CN117851269A - Cloud-based automatic test environment management method and system - Google Patents

Abstract

The invention relates to the technical field of cloud automation, and discloses a cloud automation-based test environment management method and system, wherein the cloud automation-based test environment management method comprises the following steps: automatically deploying a containerized test environment containing dependencies required for testing on the cloud computing platform; splitting an application to be tested into independent micro-service units, and implementing independent tests for each unit; automatically triggering a test flow for each micro-service through a CI/CD tool; monitoring the performance of containers and micro services, and collecting and analyzing logs; and automatically adjusting the number of micro-service examples according to the performance monitoring result, and implementing a service deployment strategy. The automation degree and efficiency of the test flow are remarkably improved, independent service unit test and integrated test are supported, and high test accuracy and reliability are ensured. By dynamically adjusting the resource usage, the cost efficiency is optimized, and the flexibility and expandability of the test environment are improved. An efficient, economical and reliable test environment management solution is provided for modern software development.

Description

A cloud-based automated test environment management method and system

Technical Field

The present invention relates to the field of cloud automation technology, and specifically to a cloud-based automated testing environment management method and system.

Background technique

With the rapid evolution of software development processes, especially driven by agile development and DevOps culture, the demand for more efficient and flexible test environment management methods is growing.

Cloud computing, with its high elasticity, scalability, and on-demand service model, provides an ideal platform for software testing. It allows for rapid deployment and adjustment of test environments to accommodate testing needs of varying scale and complexity. Cloud platforms are able to provide a variety of computing resources, including CPU, memory, storage, and network resources, which can be dynamically adjusted based on demand to optimize cost and performance. Software testing automation is key to improving the efficiency and quality of the software development cycle. It reduces the need for manual testing, allows the implementation of continuous integration/continuous deployment (CI/CD) practices, and ensures software quality and rapid iteration. Automated testing includes multiple types, such as unit testing, integration testing, and performance testing, and is essential to ensuring the reliability and stability of software in various environments.

The microservices architecture improves flexibility and maintainability by breaking down an application into a set of small, loosely coupled services. However, this architecture also brings challenges in testing, as each microservice unit needs to be tested independently and ensure that they work properly when integrated. Testing microservices in a cloud environment requires effective service discovery, load balancing, and network configuration.

In traditional test environment management, manual configuration and maintenance of the test environment is often time-consuming, labor-intensive, and error-prone. Especially in cloud environments and microservice architectures, this management method is difficult to meet the needs of rapid deployment and flexible configuration. In addition, the lack of effective performance monitoring and resource optimization mechanisms is also a shortcoming of traditional methods.

The present invention provides a new test environment management method by integrating cloud computing and automated testing technologies. It can automatically deploy and configure containerized test environments, support independent and integrated testing of microservice units, and automate test processes by integrating CI/CD tools. In addition, the present invention also includes performance monitoring and log analysis functions, as well as an intelligent resource adjustment mechanism based on monitoring data, thereby ensuring efficient operation of the test environment and optimal utilization of resources.

Summary of the invention

In view of the above-mentioned problems, the present invention is proposed.

Therefore, the technical problem solved by the present invention is: how to manage the software testing environment efficiently, flexibly and automatically in a cloud computing environment.

To solve the above technical problems, the present invention provides the following technical solutions: A cloud-based automated test environment management method, comprising: automatically deploying a containerized test environment containing dependencies required for testing on a cloud computing platform;

Split the application to be tested into independent microservice units and implement independent tests for each unit;

Automatically trigger the testing process for each microservice through CI/CD tools;

Monitor the performance of containers and microservices and collect and analyze logs;

Automatically adjust the number of microservice instances based on performance monitoring results and implement service deployment strategies.

As a preferred solution of the cloud-based automated test environment management method described in the present invention, wherein: the containerized test environment includes:

When a new test environment needs to be created, the deployment script of the containerized environment is automatically executed. The script builds a container image containing all necessary dependencies and configurations based on the pre-defined Dockerfile;

When the container image is built, upload the image to the container repository of the cloud platform so that it can be pulled and deployed at any time in various test environments;

When a test task is triggered, the corresponding container image is automatically pulled through the CI/CD tool, and the container instance is started in the specified environment of the cloud computing platform for testing;

When the container instance is started, environment variables and dependent services are automatically configured to ensure that the test environment is consistent with the expected settings and can be used for testing immediately;

When the test task is completed or updated, the relevant container instance is automatically stopped and destroyed to release resources, and the container image is updated or redeployed as needed.

As a preferred solution of the cloud-based automated test environment management method of the present invention, wherein: splitting the application to be tested into independent microservice units includes:

When the functional modules of the application are identified as units that can be run and tested independently, they are split into separate microservice units, and API interfaces and service contracts are defined for each unit;

Once the microservice units are defined, an automated test script is written for each unit, covering its key functions and interactive interfaces;

When the automated test scripts are ready, they are integrated into the CI/CD process so that they can be automatically executed when the microservice code is updated.

When unit tests are triggered in the CI/CD process, they are run in independent, isolated test environments to ensure the accuracy of test results and the independence between microservices.

When the unit test is completed, the test results and performance data are collected to evaluate the functionality and stability of each microservice and provide a basis for subsequent optimization.

As a preferred solution of the cloud-based automated test environment management method described in the present invention, wherein: the automatic triggering of the test process for each microservice by the CI/CD tool includes, when the microservice code in the source code repository is updated, the test process configured in the CI/CD tool is automatically triggered, which includes pulling the latest code from the code repository, executing the build process, and running the automated test script;

When the automated test script starts executing, the corresponding microservice instance is deployed in the isolated test environment;

When the automated test execution is completed, the test results and performance indicators are collected and a test report is generated;

When testing finds errors, error reports and related logs are sent to the development team for timely repairs and re-triggering of the testing process as needed;

When all tests pass and meet the preset quality standards, the code changes are automatically merged into the main branch or marked as deployable in preparation for subsequent release steps.

As a preferred solution of the cloud-based automated test environment management method described in the present invention, wherein: the monitoring of the performance of the container and the microservice includes configuring a monitoring tool in the cloud environment to track the performance indicators of the microservice and the container in real time;

Deploy a log collection system to automatically collect, store, and index log information from all containers and microservices;

Implement intelligent alarm mechanism, which automatically triggers the alarm system and sends alarms through integrated notification when the monitored performance indicators exceed the preset threshold or predefined error patterns appear in the logs;

When the performance indicators are within the normal range and there are no critical errors or abnormal behaviors in the log, no alarm is triggered;

Use log analysis tools to analyze collected logs to quickly locate and resolve performance issues or system anomalies;

The collected performance data is displayed in a graphical form, providing an intuitive view and in-depth analysis of system performance.

As a preferred solution of the cloud-based automated test environment management method described in the present invention, wherein: the analysis of the collected logs includes extracting time series data of performance indicators from the logs, cleaning the data, and processing missing values and abnormal points;

Applying the ARIMA model Model the processed time series;

in, is the order of the autoregressive term which allows us to transform the past The values at each time point were used as predictor variables; Indicates the number of check points, used to stabilize time series data; /> is the order of the moving average term, which allows us to convert the past The forecast errors are used as predictors; historical data are used to estimate model parameters/> , and fit the ARIMA model;

Use the fitted ARIMA model to make predictions and generate performance indicator forecasts for a period of time in the future;

Calculate time points Prediction value/> and time points/> Actual observed value/> difference between, ;

in, It's time /> The difference value of

Set a threshold To judge the abnormality, if/> Exceeding the threshold /> , then it is considered that at time point /> An exception occurs;

The threshold The formula is the average value of historical difference data plus or minus twice the standard layer:

;

in, Indicates the average value of historical difference data, /> Represents standard deviation.

As a preferred solution of the cloud-based automated test environment management method described in the present invention, wherein: the automatic adjustment of the number of microservice instances according to the performance monitoring results includes:

Determine CPU usage , memory usage/> , Disk I/O usage/> and network bandwidth usage/> To monitor indicators;

Collect performance data of each microservice instance in real time to determine whether the automatic expansion threshold and automatic reduction threshold are reached;

When any of the monitoring indicators Exceeding the upper threshold /> , it means that the current resources are insufficient to meet the performance requirements, so increase the number of microservice instances;

When any of the monitoring indicators Exceeding the lower threshold /> , indicating that the current resource usage is insufficient, the number of microservice instances is reduced to optimize resource usage;

Set the decision algorithm for automatically adjusting the number of instances to calculate the number of instances after adjustment. The formula is:

;

in, Indicates the number of instances after adjustment,/> Indicates the current number of instances, /> Indicates the adjustment sensitivity coefficient, /> Indicates the current comprehensive performance index, /> represents the target performance indicator;

Based on the calculated number of new instances , dynamically increase or decrease microservice instances;

Adjust service deployment strategies, including network configuration, load balancing, and resource allocation, to accommodate the new number of instances and optimize overall performance.

A cloud-based automated test environment management system, characterized by: including:

Automatic deployment module: Automatically deploys a containerized test environment containing dependencies required for testing on a cloud computing platform;

Microservice separation: Split the application to be tested into independent microservice units and implement independent testing for each unit;

CI/CD integration module: automatically triggers the test process for each microservice through CI/CD tools;

Performance monitoring module: monitors the performance of containers and microservices and collects and analyzes logs;

Automatic optimization: The module automatically adjusts the number of microservice instances based on performance monitoring results and implements service deployment strategies.

A computer device comprises a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

A computer-readable storage medium stores a computer program, which implements the steps of the method described above when executed by a processor.

The beneficial effects of the present invention: significantly improve the automation level and efficiency of the test process. By automating the deployment and configuration of the containerized test environment, it reduces the need for manual configuration and accelerates the software development and testing cycle. Particularly suitable for applications with microservice architectures, the present invention supports independent service unit testing and integration testing, ensuring a high degree of test accuracy and reliability. Combined with real-time performance monitoring and log analysis, it makes problem diagnosis faster and more accurate. By dynamically adjusting resource usage, cost efficiency is optimized, while improving the flexibility and scalability of the test environment. It provides an efficient, economical and reliable test environment management solution for modern software development.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

FIG1 is an overall flow chart of a cloud-based automated testing environment management method provided by the first embodiment of the present invention.

Detailed ways

In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

Example 1

Referring to FIG. 1 , an embodiment of the present invention provides a cloud-based automated test environment management method, including:

S1: Automatically deploy a containerized test environment containing the dependencies required for testing on a cloud computing platform.

When a new test environment needs to be created, the deployment script of the containerized environment is automatically executed. The script builds a container image containing all necessary dependencies and configurations based on the pre-defined Dockerfile.

When the container image is built, it is uploaded to the container repository of the cloud platform so that it can be pulled and deployed at any time in various test environments.

When a test task is triggered, the corresponding container image is automatically pulled through the CI/CD tool, and the container instance is started in the specified environment of the cloud computing platform for testing;

When the container instance is started, environment variables and dependent services are automatically configured to ensure that the test environment is consistent with the expected settings and can be used for testing immediately.

When the test task is completed or updated, the relevant container instance is automatically stopped and destroyed to release resources, and the container image is updated or redeployed as needed.

Furthermore, the deployment script will use an efficient layer cache mechanism to ensure that unchanged layers are reused when building container images, thereby reducing build time and network bandwidth consumption. After the container image is uploaded to the cloud platform, image tag management is implemented to facilitate version tracking and rapid location. An image rollback mechanism is designed so that when a problem occurs with the new version, it can be quickly switched back to the stable old version.

Furthermore, in the CI/CD process, a feedback mechanism for automated test results is integrated to quickly respond and take action when a test fails.

S2: Split the application to be tested into independent microservice units and perform independent tests for each unit.

When the functional modules of the application are identified as units that can be run and tested independently, they are split into separate microservice units, and API interfaces and service contracts are defined for each unit.

Once the microservice units are defined, automated test scripts are written for each unit, covering its key functions and interactive interfaces.

When the automated test scripts are ready, they are integrated into the CI/CD process to automatically execute these tests whenever the microservice code is updated.

When unit tests are triggered in the CI/CD process, they are run in a separate, isolated test environment to ensure the accuracy of the test results and the independence between microservices.

When the unit test is completed, the test results and performance data are collected to evaluate the functionality and stability of each microservice and provide a basis for subsequent optimization.

Furthermore, when splitting the application into microservice units, it is necessary to accurately define the responsibilities and boundaries of each unit. For each microservice unit, a strict service contract must be formulated, including the definition of the API interface, data format, error handling mechanism, etc.

It should be noted that unit tests should be run in an environment that is as consistent as possible with the production environment. This includes the same service configuration, similar network conditions, and similar data sets to ensure the validity and reliability of the test results. Integrate microservice unit test scripts into the continuous integration (CI) process to ensure that these tests are automatically executed after each code update. This requires the CI process to efficiently handle the testing requirements of multiple service units and to provide timely feedback on test results.

S3: Automatically trigger the testing process for each microservice through CI/CD tools.

When the microservice code in the source code repository is updated, the test process configured in the CI/CD tool is automatically triggered, which includes pulling the latest code from the code repository, executing the build process, and running automated test scripts.

When the automated test script starts executing, the corresponding microservice instance is deployed in an isolated test environment to ensure that the test is performed under conditions as close to the production environment as possible.

When the automated test execution is completed, the test results and performance indicators are collected and a test report is generated.

When the test finds errors or fails, the error report and related logs are sent to the development team for timely repair and re-triggering of the test process as needed.

When all tests pass and meet the preset quality standards, the code changes are automatically merged into the main branch or marked as deployable in preparation for subsequent release steps.

It should be noted that CI/CD tools are the core of the modern software development process. They automate the entire life cycle of code from development to deployment, ensuring rapid iteration and high quality of software development.

S4: Monitor the performance of containers and microservices and collect and analyze logs.

Configure monitoring tools in cloud environments to track performance metrics of microservices and containers in real time.

Deploy a log collection system to automatically collect, store, and index log information from all containers and microservices.

Implement an intelligent alarm mechanism. When the monitored performance indicators exceed the preset thresholds or predefined error patterns appear in the logs, the alarm system is automatically triggered and an alarm is sent through integrated notifications.

When the performance indicators are within the normal range and there are no critical errors or abnormal behaviors in the logs, no alarm is triggered.

Use log analysis tools to analyze collected logs to quickly locate and resolve performance issues or system anomalies.

The collected performance data is displayed in a graphical form, providing an intuitive view and in-depth analysis of system performance.

Extract time series data of performance indicators from logs, clean the data, and process missing values and outliers.

Applying the ARIMA model Model the processed time series.

in, is the order of the autoregressive term which allows us to transform the past The values at each time point were used as predictor variables; Indicates the number of check points, used to stabilize time series data; /> is the order of the moving average term, which allows us to convert the past The forecast errors are used as predictors; historical data are used to estimate model parameters/> , and fit the ARIMA model.

Use the fitted ARIMA model to make predictions and generate performance indicator forecasts for a period of time in the future;

Furthermore, the ARIMA model fitting process includes:

Collect data: First, collect time series data, which is historical data about microservice performance indicators, such as CPU usage, memory usage, etc.

Data cleaning: Process missing values and outliers to ensure data integrity and accuracy.

Stationarity test: Check the stationarity of the time series data. If the data is non-stationary, perform difference processing until the data is stationary.

Parameter selection: Determining the order of the autoregressive term (AR) ：You can use the autocorrelation function (ACF) graph to help determine the number of differences. ：Determined based on the results of the stationarity test. Determine the order of the moving average (MA)/> : Assisted by observing the partial autocorrelation function (PACF) graph.

Estimate model parameters: Use statistical software (such as R or Python's statsmodels library) to estimate the parameters of the ARIMA model. Minimize the error to find the best-fitting model parameters.

Model diagnostics: Examine the residual series to ensure the adequacy of the model fit.

Use various statistical tests (such as the Ljung-Box test) and graphical analysis (such as the ACF plot of the residuals) to assess the validity of the model.

Model prediction: Use the fitted ARIMA model to predict future performance indicators. Generate performance indicator forecasts for a period of time in the future and make corresponding resource adjustment decisions based on them.

Calculate prediction error and anomaly detection: Calculate the difference between the actual observed value and the predicted value. Set a threshold to determine whether there is an anomaly based on the prediction error.

It should be noted that the ARIMA (Autoregressive Integrated Moving Average) model is a commonly used time series prediction method, which combines the three methods of autoregression (AR), difference (I) and moving average (MA), and can effectively process non-stationary time series data. In the present invention, the ARIMA model is used to predict the performance indicators of microservices and help predict future resource requirements and performance trends. The setting of the threshold is based on the statistical analysis of historical data, and is usually determined by adding or subtracting two standard deviations from the mean value of historical difference data. This method can reduce false alarms while maintaining sensitivity and ensure the accuracy of anomaly detection.

Calculate time points Prediction value/> and time points/> Actual observed value/> difference between, ;

in, It's time /> The difference value of

Set a threshold To judge the abnormality, if/> Exceeding the threshold /> , then it is considered that at time point /> An exception occurs;

The threshold The formula is the average value of historical difference data plus or minus twice the standard layer:

;

in, Indicates the average value of historical difference data, /> Represents standard deviation.

S5: Automatically adjust the number of microservice instances based on performance monitoring results and implement service deployment strategies.

Determine CPU usage , memory usage/> , Disk I/O usage/> and network bandwidth usage/> For monitoring indicators.

Collect performance data of each microservice instance in real time to determine whether the automatic expansion threshold and automatic reduction threshold are reached.

When any of the monitoring indicators Exceeding the upper threshold /> , it means that the current resources are insufficient to meet the performance requirements, so increase the number of microservice instances;

When any of the monitoring indicators Exceeding the lower threshold /> , indicating that the current resource usage is insufficient, the number of microservice instances is reduced to optimize resource usage;

Set the decision algorithm for automatically adjusting the number of instances to calculate the number of instances after adjustment. The formula is:

;

in, Indicates the number of instances after adjustment,/> Indicates the current number of instances, /> Indicates the adjustment sensitivity coefficient, /> Indicates the current comprehensive performance index, /> Represents the target performance indicator.

Based on the calculated number of new instances , dynamically increase or decrease microservice instances;

Adjust service deployment strategies, including network configuration, load balancing, and resource allocation, to accommodate the new number of instances and optimize overall performance.

The above embodiments also include a cloud-based automated test environment management system, specifically:

Automated deployment module: Automatically deploys a containerized test environment containing the dependencies required for testing on the cloud computing platform.

Microservice separation: Split the application to be tested into independent microservice units and implement independent tests for each unit.

CI/CD integration module: automatically triggers the testing process for each microservice through CI/CD tools.

Performance monitoring module: monitors the performance of containers and microservices, and collects and analyzes logs.

Automatic optimization: The module automatically adjusts the number of microservice instances based on performance monitoring results and implements service deployment strategies.

The computer device may be a server. The computer device includes a processor, a memory, an input/output interface (Input/Output, referred to as I/O) and a communication interface. The processor, the memory and the input/output interface are connected via a system bus, and the communication interface is connected to the system bus via the input/output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data cluster data of the power monitoring system. The input/output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a cloud-based automated test environment management method is implemented.

A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

Example 2

As an embodiment of the present invention, a cloud-based automated test environment management method is provided. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation/comparative experiments.

Compare the differences between the cloud-based automated test environment management method (hereinafter referred to as the "new method") and the traditional test environment management method (hereinafter referred to as the "traditional method") in terms of deployment efficiency, resource utilization, system stability, and test coverage.

Test subjects: 10 microservice applications, each with the same size, complexity, and history.

Testing period: 30 days.

Performance parameters: During the test cycle, various performance parameters are recorded and analyzed, including deployment time, CPU and memory utilization, number of system failures, etc.

Deployment testing: Record the time from code submission to application deployment being fully ready under both the new and traditional methods.

Resource Utilization Test: Monitor and record CPU and memory utilization under two methods.

Stability test: record the number of failures and system response time of each application during the test cycle.

Test coverage evaluation: Compare the test coverage of the two methods, including the application function points and code coverage covered by automated tests.

Data Collection: Use automated tools and scripts to collect data during testing.

Analytical methods: Statistical analysis was performed on the collected data to evaluate the performance of the new method compared with the traditional method on various indicators and calculate the improvement percentage.

For a comparison of system performance and resource utilization, please refer to Table 1:

Table 1 Comparison of system performance and resource utilization

The present invention reduces deployment time by 55.6%, from 18 minutes to 8 minutes. This significant improvement demonstrates the advantages of the present invention in improving the efficiency of the deployment process, which is particularly important for modern software development that requires rapid iteration and deployment. The present invention increases CPU utilization by 20% and memory utilization by 15.3%, from 65% and 72% to 78% and 83%, respectively. This shows that the present invention can more effectively utilize hardware resources and improve the overall performance and efficiency of the system. The improvement in network bandwidth utilization (from 55% to 68%, an increase of 23.6%) reflects the optimization of the present invention in network resource management, which is particularly important for data-intensive applications. The system response time was reduced from 1.1 seconds to 0.7 seconds, a reduction of 36.4%. This improvement directly affects the user experience, and fast response time is critical to user satisfaction.

Let’s compare the test efficiency and system stability, see Table 2:

Table 2 Comparison of test efficiency and system stability

The present invention increases the test coverage from 75% to 92%, an increase of 22.7%. This shows that the present invention can detect and verify software functions more comprehensively and reduce the risks caused by untested code. The significant reduction in the failure rate (from 2 failures per month to 0.5 failures per month, a reduction of 75%) shows the significant effect of the present invention in improving system stability. The improvement in automated testing efficiency (from 65% to 88%, an increase of 35.4%) reflects the advantages of the present invention in automating the test process and improving efficiency. The significant reduction in fault detection time (from 3 hours to 45 minutes, a reduction of 75%) shows the effectiveness of the present invention in quickly identifying and responding to system problems. The improvement in user satisfaction (from 80% to 95%, an increase of 18.8%) shows the significant effect of the present invention in improving user experience and satisfaction.

In summary, these data show that the present invention has significant advantages in improving the comprehensiveness of testing, reducing system failures, improving test efficiency, accelerating fault detection and processing speed, and improving user satisfaction. These improvements are of great significance to improving the overall quality and efficiency of software development and maintenance.

It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims (10)

1. The cloud-based automatic test environment management method is characterized by comprising the following steps of:

automatically deploying a containerized test environment containing dependencies required for testing on the cloud computing platform;

splitting an application to be tested into independent micro-service units, and implementing independent tests for each unit;

automatically triggering a test flow for each micro-service through a CI/CD tool;

monitoring the performance of containers and micro services, and collecting and analyzing logs;

and automatically adjusting the number of micro-service examples according to the performance monitoring result, and implementing a service deployment strategy.

2. The cloud-based automated test environment management method of claim 1, wherein: the containerized testing environment includes,

when a new test environment needs to be created, a deployment script of the containerized environment is automatically executed, and the script constructs a container mirror image containing all configurations based on a predefined Dockerfile;

constructing the container mirror image containing all the configurations comprises automatically executing a preset script to modify Dockerfile when the testing requirement is changed due to the change in the cloud environment so as to adjust the configuration of the container environment and ensure that the container environment is consistent with the current testing requirement;

according to real-time performance monitoring data provided by the cloud platform, automatically updating resource limiting parameters in the Dockerfile to match actual service load requirements;

presetting an instruction for executing an automatic test script in Dockerfile, so that quality verification is immediately carried out after each time of constructing a container mirror image;

automatically adding a version tag to each constructed container image, and automatically triggering a process of rolling back to a previous stable version when a construction or test defect is found;

by tightly combining the construction and deployment process of the container mirror image with the CI/CD flow, the automatic reconstruction and updating of the container mirror image during the updating of codes or the change of environment are completed;

when the construction of the container mirror image is completed, uploading the mirror image to a container warehouse of a cloud platform, so that the mirror image is pulled and deployed in each test environment at any time;

when a test task is triggered, automatically pulling the corresponding container mirror image through a CI/CD tool, and starting a container instance in a designated environment of a cloud computing platform to test;

when the container instance is started, automatically configuring environment variables and dependent services, ensuring that the test environment is consistent with the expected setting and can be immediately used for testing;

when the test task is completed or updated, the related container instance is automatically stopped and destroyed, resources are released, and the container image is updated or redeployed as required.

3. The cloud-based automated test environment management method of claim 2, wherein: splitting the application to be tested into separate said micro-service units comprises,

when the functional module of the application is identified as an independently operable and testable unit, then splitting it into individual micro-service units and defining an API interface and service contract for each unit;

when the micro service units are defined, writing an automatic test script for each unit, and covering key functions and interaction interfaces of the units;

when the automated test script is ready, it is integrated into the CI/CD flow, causing it to automatically perform these tests upon micro-service code updates;

when the unit tests are triggered in the CI/CD flow, the tests are run in independent and isolated test environments, so that the accuracy of test results and the independence between micro services are ensured;

and after the unit test is completed, collecting test results and performance data.

4. The cloud-based automated test environment management method of claim 3, wherein: the automatic triggering of the test flow for each micro service through the CI/CD tool comprises that when the micro service code in the source code warehouse is updated, the test flow configured in the CI/CD tool is automatically triggered, and the method comprises the steps of pulling the latest code from the code warehouse, executing the construction process and running an automatic test script;

when the automatic test script starts to be executed, corresponding micro-service examples are deployed in the isolated test environment;

when the automatic test execution is completed, collecting test results and performance indexes, and generating a test report;

when the test finds an error, an error report and a related log are sent to a development team, and a test flow is retriggered according to the requirement;

when all tests pass and a preset quality criterion is met, the code changes are automatically merged into the main branch and marked as a deployable state.

5. The cloud-based automated test environment management method of claim 4, wherein: the monitoring of the performance of the container and the micro-service comprises the steps of configuring a monitoring tool in a cloud environment, and tracking the performance indexes of the micro-service and the container in real time;

a log collection system is deployed to automatically collect, store and index log information from all containers and micro services;

implementing an intelligent alarm mechanism, automatically triggering an alarm system when the monitored performance index exceeds a preset threshold value or a predefined error mode appears in a log, and sending an alarm in an integrated notification mode;

when the performance index is in a normal range and the irrelevant key in the log is wrong or abnormal, the alarm is not triggered;

analyzing the collected logs by using a log analysis tool so as to quickly locate and solve performance problems or system anomalies;

the collected performance data is graphically presented to provide a view of the system performance.

6. The cloud-based automated test environment management method of claim 5, wherein: the analysis of the collected logs comprises the steps of extracting time series data of performance indexes from the logs, cleaning the data and processing missing values and abnormal points;

using ARIMA modelsModeling the processed time sequence number;

wherein,representing the order of the autoregressive term, allowing the past +.>The values of the individual time points are used as prediction variables; />Representing the number of rounds of the score for stabilizing the time series data; />Representing the order of the moving average term, allows to pass +.>The individual prediction errors are used as prediction variables; estimating model parameters using historical data +.>Fitting an ARIMA model;

predicting by using the fitted ARIMA model to generate a performance index predicted value;

calculating a time pointPredictive value->And time point->Actual observations +.>Difference between->；

Wherein,is the time point->Is a difference value of (2);

setting a threshold valueTo judge abnormality if->Exceeding threshold->Then consider +.>An abnormality occurs;

the threshold valueThe standard layer added and subtracted by two times through the average value of the historical difference data is expressed as the following formula:

；

wherein,mean value representing historical difference data, +.>Representing standard deviation.

7. The cloud-based automated test environment management method of claim 6, wherein: the automatically adjusting the number of micro-service examples according to the performance monitoring result comprises,

determining CPU utilizationMemory usage->Disk I/O utilization +.>And network bandwidth utilizationIs a monitoring index;

collecting performance data of each micro service instance in real time, and judging whether an automatic expansion threshold and an automatic reduction threshold are reached;

when any one of the monitoring indexesExceeding the upper threshold +.>When the current resource does not meet the performance requirement, increasing the number of micro service examples;

when any one of the monitoring indexesExceeding the lower threshold->Indicating that the current resource is not used enough, reducing the number of micro service examples and optimizing the resource use;

setting a decision algorithm for automatically adjusting the number of instances to calculate the number of instances after adjustment, wherein the formula is as follows:

；

wherein,representation adjustmentNumber of instances later->Representing the current instance number, +.>Representing the adjustment sensitivity coefficient>Representing the current overall performance index,/-, for example>Representing a target performance index;

according to the calculated number of new instancesDynamically increasing or decreasing micro service instances;

service deployment policies, including network configuration, load balancing, and resource allocation, are adjusted to accommodate the new number of instances and optimize overall performance.

8. A cloud-based automated test environment management system employing the method of any of claims 1-7, wherein:

an automatic deployment module: automatically deploying a containerized test environment containing dependencies required for testing on the cloud computing platform;

micro-service separation: splitting an application to be tested into independent micro-service units, and implementing independent tests for each unit;

CI/CD integration module: automatically triggering a test flow for each micro-service through a CI/CD tool;

and a performance monitoring module: monitoring the performance of containers and micro services, and collecting and analyzing logs;

automatic optimization: and the module automatically adjusts the number of micro-service examples according to the performance monitoring result and implements a service deployment strategy.

9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 7 when the computer program is executed.

10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 7.