CN113988712A - Continuous operation and maintenance flow efficiency evaluation method, device, equipment and medium - Google Patents

Continuous operation and maintenance flow efficiency evaluation method, device, equipment and medium Download PDF

Info

Publication number
CN113988712A
CN113988712A CN202111395290.0A CN202111395290A CN113988712A CN 113988712 A CN113988712 A CN 113988712A CN 202111395290 A CN202111395290 A CN 202111395290A CN 113988712 A CN113988712 A CN 113988712A
Authority
CN
China
Prior art keywords
continuous operation
project
maintenance pipeline
maintenance
data
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN202111395290.0A
Other languages
Chinese (zh)
Inventor
张洋
陈婷婷
王涛
王怀民
吴逸文
蔡孟栾
邬小军
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
National University of Defense Technology
Original Assignee
National University of Defense Technology
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by National University of Defense Technology filed Critical National University of Defense Technology
Priority to CN202111395290.0A priority Critical patent/CN113988712A/en
Publication of CN113988712A publication Critical patent/CN113988712A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
    • G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/24—Querying
    • G06F16/245—Query processing
    • G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
    • G06F16/2462—Approximate or statistical queries
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/10—Office automation; Time management
    • G06Q10/101—Collaborative creation, e.g. joint development of products or services
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/20—Administration of product repair or maintenance

Abstract

The invention relates to the field of software development, and discloses a method, a device, equipment and a medium for evaluating continuous operation and maintenance flow efficiency, wherein a target project is searched in a collaborative development community according to a preset index, and an open source project set is constructed according to the target project; in the open source project set, checking the target project by using an API (application programming interface) to obtain a project of the application continuous operation and maintenance pipeline; and analyzing the influence of the continuous operation and maintenance pipeline on the project development process from different dimensions according to the regression of the precise breakpoint mixed effect in the project of the continuous operation and maintenance pipeline, thereby evaluating the efficiency of the continuous operation and maintenance pipeline.

Description

Continuous operation and maintenance flow efficiency evaluation method, device, equipment and medium
Technical Field
The present application relates to the field of software development, and in particular, to a method, an apparatus, a device, and a medium for evaluating continuous operation and maintenance flow performance.
Background
Currently co-development communities (e.g., GitHub) have attracted contributors from all over the world into project development, resulting in a significant amount of development activity. When a large amount of development activities occur, it is difficult for a developer to efficiently complete the links of code management (code submission, code static analysis, compilation, construction, packaging, unit testing, and the like), code integration, deployment, release, and the like. To reduce the operation and maintenance burden and improve operation and maintenance efficiency, more and more project managers are beginning to use continuous operation and maintenance pipelines. In the actual software development process, a development team utilizes a continuous operation and maintenance tool provided by a third party or a collaborative development community to construct continuous operation and maintenance pipelines according to project requirements, and each continuous operation and maintenance pipeline can automatically complete the operation and maintenance work after being triggered, so that the aim of rapid and controllable delivery is fulfilled.
In a collaborative development community like GitHub, there are a number of persistent operation and maintenance tools, such as Travis CI, Jenkins, Docker, etc. provided by third parties, and GitHub Actions provided by GitHub. However, currently, an evaluation method for the performance of the continuous operation and maintenance pipeline is lacking, and the continuous operation and maintenance pipeline cannot be evaluated and compared. In addition, the continuous operation and maintenance pipeline actually required by the project often depends on task complexity, development requirements, team scale and the like, and at present, the project manager mainly selects the continuous operation and maintenance pipeline by experience, and the selection of the continuous operation and maintenance pipeline suitable for the project becomes extremely difficult. If a continuous operation and maintenance tool or a continuous operation and maintenance pipeline which is not suitable for the project is selected, the operation and maintenance efficiency cannot be improved, and the development and operation and maintenance burden is increased. Therefore, how to quantitatively evaluate the performance of the continuous operation and maintenance pipeline, help the project manager select the continuous operation and maintenance pipeline more suitable for the project, and promote the continuous integration and delivery of the software project becomes a technical problem to be solved urgently.
The above is only for the purpose of assisting understanding of the technical aspects of the present invention, and does not represent an admission that the above is prior art.
Disclosure of Invention
The invention mainly aims to provide a method, a device, equipment and a medium for evaluating the efficiency of a continuous operation and maintenance pipeline, and aims to solve the technical problem that the efficiency of the continuous operation and maintenance pipeline cannot be quantitatively evaluated in the prior art.
In order to achieve the above object, the present invention provides a method for continuous operation and maintenance flow performance evaluation, the method comprising:
searching a target project in a collaborative development community according to a preset index, and constructing an open source project set according to the target project;
in the open source project set, checking the target project by using an API (application programming interface) to obtain a project of the application continuous operation and maintenance pipeline;
acquiring specific continuous operation and maintenance pipeline data in the project applying the continuous operation and maintenance pipeline, and constructing a continuous operation and maintenance pipeline analysis database according to the continuous operation and maintenance pipeline data;
extracting data from the continuous operation and maintenance pipeline analysis database according to a preset evaluation dimension and a preset index to generate an output data set;
extracting project dimensions, continuous operation and maintenance pipeline dimensions and measurement factors which possibly influence the performance of the continuous operation and maintenance pipeline under the time dimension from the projects of the continuous operation and maintenance pipeline and generating a multi-dimensional measurement factor set;
taking the output data set as an output variable, and taking the multi-dimensional measurement factor set as an input variable to construct an accurate breakpoint mixed effect regression model;
the regression model is analyzed to obtain a performance assessment.
Optionally, the searching for the target project according to the preset index in the collaborative development community, and constructing the open source project set according to the target project includes:
collecting active open source projects according to Fork, Watch, Star and Issue indexes in a collaborative development community;
and constructing an open source project set according to the active open source projects.
Optionally, the step of obtaining specific continuous operation and maintenance pipeline data in the item to which the continuous operation and maintenance pipeline is applied and constructing a continuous operation and maintenance pipeline analysis database according to the continuous operation and maintenance pipeline data includes:
acquiring project basic information in a project of the application continuous operation and maintenance pipeline, wherein the project basic information comprises:
project name, project ID, programming language, project size, Star number, contributor number, and creation time;
collecting code development data, development task data and continuous operation and maintenance pipeline data according to the project ID;
and constructing a continuous operation and maintenance pipeline analysis database according to the code development data, the development task data and the continuous operation and maintenance pipeline data.
Optionally, the step of extracting data from the continuous operation and maintenance pipeline analysis database according to a preset evaluation dimension and a preset index to generate an output data set includes:
and extracting data from the continuous operation and maintenance pipeline analysis database in a mode of dividing an observation period time window by using continuous operation and maintenance pipeline time as a node according to Commit, Pull Request, Issue and code change dimension to generate an output data set.
Optionally, the step of constructing the accurate breakpoint mixed effect regression model by using the output data set as an output variable and the multidimensional measurement factor set as an input variable includes:
carrying out numerical coding on non-numerical type factors in the multi-dimensional measurement factor set, and carrying out normalization pretreatment on the numerical type factors to obtain a processed multi-dimensional measurement factor set;
and extracting items of which 24 windows are effective and can evaluate dimension data, taking the output data set as an output variable, and taking the processed multi-dimension measurement factor set as an input variable to construct an accurate breakpoint mixed effect regression model.
Optionally, after the step of extracting the items of which 24 windows are all effective and can evaluate the dimension data, taking the output data set as an output variable, and taking the processed multidimensional measurement factor set as an input variable to construct the accurate breakpoint mixed effect regression model, the method further includes:
checking the multiple collinearity of the prediction variable set by using multiple collinearity checking-variance expansion coefficient (VIF), and checking whether the variance expansion factor is safe;
obtaining basic results of the model, such as fixed effect fitting degree, mixed effect fitting degree, variable coefficient, standard error, significance level and the like, according to the regression model;
and obtaining results such as variable square sum, significance level and the like of the regression model by using an ANOVA analysis of variance method.
Optionally, the step of analyzing the regression model to obtain a performance assessment result includes:
analyzing the regression model to obtain coefficients of variables;
screening out variables with significant influence, namely variables with significant coefficients less than 0.05 according to the coefficient significance level of the variables;
aiming at the variables with significant influence, screening out variables with large influence according to the square sum information of the variables, namely, the variables with the square sum significance coefficient smaller than 0.05 and the explained variance ratio larger than 0.01;
and calculating the influence effect value of the variable by using the variable coefficient value according to the variable with larger influence as a key measurement factor, and obtaining the efficiency evaluation result according to the influence effect value.
In addition, in order to achieve the above object, the present invention further provides an apparatus for evaluating the effectiveness of continuous operation and maintenance flowing water, the apparatus comprising:
the project searching module is used for searching a target project in the collaborative development community according to a preset index and constructing an open source project set according to the target project;
the project inspection module is used for inspecting the target project by utilizing an API (application programming interface) in the open source project set to obtain a project of the application continuous operation and maintenance pipeline;
the data construction module is used for acquiring specific continuous operation and maintenance pipeline data in the project applying the continuous operation and maintenance pipeline and constructing a continuous operation and maintenance pipeline analysis database according to the continuous operation and maintenance pipeline data;
the data extraction module is used for extracting data from the continuous operation and maintenance pipeline analysis database according to a preset evaluation dimension and a preset index so as to generate an output data set;
the dimension extraction module is used for extracting project dimensions, continuous operation and maintenance pipeline dimensions and measurement factors which possibly influence the performance of the continuous operation and maintenance pipeline under the time dimension in the projects of the continuous operation and maintenance pipeline and generating a multi-dimensional measurement factor set;
the model construction module is used for constructing an accurate breakpoint mixed effect regression model by taking the output data set as an output variable and the multi-dimensional measurement factor set as an input variable;
and the result evaluation module is used for analyzing the regression model to obtain a performance evaluation result.
In addition, to achieve the above object, the present invention also provides a computer device, including: the system comprises a memory, a processor and a continuous operation and maintenance pipeline performance evaluation program stored on the memory and capable of running on the processor, wherein the continuous operation and maintenance pipeline performance evaluation program is configured to implement the continuous operation and maintenance pipeline performance evaluation method.
In addition, to achieve the above object, the present invention further provides a medium, in which a persistent operation and maintenance pipeline performance evaluation program is stored, and when the persistent operation and maintenance pipeline performance evaluation program is executed by a processor, the method implements the steps of the persistent operation and maintenance pipeline performance evaluation method as described above.
Searching a target project in a collaborative development community according to a preset index, and constructing an open source project set according to the target project; in the open source project set, checking the target project by using an API (application programming interface) to obtain a project of the application continuous operation and maintenance pipeline; acquiring specific continuous operation and maintenance pipeline data in the project applying the continuous operation and maintenance pipeline, and constructing a continuous operation and maintenance pipeline analysis database according to the continuous operation and maintenance pipeline data; extracting data from the continuous operation and maintenance pipeline analysis database according to a preset evaluation dimension and a preset index to generate an output data set; extracting project dimensions, continuous operation and maintenance pipeline dimensions and measurement factors which possibly influence the performance of the continuous operation and maintenance pipeline under the time dimension from the projects applying the continuous operation and maintenance pipeline and generating a multi-dimensional measurement factor set; taking the output data set as an output variable, and taking the multi-dimensional measurement factor set as an input variable to construct an accurate breakpoint mixed effect regression model; analyzing the regression model to obtain a performance evaluation result; analyzing the statistical association relationship between the performance index of the continuous operation and maintenance pipeline and each measurement factor; according to the regression model analysis result, all measurement factors are integrated by using indexes such as coefficients, significance levels, square sums and the like to obtain an efficiency evaluation result, and the technical effect of quantitatively evaluating the efficiency of the continuous production line is achieved.
Drawings
Fig. 1 is a schematic structural diagram of a continuous operation and maintenance pipeline performance evaluation device of a hardware operating environment according to an embodiment of the present invention;
FIG. 2 is a flowchart illustrating a method for evaluating performance of a continuous operation and maintenance pipeline according to a first embodiment of the present invention.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Referring to fig. 1, fig. 1 is a schematic structural diagram of a continuous operation and maintenance pipeline performance evaluation device of a hardware operating environment according to an embodiment of the present invention.
As shown in fig. 1, the continuous operation and maintenance flow performance evaluation device may include: a processor 1001, such as a Central Processing Unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Wherein a communication bus 1002 is used to enable connective communication between these components. The user interface 1003 may include a Display screen (Display), an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a Wireless interface (e.g., a Wireless-Fidelity (WI-FI) interface). The Memory 1005 may be a Random Access Memory (RAM) Memory, or may be a Non-Volatile Memory (NVM), such as a disk Memory. The memory 1005 may alternatively be a storage device separate from the processor 1001.
Those skilled in the art will appreciate that the configuration shown in fig. 1 does not constitute a limitation of the continuous operation and maintenance flow performance evaluation device, and may include more or less components than those shown, or some components in combination, or a different arrangement of components.
As shown in fig. 1, the memory 1005 as a storage medium may include an operating system, a data storage module, a network communication module, a user interface module, and a continuous operation and maintenance pipeline performance evaluation program.
In the continuous operation and maintenance pipelining performance evaluation device shown in fig. 1, the network interface 1004 is mainly used for data communication with a network server; the user interface 1003 is mainly used for data interaction with a user; the processor 1001 and the memory 1005 of the continuous operation and maintenance pipeline performance evaluation device of the present invention may be disposed in the continuous operation and maintenance pipeline performance evaluation device, and the continuous operation and maintenance pipeline performance evaluation device invokes the continuous operation and maintenance pipeline performance evaluation program stored in the memory 1005 through the processor 1001 and executes the continuous operation and maintenance pipeline performance evaluation method provided by the embodiment of the present invention.
An embodiment of the invention provides a method for evaluating a continuous operation and maintenance flow performance, and referring to fig. 2, fig. 2 is a flowchart illustrating a first embodiment of the method for evaluating a continuous operation and maintenance flow performance according to the invention.
In this embodiment, the method for evaluating the performance of the continuous operation and maintenance flow includes the following steps:
step S10: searching a target project in a collaborative development community according to a preset index, and constructing an open source project set according to the target project;
it should be noted that, according to indicators such as Fork, Watch, Star, Issue, and the like, active open-source projects in the collaborative development community are collected, and an active open-source project set is constructed, for example: the API of GitHub is utilized to gather popular open source items that satisfy the following conditions: at least 1000 stars; is at least 1 time by Fork; is not delete and is not a Fork entry; project was created 2 months ago 2021; and simultaneously screening active open source projects meeting the following conditions: at least 10 Issue or Pull Request; in the last 3 months, development activities such as code submission, development task processing, contribution merging, comment submission and the like exist.
Further, the steps of searching for a target project according to a preset index in the collaborative development community and constructing an open source project set according to the target project include: collecting active open source projects according to Fork, Watch, Star and Issue indexes in a collaborative development community; and constructing an open source project set according to the active open source projects.
Step S20: in the open source project set, checking the target project by using an API (application programming interface) to obtain a project of the application continuous operation and maintenance pipeline;
step S30: acquiring specific continuous operation and maintenance pipeline data in the project applying the continuous operation and maintenance pipeline, and constructing a continuous operation and maintenance pipeline analysis database according to the continuous operation and maintenance pipeline data;
further, the step of obtaining specific continuous operation and maintenance pipeline data in the item applying the continuous operation and maintenance pipeline and constructing a continuous operation and maintenance pipeline analysis database according to the continuous operation and maintenance pipeline data includes: acquiring basic project information in the application continuous operation and maintenance pipeline project, wherein the basic project information comprises: project name, project ID, programming language, project size, Star number, contributor number, and creation time; collecting code development data, development task data and continuous operation and maintenance pipeline data according to the project ID; and constructing a continuous operation and maintenance pipeline analysis database according to the code development data, the development task data and the continuous operation and maintenance pipeline data.
In specific implementation, whether a continuous operation and maintenance pipeline (GitHub Actions) is used or not is judged by using an API (application programming interface), and the specific judgment method is that if a relevant configuration YAML file exists in a "/githu/workflows" path, the item uses the continuous operation and maintenance pipeline, otherwise, the item is not used; acquiring basic project information according to the screened open source project names of the continuous operation and maintenance assembly line, wherein the specific data acquisition content comprises the following steps: project name, project ID, programming language, project size, Star number, contributor number, creation time, etc.; collecting code development data according to the project ID, specifically comprising: commit ID, Commit type, Commit author, submitter, Commit time, etc.; collecting development task data according to the project ID, specifically comprising: ID of Issue or Pull Request, publisher, processing status, publication time, end time, title and description, number of comments, etc.; acquiring continuous operation and maintenance pipeline data according to the project ID, which specifically comprises the following steps: the ID of the continuous operation and maintenance pipeline, the name of the continuous operation and maintenance pipeline, the release time, the content of the configuration file, the running log and the like.
Step S40: extracting data from the continuous operation and maintenance pipeline analysis database according to a preset evaluation dimension and a preset index to generate an output data set;
further, the step of extracting data from the continuous operation and maintenance pipeline analysis database according to a preset evaluation dimension and a preset index to generate an output data set includes: and extracting data from the continuous operation and maintenance pipeline analysis database in a mode of dividing an observation period time window by using continuous operation and maintenance pipeline time as a node according to Commit, Pull Request, Issue and code change dimension to generate an output data set.
In specific implementation, determining the performance evaluation dimensions of the continuous operation and maintenance pipeline to be Commit, Pull Request, Issue, code change and the like; and dividing an observation period time window by using continuous operation and maintenance pipeline time as a node. Taking 375 days before and after the observation period, dividing the observation period into 25 windows by taking 30 days as a unit, and filtering the windows which adopt 15 days before and after the observation period, wherein the number of the rest windows is 1-24; extracting Commit data, wherein the specific factors comprise: average number of Commit merged (nMergeCommits) in each window period, average number of Commit not merged (nNonMergeCommits) in each window period; extracting Pull Request data, wherein the specific factors comprise: average number of Pull requests closed (nClosedPRs) within each window period; the average processing time (avgPRLatency) of closed Pull requests in each window period, namely the time interval from submission to closing of the Pull requests, which is taken as a unit of hour; extracting Issue data, wherein the specific factors comprise: the average number of issues closed over each window period (ncosedIssues); the average processing time (avg Issue latency) of closed issues within each window period, i.e., the time interval between submission and closing of an Issue, in hours; and extracting code change data, wherein the code change data specifically comprises the average line number (avgChangedLines) of code changes in each window period, and the line number of the code change is increased by one every time one line is added, deleted or changed.
Step S50: extracting project dimensions, continuous operation and maintenance pipeline dimensions and measurement factors which possibly influence the performance of the continuous operation and maintenance pipeline under the time dimension from the projects applying the continuous operation and maintenance pipeline and generating a multi-dimensional measurement factor set;
it should be noted that the specific measurement factors include: a programming language (language) for selecting a program having the largest code amount from a plurality of programming languages; calculating the number of contributors of a project, i.e. the number of developers who submitted Commit at least once to the code base, including core developers and external contributors; developing network scales (nForks) calculating the Fork number of the project; item popularity (nStars) calculating the number of Stars for an item; project age (ageAtGH) calculating the time interval from the creation time of the project on the GitHub to 2021 and 2 months, in months; code task (nCommits) calculates the total Commit Commit number.
In a specific implementation, the pipeline dimension measurement factors are extracted, and the specific measurement factors include: the number of pipelines (nActions) is used for calculating the number of pipeline configuration files under a pipeline working directory; age of using the continuous operation and maintenance pipeline (ageAtGA), i.e. the time for starting using the continuous operation and maintenance pipeline and the time interval of 2021 year and 2 months, which is in units of months; extracting time window factors, specifically including: time window number (time), i.e. the window number in S3.1; using a time label (timeafter interaction) after the continuous operation and maintenance pipeline, wherein the time label (timeafter interaction) before the continuous operation and maintenance pipeline is used is 0, and the time label (timeafter interaction) after the continuous operation and maintenance pipeline is used is window number-12; whether the window period uses a continuous operation and maintenance pipeline (intervention), 0 is unused, and 1 is used.
Step S60: taking the output data set as an output variable, and taking the multi-dimensional measurement factor set as an input variable to construct an accurate breakpoint mixed effect regression model;
further, the step of constructing the accurate breakpoint mixed effect regression model by using the output data set as an output variable and the multidimensional measurement factor set as an input variable includes: carrying out numerical coding on non-numerical type factors in the multi-dimensional measurement factor set, and carrying out normalization pretreatment on the numerical type factors to obtain a processed multi-dimensional measurement factor set; and extracting items of which 24 windows are effective and can evaluate dimension data, taking the output data set as an output variable, and taking the processed multi-dimension measurement factor set as an input variable to construct an accurate breakpoint mixed effect regression model.
It should be noted that, after the steps of extracting the items of which 24 windows are all effective and can evaluate the dimensional data, taking the output data set as the output variable, and taking the processed multidimensional measurement factor set as the input variable to construct the accurate breakpoint mixed effect regression model, the method further includes: checking the multiple collinearity of the prediction variable set by using multiple collinearity checking-variance expansion coefficient (VIF), and checking whether the variance expansion factor is safe; obtaining basic results of the model, such as fixed effect fitting degree, mixed effect fitting degree, variable coefficient, standard error, significance level and the like, according to the regression model; and obtaining results such as variable square sum, significance level and the like of the regression model by using an ANOVA analysis of variance method.
Step S70: the regression model is analyzed to obtain a performance assessment.
It should be noted that, the non-numerical factors in the multidimensional measurement factors are subjected to numerical coding, the numerical factors are subjected to normalization preprocessing, the influence caused by different numerical scales is eliminated, and meanwhile, the related factors are subjected to logarithmic change to stabilize the variance and reduce the heteroscedasticity when necessary.
In specific implementation, an lmer function in an R tool is used for constructing an accurate breakpoint mixed effect regression model, and a specific calculation formula is as follows:
yi＝α+β·timei+γ·interventioni+δ·timeAfterInterverntioni+η·controlsi+εi
wherein, yiFor specific model output variables, alpha, beta, gamma, delta and eta are model training coefficients (actual operation can be obtained by R tool training), controliFor specific control variables (including random effect variables and fixed effect variables), εiIs an error.
In the specific implementation, the multiple collinearity of the prediction variable set is checked by using a multiple collinearity check-variance expansion factor (VIF), and whether the variance expansion factor is safe is checked; obtaining fixed effect fitting degree of model according to model output
Figure BDA0003369791150000101
Degree of fitting of mixing effect
Figure BDA0003369791150000102
Basic results such as a coefficient of variation (coefficient), a standard error (standard error), a significance level (significance level), and the like; and obtaining results such as the variable square sum, the significance level and the like of the model by using an ANOVA analysis of variance method.
Further, the step of analyzing the regression model to obtain a performance evaluation result includes: analyzing the regression model to obtain coefficients of variables; screening out variables with significant influence, namely variables with significant coefficients less than 0.05 according to the coefficient significance level of the variables; aiming at the variables with significant influence, screening out variables with large influence according to the square sum information of the variables, namely, the variables with the square sum significance coefficient smaller than 0.05 and the explained variance ratio larger than 0.01; and calculating the influence effect value of the variable by using the variable coefficient value according to the variable with larger influence as a key measurement factor, and obtaining the efficiency evaluation result according to the influence effect value.
Searching a target project in a collaborative development community according to a preset index, and constructing an open source project set according to the target project; in the open source project set, checking the target project by using an API (application programming interface) to obtain a project of the application continuous operation and maintenance pipeline; acquiring specific continuous operation and maintenance pipeline data in the project applying the continuous operation and maintenance pipeline, and constructing a continuous operation and maintenance pipeline analysis database according to the continuous operation and maintenance pipeline data; extracting data from the continuous operation and maintenance pipeline analysis database according to a preset evaluation dimension and a preset index to generate an output data set; extracting project dimensions, continuous operation and maintenance pipeline dimensions and measurement factors which possibly influence the performance of the continuous operation and maintenance pipeline under the time dimension from the projects applying the continuous operation and maintenance pipeline and generating a multi-dimensional measurement factor set; taking the output data set as an output variable, and taking the multi-dimensional measurement factor set as an input variable to construct an accurate breakpoint mixed effect regression model; analyzing the regression model to obtain a performance evaluation result; analyzing the statistical association relationship between the performance index of the continuous operation and maintenance pipeline and each measurement factor; according to the regression model analysis result, all measurement factors are integrated by using indexes such as coefficients, significance levels, square sums and the like to obtain an efficiency evaluation result, and the technical effect of quantitatively evaluating the efficiency of the continuous production line is achieved.
In addition, an embodiment of the present invention further provides a medium, where the continuous operation and maintenance pipeline performance evaluation program is stored, and when the continuous operation and maintenance pipeline performance evaluation program is executed by a processor, the steps of the continuous operation and maintenance pipeline performance evaluation method described above are implemented.
The embodiments or specific implementations of the continuous operation and maintenance flow performance evaluation apparatus of the present invention refer to the above method embodiments, and are not described herein again.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or system that comprises the element.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which is stored in a storage medium (e.g., a rom/ram, a magnetic disk, an optical disk) and includes instructions for enabling a terminal device (e.g., a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present invention.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.

Claims (10)

1. A method for continuous operation and maintenance flow performance evaluation, the method comprising:
searching a target project in a collaborative development community according to a preset index, and constructing an open source project set according to the target project;
in the open source project set, checking the target project by using an API (application programming interface) to obtain a project of the application continuous operation and maintenance pipeline;
acquiring specific continuous operation and maintenance pipeline data in the project applying the continuous operation and maintenance pipeline, and constructing a continuous operation and maintenance pipeline analysis database according to the continuous operation and maintenance pipeline data;
extracting data from the continuous operation and maintenance pipeline analysis database according to a preset evaluation dimension and a preset index to generate an output data set;
extracting project dimensions, continuous operation and maintenance pipeline dimensions and measurement factors which possibly influence the performance of the continuous operation and maintenance pipeline under the time dimension from the projects applying the continuous operation and maintenance pipeline and generating a multi-dimensional measurement factor set;
taking the output data set as an output variable, and taking the multi-dimensional measurement factor set as an input variable to construct an accurate breakpoint mixed effect regression model;
the regression model is analyzed to obtain a performance assessment.
2. The method of claim 1, wherein the step of searching for target projects in the collaborative development community according to preset indexes and constructing an open source project set according to the target projects comprises:
collecting active open source projects according to Fork, Watch, Star and Issue indexes in a collaborative development community;
and constructing an open source project set according to the active open source projects.
3. The method of claim 1, wherein the step of obtaining specific persistent operation and maintenance pipeline data in the project of the application persistent operation and maintenance pipeline, and constructing the persistent operation and maintenance pipeline analysis database according to the persistent operation and maintenance pipeline data comprises:
acquiring project basic information in a project of the application continuous operation and maintenance pipeline, wherein the project basic information comprises:
project name, project ID, programming language, project size, Star number, contributor number, and creation time;
collecting code development data, development task data and continuous operation and maintenance pipeline data according to the project ID;
and constructing a continuous operation and maintenance pipeline analysis database according to the code development data, the development task data and the continuous operation and maintenance pipeline data.
4. The method of claim 1, wherein the step of extracting data from the continuous operation and maintenance pipeline analysis database according to a preset evaluation dimension and a preset index to generate an output data set comprises:
and extracting data from the continuous operation and maintenance pipeline analysis database in a mode of dividing an observation period time window by taking continuous operation and maintenance pipeline time as a node according to Commit, PullRequest, Issue and code change dimension to generate an output data set.
5. The method of claim 1, wherein the step of constructing an accurate breakpoint mixed effect regression model using the output data set as output variables and the multidimensional metric factor set as input variables comprises:
carrying out numerical coding on non-numerical type factors in the multi-dimensional measurement factor set, and carrying out normalization pretreatment on the numerical type factors to obtain a processed multi-dimensional measurement factor set;
and extracting items of which 24 windows are effective and can evaluate dimension data, taking the output data set as an output variable, and taking the processed multi-dimension measurement factor set as an input variable to construct an accurate breakpoint mixed effect regression model.
6. The method of claim 5, wherein after the steps of extracting items of which 24 windows are all valid to evaluate dimension data, using the output data set as output variables, and using the processed multidimensional metric factor set as input variables to construct the accurate breakpoint mixed effect regression model, the method further comprises:
checking the multiple collinearity of the prediction variable set by using multiple collinearity checking-variance expansion coefficient (VIF), and checking whether the variance expansion factor is safe;
obtaining basic results of the model, such as fixed effect fitting degree, mixed effect fitting degree, variable coefficient, standard error, significance level and the like, according to the regression model;
and obtaining results such as variable square sum, significance level and the like of the regression model by using an ANOVA analysis of variance method.
7. The method of claim 1, wherein the step of analyzing the regression model to obtain performance assessment results comprises:
analyzing the regression model to obtain coefficients of variables;
screening out variables with significant influence, namely variables with significant coefficients less than 0.05 according to the coefficient significance level of the variables;
aiming at the variables with significant influence, screening out variables with large influence according to the square sum information of the variables, namely, the variables with the square sum significance coefficient smaller than 0.05 and the explained variance ratio larger than 0.01;
and calculating the influence effect value of the variable by using the variable coefficient value according to the variable with larger influence as a key measurement factor, and obtaining the efficiency evaluation result according to the influence effect value.
8. An apparatus for evaluating continuous operation and maintenance flow performance, the apparatus comprising:
the project searching module is used for searching a target project in the collaborative development community according to a preset index and constructing an open source project set according to the target project;
the project inspection module is used for inspecting the target project by utilizing an API (application programming interface) in the open source project set to obtain a project of the application continuous operation and maintenance pipeline;
the data construction module is used for acquiring specific continuous operation and maintenance pipeline data in the project applying the continuous operation and maintenance pipeline and constructing a continuous operation and maintenance pipeline analysis database according to the continuous operation and maintenance pipeline data;
the data extraction module is used for extracting data from the continuous operation and maintenance pipeline analysis database according to a preset evaluation dimension and a preset index so as to generate an output data set;
the dimension extraction module is used for extracting project dimensions, continuous operation and maintenance pipeline dimensions and measurement factors which possibly influence the performance of the continuous operation and maintenance pipeline under the time dimension in the projects of the continuous operation and maintenance pipeline and generating a multi-dimensional measurement factor set;
the model construction module is used for constructing an accurate breakpoint mixed effect regression model by taking the output data set as an output variable and the multi-dimensional measurement factor set as an input variable;
and the result evaluation module is used for analyzing the regression model to obtain a performance evaluation result.
9. An apparatus for evaluating continuous operation and maintenance flow performance, the apparatus comprising: a memory, a processor, and a continuous operation and maintenance pipeline performance evaluation program stored on the memory and executable on the processor, the continuous operation and maintenance pipeline performance evaluation program being configured to implement the steps of the continuous operation and maintenance pipeline performance evaluation method according to any one of claims 1 to 7.
10. A medium having a persistent operation and maintenance pipeline performance evaluation program stored thereon, wherein the persistent operation and maintenance pipeline performance evaluation program when executed by a processor implements the steps of the persistent operation and maintenance pipeline performance evaluation method according to any one of claims 1 to 7.
CN202111395290.0A 2021-11-23 2021-11-23 Continuous operation and maintenance flow efficiency evaluation method, device, equipment and medium Pending CN113988712A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202111395290.0A CN113988712A (en) 2021-11-23 2021-11-23 Continuous operation and maintenance flow efficiency evaluation method, device, equipment and medium

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202111395290.0A CN113988712A (en) 2021-11-23 2021-11-23 Continuous operation and maintenance flow efficiency evaluation method, device, equipment and medium

Publications (1)

Publication Number Publication Date
CN113988712A true CN113988712A (en) 2022-01-28

Family

ID=79750033

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202111395290.0A Pending CN113988712A (en) 2021-11-23 2021-11-23 Continuous operation and maintenance flow efficiency evaluation method, device, equipment and medium

Country Status (1)

Country Link
CN (1) CN113988712A (en)

Similar Documents

Publication Publication Date Title
JP6636071B2 (en) Computer mounting method, computer system and computer device
Muller et al. The application of the pedigree approach to the distributions foreseen in ecoinvent v3
US9031873B2 (en) Methods and apparatus for analysing and/or pre-processing financial accounting data
Bettenburg et al. Studying the impact of social interactions on software quality
US10984476B2 (en) Method and apparatus for determining inventor impact
US20110040587A1 (en) System and Method of Measuring Process Compliance
Chaturvedi et al. Predicting the complexity of code changes using entropy based measures
Li et al. Robust jump regressions
Izquierdo et al. An empirical study on the maturity of the eclipse modeling ecosystem
Manjunath et al. A study on sampling techniques for data testing
Gold et al. Spatial complexity metrics: an investigation of utility
CN112184003A (en) Bank counter workload assessment method and device, storage medium and electronic equipment
US11372640B1 (en) Generating efficiency metrics for knowledge workers
CN113988712A (en) Continuous operation and maintenance flow efficiency evaluation method, device, equipment and medium
CN111612098B (en) Method and device for predicting milestone completion time in collaborative development community
Mittelmann et al. A server for automated performance analysis of benchmarking data
Wang et al. Quantitative analysis of requirements evolution across multiple versions of an industrial software product
CN115544127A (en) Code amount processing method and system, electronic device and readable storage medium
Hernández et al. Identifying Similarity of Software in Apache Ecosystem--An Exploratory Study
CN112069807A (en) Text data theme extraction method and device, computer equipment and storage medium
AU2020201689A1 (en) Cognitive forecasting
Saade et al. LCA—interpretation of results
CN116860227B (en) Data development system and method based on big data ETL script arrangement
Zheng et al. Measure the Diversity of Open Source Software Projects' Forks with Fork Entropy
Fatima et al. Towards a Sustainability-aware Software Architecture Evaluation for Cloud-Based Software Services

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination