a method for calculating an index of an open source software ecosystem, which includes the following steps:
s101, acquiring basic information of an open source software project, specifically comprising: extracting an open source item list through an API provided by a GitHub by adopting a Scapy crawler frame; manually summarizing and summarizing the template rule of the extractor according to the html webpage structure of the GitHub; the project information needing to be crawled comprises version submission information, community information, developer information and the like; acquiring data related to open source projects by using Beautiful Soup parsing html webpage content provided by Python, wherein in the embodiment, the data includes a version release period, participant code contribution amount, commit number, branch number, PullRequest number, Watch number, Star number, Fork number, development language number, online time, certificate type, developer number, developer project number, code line number, Issue number and Issue feedback period; storing the crawling result of the crawler in a local MySQL database;
s102, training a regression model according to basic information of an open source software project to obtain scores of all indexes, and specifically comprising the following steps: preprocessing data crawled by an open source software project by means of a numpy library and a pandas library provided by Python to serve as input data, constructing a part of index scores by a manual labeling method to serve as labels, and constructing a training set and a test set by the input data and the labels; as shown in fig. 3, the data corresponding to each index is input to the linear regression model to obtain the score of each index, the Python provides the linear regression model of the skleann library, the number of the defined indexes is k, and the nth index is mnIndex mnRelevant data is X'nLet Xn＝(X'n1), defining the actual score as YnThen index mnScore function F (X)n) Comprises the following steps:
solved by least square method to obtain
S103, index weight calculation is carried out according to the index model of the software ecosystem, and the method specifically comprises the following steps: constructing ANP to draw the overall modelDividing into target (comprehensive index of open source software ecosystem, control layer (including activity index, regulation and control index, diversity index and sustainability index) and network layer (sub-index under control layer), constructing hypermatrix, and controlling layer element being p1,p2,…pmThe target layer element is t1,t2,…tn. Fixed control layer element pkTo p belongs tokComparing every two network layer elements to obtain pkJudgment matrix A under standard rule2k. Calculation of A2kTo obtain pkWeight vector w under criterion2k. Obtaining a judgment matrix A of a control layer under the target in the same way1kAnd a weight vector w1. Constructing a weighted hypermatrix of a network layer below a control layer
Constructing a DEMATEL model, wherein the vertex in the model represents a specific index, and the directed edge represents the influence of the index; constructing a direct influence matrix Z ═ (Z)ij)m×m，zijRepresenting the degree of influence of the index i on the index j; constructing a normalized direct relation matrix
Constructing a comprehensive influence matrix T ═ X (I-X)-1Wherein: the comprehensive influence matrix of the first-layer indexes (diversity index, activity index, sustainability, regulation index) is recorded as T1The comprehensive influence matrix of the second layer index is recorded as T2(ii) a Constructing a network structure-based super matrix S ═ (S) by using results obtained by DEMATEL and ANPij)(m+n+1)×(m+n+1)Wherein: w is a1And W2Is a weight vector and a weight matrix between layers calculated according to ANP, T1And T2For the total relation matrix calculated according to DEMATAL
A weighted super matrix is calculated. Will T1And T2Each element in (1) is respectively related to w1And W2Multiplying the only elements in the same row which are not 0 to obtain T'1And T'2. Finally, mixing T'2Index of (1) is T'1Multiplying the elements of the middle membership to obtain a weighted super matrix H (H) of all indexesi)n×n(ii) a And determining the weight. And multiplying the weighted super matrix H by itself for multiple times to obtain a stable weighted super matrix. Calculating weights
And S104, weighting and summing according to each index score of the open source software project and the index weight of the software ecosystem to obtain a comprehensive index.
Considering that the application scenario of the present embodiment mainly aims at an open source software ecosystem, the present application is studied on an open source project on a GitHub, and specifically includes: and performing weighted summation on the calculated index score of the software ecosystem and the weight of each index to finally obtain the comprehensive index of the software ecosystem.
The invention creatively combines a comprehensive evaluation method with a machine learning method and introduces a regression analysis model to the index evaluation of the open-source software ecosystem. Further details with significant improvements over existing software quality models are represented in: not only the quality of the software is concerned, but also the overall quality of the software is comprehensively analyzed from the aspects of factors such as an open source software platform community, internal and external developers and the like.
Through a specific practical experiment, the weight of each index is obtained by a DEMATEL-ANP method flow, and the weight is specifically as follows:
step 2, calculating to obtain a weight vector:
w1＝[0.250，0.095，0.095，0.560]T
w21＝[0.160，0.060，0.060，0.160，0.160，0.400]T
w22＝[0.500，0500]T
w23＝[0.500，0500]T
w24＝[0.222，0.095，0.222，0.035，0.095，0.095，0.035，0.035，0.095，0.035，0.035]T。
step 3, constructing a direct influence matrix:
and 4, calculating through a series of matrixes, and obtaining a final weight result as shown in table 1.
The linear regression model method is operated by taking project information crawled up by Github as parameters, obtained regression model index score experimental data are shown in tables 2-5, index weights and index scores are weighted and summed, and finally obtained open-source software ecosystem comprehensive indexes are shown in table 6.
TABLE 2
TABLE 3
TABLE 4
TABLE 5
TABLE 6
| Sample(s) | Activity (0.29) | Diversity (0.1) | Regulation and control property (0.1) | Sustainability (0.51) | |
| 1 | 0.85 | 0.72 | 0.38 | 0.41 | 0.5656 |
| 2 | 0.51 | 0.72 | 0.39 | 0.63 | 0.5802 |
| 3 | 0.58 | 0.44 | 0.94 | 0.81 | 0.7193 |
| 4 | 0.89 | 0.64 | 0.36 | 0.62 | 0.6743 |
| 5 | 0.57 | 0.48 | 0.80 | 0.64 | 0.6197 |
In the prior art, a qualitative mode is adopted for evaluating the open source software ecosystem, and a comprehensive evaluation mode is utilized for evaluating a certain specific open source software ecosystem. The method comprehensively learns the index model of the open source software ecosystem, summarizes and summarizes a set of methods for automatically evaluating the software ecosystem, and directly obtains the score of the open source software ecosystem by regression analysis and a DEMATEL-ANP weight calculation method starting from objective project data.
The foregoing embodiments may be modified in many different ways by those skilled in the art without departing from the spirit and scope of the invention, which is defined by the appended claims and all changes that come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein.
Claims (10)
1. A method for calculating indexes of an open source software ecosystem is characterized in that basic data of open source software is obtained by crawling and analyzing version information, community information and author information of open source software projects; calculating to obtain each index score of the open source software ecosystem by a linear regression mode; obtaining the weight of each index by adopting a DEMATEL-ANP comprehensive analysis method, and obtaining a total evaluation index by weighting;
crawling and analyzing are realized by formulating a crawler scheme, and specifically the method comprises the following steps: acquiring data of a source software project website by using a distributed crawler system, extracting structured information from an HTML webpage by using a wrapper, reducing the structured information into structured data and storing the structured data into a database;
the overall evaluation index is specifically as follows: activity index, diversity index, regulation index, sustainability index.
2. The open source software ecosystem index calculation method according to claim 1, wherein the distributed crawler system comprises: the system comprises a distributed cloud acquisition platform supporting high-expansibility and quick-deployment acquisition and a high-availability network agent system.
3. The open source software ecosystem index calculation method according to claim 1, wherein the wrapper is: the tool for reading and converting data on the webpage by using the access interface is generated by adopting an inductive method, and the tool learns the rule of information extraction from the labeled training sample set based on a manual method and then extracts data of other webpages with the same template.
4. The open source software ecosystem index calculation method according to claim 1, wherein the linear regression mode is: index mnScore function
Wherein: the number of indexes is k, the nth index is mnIndex mnRelevant data is X'nLet Xn＝(X′n1), actual score YnSolved by least square method to obtain
Solving by using a random gradient descent method, stopping training when the maximum training times are set or the error is smaller than a certain threshold value, and selecting the ith training timeAt the time of sampling, the gradient is decreased by the formula
5. The open source software ecosystem index calculation method according to claim 1, wherein the activity index comprises: the functions of communication and conversion between the inside and the outside of the software ecosystem comprise system activity, community activity, code life cycle, developers, attention and company conditions participating in software development.
6. The open source software ecosystem index calculation method according to claim 1, wherein the diversity index comprises: the role diversity index and the project diversity index in the software ecosystem comprise a role diversity index and a system attribute diversity index.
7. The open source software ecosystem index calculation method according to claim 1, wherein the regulation and control index comprises: the software ecosystem has the resistance when being interfered and the recovery capability after being interfered, including the resistance when being interfered and the recovery capability when being interfered.
8. The open source software ecosystem index calculation method according to claim 1, wherein the sustainability index comprises: the software ecosystem provides users with the ability to continue to provide value, requires that the ecosystem be able to continue to maintain users and developers on a certain scale, and ensures a certain market share, including functionality, performance, security, extensibility, reliability, availability, document quality, collaborative quality, operation and maintenance services, standardization, and compatibility.
9. The open source software ecosystem index calculation method according to claim 1, wherein the DEMATEL-ANP comprehensive analysis method is as follows: the method comprises the following steps of adopting an ANP model to utilize pairwise comparison of all indexes of a software ecosystem, adopting a DEMATEL graph theory to analyze the mutual influence between the indexes of the software ecosystem, and adopting DEMATEL + ANP to evaluate the index weight according to an open source software ecosystem index model, wherein the method specifically comprises the following steps:
dividing an open source software ecosystem index model into a target layer, a control layer and a network layer;
constructing a super matrix with control layer element of p1，p2，...pmThe target layer element is t1，t2，...tn(ii) a Fixed control layer element pk(k∈[1，m]) To p belongs tokComparing every two network layer elements to obtain pkJudgment matrix A under the criterion2k(ii) a The elements in the matrix satisfy
aijThe larger the value is, the larger the influence of the element i on the fixed element of the control layer is; calculation of A2kTo obtain pkWeight vector w under criterion2k(ii) a Obtaining a judgment matrix A of the control layer under the target in the same way1kAnd a weight vector w1(ii) a Constructing a weighted hypermatrix of a network layer below a control layer
Constructing a DEMATEL model, wherein the vertex of a directed graph represents a specific index, a directed edge represents the index to influence the index, and the weight of the edge represents the influence degree of the index on the index;
step four, constructing the direct influence matrix Z, Z ═ Zij)m×m，zijRepresenting the influence degree of the index i on the index j, namely the weight of the directed edge in the relational graph; provision of zij∈{0，1，2，3}，zijThe larger the index is, the more the shadow of the index i on the index j isThe larger the loudness is, and the diagonal element is defined as 0;
step five, constructing a normalized direct relation matrix
Step (I-X) of constructing comprehensive influence matrix T ═ X-1Wherein: the comprehensive influence matrix of the first-layer indexes (diversity index, activity index, sustainability and regulation index) is recorded as T1The comprehensive influence matrix of the second layer index is recorded as T2；
Step (c) constructing a super matrix S ═ S based on a network structure using the results obtained by DEMATEL and ANPij)(m+n+1)×(m+n+1)Wherein: w is a1And W2Is a weight vector and a weight matrix between layers calculated according to ANP, T1And T2For the total relation matrix calculated according to DEMATAL
Step eight, calculating a weighted supermtrix: will T1And T2Each element in (1) is respectively related to w1And W2Multiplying the only elements in the same row which are not 0 to obtain T'1And T'2(ii) a Finally, mixing T'2Index of (1) is T'1Multiplying the elements of the middle membership to obtain a weighted super matrix H (H) of all indexesi)n×n；
Ninthly, determining weight: multiplying the weighted super matrix H by multiple times, normalizing the column vector of the matrix before each multiplication to obtain a stable weighted super matrix, and calculating the weight
10. A system for implementing the method of any preceding claim, comprising: a crawler unit, a linear regression unit, and a DEMATEL-ANP unit, wherein: the crawler unit is connected with the linear regression unit and transmits basic data of the software project, the linear regression unit is connected with the DEMATEL-ANP unit and transmits index scores, and finally index weighting summation is carried out through the DEMATEL-ANP unit to obtain a comprehensive index result.
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