Comprehensive development evaluation method and system based on cloud computing technology

Abstract

The invention discloses a development comprehensive evaluation method and system based on a cloud computing technology, and the system comprises a data collection module which is responsible for automatically collecting data of functional and non-functional demands in the software development and testing process; the data processing module is used for carrying out preprocessing and deep analysis on the collected data so as to identify key indexes; and the weight distribution module is used for quantifying the key indexes and distributing weights according to project characteristics and business requirements. Through an automatic tool of a cloud computing platform, comprehensive monitoring and data collection of the test process can be realized, so that the test coverage rate is improved, a code coverage rate analysis tool is used, all key code paths are ensured to be tested, a continuous integration / continuous deployment (CI / CD) process, automatic test and feedback are implemented, and the test efficiency is improved. And key problem omission caused by human negligence is reduced.

Description

Technical Field

[0001] The present invention relates to the field of software development, and in particular to a comprehensive development evaluation method and system based on cloud computing technology. Background Art

[0002] When conducting software quality assessment, a series of testing methods are usually used, including unit testing, integration testing, system testing and acceptance testing, as well as code review, performance testing, security testing, etc. In addition, international standards such as ISO / IEC 25010 are also referenced. This is a model for software product quality assessment, which provides a framework to define and evaluate the quality characteristics of software products.

[0003] Quality assessment is not only carried out at the final stage of software development, but should also be carried out throughout the entire software development life cycle, from requirements analysis, design, coding to testing and maintenance. Through continuous quality assurance and quality control activities, it can be ensured that the final delivered software product meets the expectations and needs of users.

[0004] For example, the cloud computing-based software quality assessment method disclosed in the authorization announcement number CN114546841B, after adopting the above technical scheme, the beneficial effects of the present invention are: combining the working hours in the software development process, the function importance coefficient, the basic problem rate, the deduction coefficient and the number of functions of the version, multiple aspects work together to achieve rapid evaluation of software quality.

[0005] Although the above patent solves some problems in the prior art, such as the complexity and workload of finished software quality assessment, and the timeliness of quality assessment when small versions are rapidly developed and launched under the agile development model, the effectiveness of this method depends largely on the coverage of the test. If the test is not comprehensive, some key issues may be missed, resulting in inaccurate evaluation results. In addition, software quality includes not only functional requirements (such as the number of functions and BUG information), but also non-functional requirements (such as performance, usability, security, etc.). These non-functional requirements may be difficult to quantify, thus affecting the comprehensiveness of the evaluation. Summary of the invention

[0006] The purpose of the present invention is to provide a development comprehensive evaluation method and system based on cloud computing technology to solve the problems raised in the above background technology.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] A comprehensive development evaluation method based on cloud computing technology includes the following steps:

[0009] Step S1, collecting data on functional requirements and non-functional requirements during software development and testing through automated tools, and integrating the data into a cloud computing platform;

[0010] Step S2, cleaning, formatting and normalizing the data, and using analysis tools of the cloud computing platform to conduct in-depth analysis of the data to identify key indicators;

[0011] Step S3, quantifying the key indicators, converting the functional importance into importance coefficients, converting the performance indicators into performance scores, and assigning weights to each indicator according to project characteristics and business requirements;

[0012] Step S4, calculating the individual function quality score of each function according to the quantified indicators and the assigned weights, and summing up the individual function quality scores of all functions to obtain the quality score of the entire version;

[0013] Step S5, using the reporting tool of the cloud computing platform to generate a quality assessment report including a detailed quality score of each function and a quality score of the entire version.

[0014] In the present invention, in step S1, the automation tools include but are not limited to code analysis tools, performance testing tools, security scanning tools and user experience feedback collection tools.

[0015] In the present invention, in step S2, the analysis tools of the cloud computing platform include data mining, machine learning and statistical analysis tools.

[0016] In the present invention, in step S3, the determination of the importance coefficient is based on the core, generality or secondary nature of the function, and the calculation of the performance score is based on the response time, throughput and error rate indicators;

[0017] Functional importance coefficient calculation formula:

[0018] [\text{Importance coefficient}=\begin{cases}

[0019] 1.2&\text{If the function is a core function}\

[0020] 1.0&\text{If the function is a general function}\

[0021] 0.8&\text{If the function is a secondary function}

[0022] \end{cases}]

[0023] Among them, core functions refer to functions that have a significant impact on the overall performance and user experience of the software, general functions refer to conventional functions of the software, which have a certain impact on the overall performance and user experience, and secondary functions refer to auxiliary functions, which have a small impact on the overall performance and user experience;

[0024] The formula for calculating the total functional working hours of a version is:

[0025] Total function work time of version = i = 1∑n (work time of function i)

[0026] Where n represents the total number of functions in the version, and the work hours of function i represent the working time required to complete function i;

[0027] Calculation formula for working hours quality points:

[0028] Working hours quality score = total functional working hours of the version / standard working hours

[0029] Among them, standard working hours represent the preset ideal working hours, which are used for standardized calculation;

[0030] Basic problem rate calculation formula:

[0031] Basic problem rate = number of basic problems / number of functions

[0032] Among them, the number of basic problems refers to the number of non-serious problems found during the test;

[0033] Deduction coefficient calculation formula:

[0034] [\text{Deduction coefficient}=\begin{cases}

[0035] 0&\text{If there is a blocking problem}\

[0036] 1&\text{If there is no blocking problem}

[0037] \end{cases}]

[0038] Among them, blocking issues refer to problems that seriously affect the functionality or performance of the software.

[0039] In the present invention, in step S4, the calculation formula of the quality score of a single function is: quality score = work-hour quality score * importance coefficient * (1-basic problem rate) * deduction coefficient, wherein the basic problem rate and the deduction coefficient are determined according to the importance of the BUG information;

[0040] The calculation formula of single function quality score is:

[0041] Single function quality score = work-hour quality score × importance coefficient × (1-basic problem rate) × deduction coefficient

[0042] Among them, the work-hour quality score indicates the quality score calculated based on the total functional work-hours of the version, the importance coefficient indicates the coefficient determined based on the importance of the function, the basic problem rate indicates the problem rate calculated based on the number of basic problems, and the deduction coefficient indicates the coefficient determined based on whether there is a blocking problem;

[0043] The quality score calculation formula for the entire version is:

[0044] The quality score of the entire version = i = 1∑n (the quality score of a single function i)

[0045] The quality score of a single function i represents the quality score of each function calculated according to the above formula.

[0046] In the present invention, in step S5, the reporting tool includes a chart generator, a dashboard and a report template to intuitively display the evaluation results.

[0047] A system for developing a comprehensive evaluation method based on cloud computing technology, comprising:

[0048] A data collection module, which is responsible for automatically collecting data on functional and non-functional requirements from the software development and testing process;

[0049] A data processing module, which is used to pre-process and deeply analyze the collected data to identify key indicators;

[0050] A weight allocation module, which is used to quantify key indicators and allocate weights according to project characteristics and business needs;

[0051] A score calculation module, which is used to calculate the quality score of each function and the quality score of the entire version according to the quantified indicators and the assigned weights;

[0052] The report generation module is used to generate a detailed quality assessment report, including charts and dashboards, to intuitively display the assessment results.

[0053] In the present invention, the data collection module is composed of a data source interface, a data grabber and a data cache, and the data processing module is composed of a data cleaning tool, a data formatter, a data normalizer and an analysis engine.

[0054] In the present invention, the weight distribution module is composed of an indicator quantizer, a weight distributor and a weight adjustment tool, and the score calculation module is composed of a calculation engine and a quality score database.

[0055] In the present invention, the report generation module is composed of a report template library, a report generator and a report distribution system.

[0056] Compared with the prior art, the present invention has the following beneficial effects:

[0057] 1. The present invention can realize comprehensive monitoring and data collection of the test process through the automation tools of the cloud computing platform, thereby improving the test coverage, using code coverage analysis tools to ensure that all key code paths are tested, implementing the continuous integration / continuous deployment (CI / CD) process, automating testing and feedback, and reducing the omission of key issues caused by human negligence.

[0058] 2. The present invention utilizes the analysis tools of the cloud computing platform, including performance testing tools, security scanning tools, and user experience feedback collection tools, to quantify non-functional requirements, and adopts standardized quantification methods and indicators, such as performance scores, security vulnerability scores, and user experience indexes, to ensure the quantifiability of non-functional requirements. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 A schematic diagram of a process flow of a comprehensive development evaluation method based on cloud computing technology of the present invention;

[0060] Figure 2 It is a schematic diagram of the overall structure of a development comprehensive evaluation system based on cloud computing technology of the present invention;

[0061] Figure 3 A schematic diagram of a data collection structure of a development comprehensive evaluation system based on cloud computing technology according to the present invention;

[0062] Figure 4 This is a schematic diagram of the data processing module structure of a comprehensive evaluation system for development based on cloud computing technology in the present invention;

[0063] Figure 5 This is a schematic diagram of the structure of a weight allocation module of a development comprehensive evaluation system based on cloud computing technology in the present invention;

[0064] Figure 6 This is a schematic diagram of the structure of a score calculation module of a development comprehensive evaluation system based on cloud computing technology of the present invention;

[0065] Figure 7 This is a schematic diagram of the report generation module structure of a comprehensive evaluation system developed based on cloud computing technology in the present invention. DETAILED DESCRIPTION

[0066] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0067] See also Figure 1 , the present invention provides a technical solution:

[0068] A comprehensive development evaluation method based on cloud computing technology includes the following steps:

[0069] Step S1, collecting data on functional requirements and non-functional requirements during software development and testing through automated tools, and integrating the data into a cloud computing platform, wherein the automated tools include but are not limited to code analysis tools, performance testing tools, security scanning tools, and user experience feedback collection tools;

[0070] In this step, automated tools are used to collect data on functional and non-functional requirements during software development and testing to ensure test coverage. Automated tools include code analysis tools, performance testing tools, security scanning tools, and user experience feedback collection tools. The collected data is integrated into the cloud computing platform to ensure the accuracy and completeness of the data, laying the foundation for subsequent analysis.

[0071] Step S2, cleaning, formatting and normalizing the data, and using analysis tools of the cloud computing platform to conduct in-depth analysis of the data to identify key indicators, the analysis tools of the cloud computing platform including data mining, machine learning and statistical analysis tools;

[0072] In this step, the data integrated into the cloud computing platform is cleaned, formatted, and normalized for subsequent analysis. The cloud computing platform's analytical tools are used to conduct in-depth analysis of the data to identify key indicators of functional and non-functional requirements. The test coverage is ensured through analysis, key issues missed in the test are identified, and non-functional requirements are quantified.

[0073] Step S3, quantifying the key indicators, converting the function importance into importance coefficients, converting the performance indicators into performance scores, assigning weights to each indicator according to project characteristics and business needs, determining the importance coefficient based on the core, general or secondary nature of the function, and calculating the performance score based on response time, throughput and error rate indicators;

[0074] Functional importance coefficient calculation formula:

[0075] [\text{Importance coefficient}=\begin{cases}

[0076] 1.2&\text{If the function is a core function}\

[0077] 1.0&\text{If the function is a general function}\

[0078] 0.8&\text{If the function is a secondary function}

[0079] \end{cases}]

[0080] Among them, core functions refer to functions that have a significant impact on the overall performance and user experience of the software, general functions refer to conventional functions of the software, which have a certain impact on the overall performance and user experience, and secondary functions refer to auxiliary functions, which have a small impact on the overall performance and user experience;

[0081] The formula for calculating the total functional working hours of a version is:

[0082] Total function work time of version = i = 1∑n (work time of function i)

[0083] Where n represents the total number of functions in the version, and the work hours of function i represent the working time required to complete function i;

[0084] Calculation formula for working hours quality points:

[0085] Working hours quality score = total functional working hours of the version / standard working hours

[0086] Among them, standard working hours represent the preset ideal working hours, which are used for standardized calculation;

[0087] Basic problem rate calculation formula:

[0088] Basic problem rate = number of basic problems / number of functions

[0089] Among them, the number of basic problems refers to the number of non-serious problems found during the test;

[0090] Deduction coefficient calculation formula:

[0091] [\text{Deduction coefficient}=\begin{cases}

[0092] 0&\text{If there is a blocking problem}\

[0093] 1&\text{If there is no blocking problem}

[0094] \end{cases}]

[0095] Among them, blocking issues represent problems that severely affect the functionality or performance of the software;

[0096] In this step, the identified key indicators are quantified, including the quantification of functional importance and the quantification of performance indicators. Weights are assigned to each indicator based on project characteristics and business needs to ensure that the evaluation method can adapt to the dynamic environment. The quantification methods and indicators ensure the quantifiability of non-functional requirements, simplify the quantification process, and reduce complexity.

[0097] Step S4, calculate the quality score of each function according to the quantified indicators and the assigned weights, and summarize the quality scores of all functions to obtain the quality score of the entire version. The calculation formula of the quality score of a single function is: quality score = work-hour quality score * importance coefficient * (1-basic problem rate) * deduction coefficient, where the basic problem rate and the deduction coefficient are determined according to the importance of the BUG information;

[0098] The calculation formula of single function quality score is:

[0099] Single function quality score = work-hour quality score × importance coefficient × (1-basic problem rate) × deduction coefficient

[0100] Among them, the work-hour quality score indicates the quality score calculated based on the total functional work-hours of the version, the importance coefficient indicates the coefficient determined based on the importance of the function, the basic problem rate indicates the problem rate calculated based on the number of basic problems, and the deduction coefficient indicates the coefficient determined based on whether there is a blocking problem;

[0101] The quality score calculation formula for the entire version is:

[0102] The quality score of the entire version = i = 1∑n (the quality score of a single function i)

[0103] Among them, the quality score of a single function i represents the quality score of each function calculated according to the above formula;

[0104] In this step, the individual function quality score of each function is calculated based on the quantified indicators and the assigned weights, and the individual function quality scores of all functions are summarized to obtain the quality score of the entire version. The calculation formula ensures that the evaluation results are consistent with the actual situation, simplifies the quality score calculation process, and reduces complexity. The quality score calculation ensures the accuracy and comprehensiveness of the evaluation results and adapts to dynamic environments.

[0105] Step S5, using a reporting tool on the cloud computing platform to generate a quality assessment report including a detailed quality score for each function and a quality score for the entire version, the reporting tool including a chart generator, a dashboard, and a report template to intuitively display the assessment results;

[0106] In this step, the reporting tool of the cloud computing platform is used to generate a detailed quality assessment report, including the detailed quality score of each function and the quality score of the entire version, to intuitively display the evaluation results. The reporting tool ensures rapid feedback and continuous improvement, simplifies the report generation process, and reduces complexity. The quality assessment report ensures that the evaluation method can adapt to the dynamic environment, establish a feedback loop, and ensure the effectiveness of quality improvement.

[0107] Based on the above comprehensive development evaluation method, assume that the development quality of a software version is being evaluated, which contains three functions: function A (core function), function B (general function) and function C (secondary function).

[0108] Assume the following parameters:

[0109] The working hours for function A are 100 hours, the working hours for function B are 80 hours, and the working hours for function C are 50 hours.

[0110] The standard working hours are set at 200 hours.

[0111] Function A found 2 basic problems, Function B found 1 basic problem, and Function C found 3 basic problems.

[0112] Function A has a blocking problem, while functions B and C do not have blocking problems.

[0113] Step S1: Data collection and integration

[0114] Collect the working hours data and problem data of functions A, B, and C, and integrate the data into the cloud computing platform.

[0115] Step S2: Data preprocessing and analysis

[0116] Clean, format and normalize data and identify key indicators using analytical tools on cloud computing platforms.

[0117] Step S3: Indicator quantification and weight allocation

[0118] Calculate the significance coefficient:

[0119] Importance coefficient A =1.2,importance coefficient B =1.0,importance coefficient C =0.8;

[0120] Calculate the total functional hours of a version:

[0121] Total functional work hours for the version = 100 + 80 + 50 = 230 hours;

[0122] Calculate the work time quality points:

[0123] Work-hour quality score = 230 / 200 = 1.15;

[0124] Calculate the basic problem rate:

[0125] Basic problem rate A =2 / 1=2, basic problem rate B =1 / 1=1, basic problem rate C =3 / 1=3;

[0126] Calculate the deduction factor:

[0127] Deduction coefficient A=0, deduction coefficient B=1, deduction coefficient C=1.

[0128] Step S4: Quality score calculation

[0129] Calculate the quality score of a single function:

[0130] Individual function quality score A =1.15×1.2×(1-2)×0=0;

[0131] Individual function quality score B =1.15×1.0×(1-1)×1=0;

[0132] Individual function quality score C =1.15×0.8×(1-3)×1=-1.84;

[0133] Sum up the individual function quality scores of all functions to get the quality score of the entire version:

[0134] The quality score of the entire version = 0 + 0 - 1.84 = -1.84.

[0135] Step S5: Quality assessment report generation

[0136] The report tool of the cloud computing platform is used to generate a quality assessment report, which includes the detailed quality score of each function and the quality score of the entire version.

[0137] like Figure 2-Figure 7 A system for developing a comprehensive evaluation method based on cloud computing technology is shown, comprising:

[0138] A data collection module, which is responsible for automatically collecting data on functional and non-functional requirements from the software development and testing process. The data collection module consists of a data source interface, a data grabber, and a data cache;

[0139] Specifically, the data collection module is connected to the data source through an API interface, and the data source includes but is not limited to code repositories, testing tools, performance monitoring, security scanning and user feedback systems. The data crawler captures data from each data source regularly or in real time, and stores the data in a data cache for subsequent processing.

[0140] A data processing module, which is used to pre-process and deeply analyze the collected data to identify key indicators. The data processing module consists of a data cleaning tool, a data formatter, a data normalizer, and an analysis engine;

[0141] Specifically, the data processing module receives data from the data collection module through the data pipeline Kafka or RabbitMQ, and passes the processed data to the indicator quantification and weight allocation module, where the data cleaning tool removes invalid and redundant data, the data formatter and normalizer convert the data into a unified format and dimension, and the analysis engine uses preset algorithms to conduct in-depth analysis of the data and identify key indicators.

[0142] A weight allocation module, which is used to quantify key indicators and allocate weights according to project characteristics and business needs. The weight allocation module consists of an indicator quantifier, a weight allocator, and a weight adjustment tool;

[0143] Specifically, the weight allocation module receives key indicator data from the data processing and analysis module through the data interface, and passes the quantified indicators and weights to the quality score calculation module, where the indicator quantifier converts the key indicators into quantifiable values, the weight allocator assigns weights to each indicator based on project characteristics and business needs, and the weight adjustment tool allows users to adjust the weights based on actual conditions.

[0144] A score calculation module, which is used to calculate the quality score of each function and the quality score of the entire version according to the quantified indicators and the assigned weights. The score calculation module is composed of a calculation engine and a quality score database;

[0145] Specifically, the score calculation module receives the quantified indicators and weights from the indicator quantification and weight allocation module through the data interface, and passes the calculated quality score to the report generation module, wherein the calculation engine uses a preset mathematical formula to calculate the quality score based on the quantified indicators and the assigned weights, and stores the results in the quality score database.

[0146] A report generation module, which is used to generate a detailed quality assessment report, including charts and dashboards, to intuitively display the assessment results. The report generation module consists of a report template library, a report generator, and a report distribution system;

[0147] Specifically, the report generation module receives quality score data from the quality score calculation module through the data interface, and distributes the generated report to relevant development team members, wherein the report generator generates a detailed quality assessment report based on the data in the quality score database using the template in the report template library, and sends the report to the target user through the report distribution system.

[0148] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0149] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A comprehensive development evaluation method based on cloud computing technology, characterized in that: The following steps are involved: Step S1, collecting data on functional requirements and non-functional requirements during software development and testing through automated tools, and integrating the data into a cloud computing platform; Step S2, cleaning, formatting and normalizing the data, and using analysis tools of the cloud computing platform to conduct in-depth analysis of the data to identify key indicators; Step S3, quantifying the key indicators, converting the functional importance into importance coefficients, converting the performance indicators into performance scores, and assigning weights to each indicator according to project characteristics and business requirements; Step S4, calculating the individual function quality score of each function according to the quantified indicators and the assigned weights, and summing up the individual function quality scores of all functions to obtain the quality score of the entire version; Step S5, using the reporting tool of the cloud computing platform to generate a quality assessment report including a detailed quality score of each function and a quality score of the entire version.

2. According to claim 1, a comprehensive development evaluation method based on cloud computing technology is characterized by: In step S1, the automation tools include but are not limited to code analysis tools, performance testing tools, security scanning tools and user experience feedback collection tools.

3. According to claim 1, a comprehensive development evaluation method based on cloud computing technology is characterized by: In step S2, the analysis tools of the cloud computing platform include data mining, machine learning and statistical analysis tools.

4. According to claim 1, a comprehensive development evaluation method based on cloud computing technology is characterized by: In step S3, the importance coefficient is determined based on the core, general or secondary nature of the function, and the performance score is calculated based on the response time, throughput and error rate indicators; Functional importance coefficient calculation formula: [\text{Importance coefficient}=\begin{cases} 1.2&\text{If the function is a core function}\ 1.0&\text{If the function is a general function}\ 0.8&\text{If the function is a secondary function} \end{cases}] Among them, core functions refer to functions that have a significant impact on the overall performance and user experience of the software, general functions refer to conventional functions of the software, which have a certain impact on the overall performance and user experience, and secondary functions refer to auxiliary functions, which have a small impact on the overall performance and user experience; The formula for calculating the total functional working hours of a version is: Total function work time of version = i = 1∑n (work time of function i) Where n represents the total number of functions in the version, and the work hours of function i represent the work time required to complete function i; Calculation formula for working hours quality points: Working hours quality score = total functional working hours of the version / standard working hours Among them, standard working hours represent the preset ideal working hours, which are used for standardized calculation; Basic problem rate calculation formula: Basic problem rate = number of basic problems / number of functions Among them, the number of basic problems refers to the number of non-serious problems found during the test; Deduction coefficient calculation formula: [\text{Deduction coefficient}=\begin{cases} 0&\text{If there is a blocking problem}\ 1&\text{If there is no blocking problem} \end{cases}] Among them, blocking issues refer to problems that seriously affect the functionality or performance of the software.

5. According to claim 1, a comprehensive development evaluation method based on cloud computing technology is characterized by: In step S4, the calculation formula of the quality score of a single function is: quality score = work-hour quality score * importance coefficient * (1-basic problem rate) * deduction coefficient, wherein the basic problem rate and the deduction coefficient are determined according to the importance of the BUG information; The calculation formula of single function quality score is: Single function quality score = work-hour quality score × importance coefficient × (1-basic problem rate) × deduction coefficient Among them, the work-hour quality score indicates the quality score calculated based on the total functional work-hours of the version, the importance coefficient indicates the coefficient determined based on the importance of the function, the basic problem rate indicates the problem rate calculated based on the number of basic problems, and the deduction coefficient indicates the coefficient determined based on whether there is a blocking problem; The quality score calculation formula for the entire version is: The quality score of the entire version = i = 1∑n (the quality score of a single function i) The quality score of a single function i represents the quality score of each function calculated according to the above formula.

6. According to claim 1, a comprehensive development evaluation method based on cloud computing technology is characterized by: In step S5, the reporting tool includes a chart generator, a dashboard and a report template to intuitively display the evaluation results.

7. A system for developing a comprehensive evaluation method based on cloud computing technology according to any one of claims 1 to 6, characterized in that: include A data collection module, which is responsible for automatically collecting data on functional and non-functional requirements from the software development and testing process; A data processing module, which is used to pre-process and deeply analyze the collected data to identify key indicators; A weight allocation module, which is used to quantify key indicators and allocate weights according to project characteristics and business needs; A score calculation module, which is used to calculate the quality score of each function and the quality score of the entire version according to the quantified indicators and the assigned weights; The report generation module is used to generate a detailed quality assessment report, including charts and dashboards, to intuitively display the assessment results.

8. The system for developing a comprehensive evaluation method based on cloud computing technology according to claim 7, characterized in that: The data collection module is composed of a data source interface, a data grabber and a data cache, and the data processing module is composed of a data cleaning tool, a data formatter, a data normalizer and an analysis engine.

9. The system for developing a comprehensive evaluation method based on cloud computing technology according to claim 7, characterized in that: The weight distribution module is composed of an indicator quantizer, a weight distributor and a weight adjustment tool, and the score calculation module is composed of a calculation engine and a quality score database.

10. The system for developing a comprehensive evaluation method based on cloud computing technology according to claim 7, characterized in that: The report generation module consists of a report template library, a report generator and a report distribution system.

Images