Software development-based state monitoring device and system

Abstract

The invention discloses a state monitoring device and system based on software development, belongs to the technical field of state monitoring, and aims to solve the problem that an existing test system only analyzes current tested data and lacks effective utilization of historical data, so that a result is inaccurate. The system comprises a data acquisition module, a data storage module, a data analysis module, a result display module and a system management module, by setting the data acquisition module, after each software test, result data is stored, the newly stored data is deeply analyzed by using the data analysis module, and the result display module displays the result data. Historical data is called in the analysis process to assist analysis, the defects of an existing analysis method are reduced, the analysis result is more comprehensive and accurate, the change trend of software performance along with time and potential systematic problems can be found in time, and the software development accuracy is improved through application of the historical data.

Description

Technical Field

[0001] The present invention relates to the technical field of state monitoring, and in particular to a state monitoring device and system based on software development. Background Art

[0002] In today's digital age, the scale and complexity of software development continues to grow. With the widespread application of software in various fields, the requirements for software quality, performance, and stability are also getting higher and higher. In the software development process, accurately monitoring the status of the software is crucial to ensuring the quality of the software and timely discovering potential problems.

[0003] Traditional software development status monitoring methods often have some limitations. They only rely on the data after the current test for analysis, and lack the effective use of historical data, which makes the analysis results not comprehensive and accurate, and it is difficult to find the trend of software performance changes over time and potential systemic problems.

[0004] In order to solve these problems, there is an urgent need for an innovative status monitoring device and system based on software development. Summary of the invention

[0005] The purpose of the present invention is to provide a state monitoring device and system for software development, which solves the problem that the existing test system in the background technology only analyzes the data after the current test, lacks effective use of historical data, and leads to inaccurate results.

[0006] To achieve the above object, the present invention provides the following technical solution: a state monitoring system based on software development, comprising:

[0007] Data collection module: used to collect various status data of the software in real time during the software development process. The status data includes running time, memory usage, CPU usage and response time. It is also used to collect test result data after each software test, including function test pass status, performance test indicators and security test vulnerabilities.

[0008] Data storage module: The data storage module includes current data storage and historical data storage. It stores the data collected after each test, using one or more of the database and file system to store the previous test data and software operation status data for subsequent analysis.

[0009] Data analysis module: The work of the data analysis module includes four steps: data preprocessing, real-time data analysis, historical data analysis, and comprehensive analysis. The data analysis module first cleans and preprocesses the collected data to remove noise and outliers, then analyzes the data of this test to evaluate the current status and performance of the software, and then calls historical data for auxiliary analysis, compares the difference between this data and historical data, finds out the trend and law of software performance changes, and finally combines the results of real-time data analysis and historical data analysis to obtain a comprehensive software status evaluation and optimization suggestions;

[0010] Result display module: used to visualize the analysis results in the form of charts, reports, etc., and also used to generate detailed analysis reports, including test results, data analysis process, problem findings and optimization suggestions;

[0011] System management module: used for user management, configuration management and log management;

[0012] The data analysis module specifically includes the following steps:

[0013] S1. Data initialization: define the time interval T for data collection and define the state parameter set S = {s1, s2, ..., s n}, initialize the historical data storage container H, which is used to store the collected data in the past period of time, and determine the similarity threshold θ, which is used to judge the similarity between the current data and the historical data;

[0014] S2. Real-time data collection: Data collection is performed at time intervals T. For each state parameter s i , the data collected at time t is recorded as d i,t , the collected data is formed into a data vector D t =(d 1,t ,d n,t ,…,d n,t );

[0015] S3. Data preprocessing: perform outlier detection on the collected data, use a box plot-based method to calculate the lower quartile Q1, median Q2, and upper quartile Q3 of each state parameter, define the outlier range, and if the data point exceeds the range, it is considered an outlier, marked and replaced, and the data is normalized. Use the min-max normalization method to map the data to the [0,1] interval. The formula is: in Represents the normalized data, min(S i ) and max(S i ) represent the parameters s i The minimum and maximum values ​​of

[0016] S4. Data fusion: The weighted average method is used for data fusion. For each state parameter s i , assign a weight w i ,satisfy The fused data

[0017] S5. Historical data matching analysis: Calculate the current fusion data F t The similarity with each historical fusion data in the historical data storage container H, if it is found t The historical fusion data whose similarity is greater than the similarity threshold θ is denoted as F t-hist ;

[0018] S6. Data correction: Based on historical fusion data F t-hist The structure and features of the current fusion data F t The corrected fusion data is recorded as F t,co ;

[0019] S7. Historical data update: Update the fused data collected and processed this time t,co Stored in the historical data storage container H.

[0020] Furthermore, in step S3, the range of abnormal values ​​is less than Q1-1.5×IQR or greater than Q3+1.5×IQR, where IQR=Q3-Q1.

[0021] Furthermore, in step S5, the Euclidean distance calculation method is used to calculate the current fusion data F t The similarity with each historical fusion data in the historical data storage container H is calculated as follows: for the current fusion data F t =(f t,1 ,f t,n ,…,f t,n ) and historical fusion data F hist =(f hist,1 ,f hist,2 ,…,f hist,n ), the Euclidean distance calculation formula is The similarity is also defined as The smaller the distance, the higher the similarity.

[0022] Furthermore, in step S6, the weighted average method is used for correction, and the corrected fusion data F t,co =α×F t +(1-α)×F t-hist , where α is the correction weight, which is adjusted according to the relative importance of current data and historical data. If the current data is more reliable, increase the α value; if the historical data is more valuable for reference, decrease the α value.

[0023] Furthermore, the data acquisition module adopts embedded sensor technology, and the sensors deployed in the hardware devices where the software runs monitor the temperature, voltage and current parameters of the hardware in real time, indirectly reflecting the software's use of hardware resources and the stability of the software operation; using the performance monitoring interface provided by the operating system, the software's memory usage is collected in real time, including the usage of physical memory and virtual memory. The data acquisition module also collects the software's network traffic, network delay and data packet loss rate indicators in real time through network monitoring equipment; the data acquisition module measures the response time at key nodes where the software interacts with the user, including the loading time of the user interface and the operation response time, by installing a response time measurement tool.

[0024] Furthermore, the data storage module uses a relational database management system to store the collected data in a structured manner, and uses ETL data warehouse technology to extract, convert and load historical data from the original data source into the data warehouse.

[0025] Furthermore, the system management module provides fine-grained control of user permissions and performs permission control on each function and data to ensure that users can only access functions and data for which they have permission. The system management module is also used to configure system parameters, including data collection frequency and storage path. The frequency of data collection can be adjusted according to software performance and resource usage to avoid excessive impact on software performance; the storage path and storage method can be adjusted according to data storage requirements to improve data storage efficiency and reliability.

[0026] Compared with the prior art, the present invention has the following beneficial effects:

[0027] The present invention provides a status monitoring device and system for software development. By setting a data acquisition module, the result data is stored after each software test. The data analysis module is used to conduct in-depth analysis on the newly stored data, and historical data is called to assist in the analysis during the analysis process, thereby reducing the defects of existing analysis methods and making the analysis results more comprehensive and accurate. The software performance change trend over time and potential systemic problems can be discovered in a timely manner. The use of historical data improves the accuracy of software development. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 It is a schematic diagram of the device module of the present invention;

[0029] Figure 2 It is a schematic diagram of the system module of the present invention;

[0030] Figure 3 It is a logical step diagram of the present invention. DETAILED DESCRIPTION

[0031] 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.

[0032] In order to solve the technical problem that the existing test system only analyzes the data after the current test and lacks effective use of historical data, resulting in inaccurate results, such as Figure 1-Figure 3 As shown, the following preferred technical solutions are provided:

[0033] A state monitoring system based on software development, comprising:

[0034] Data collection module: used to collect various status data of the software in real time during the software development process. The status data includes running time, memory usage, CPU usage and response time. It is also used to collect test result data after each software test, including function test pass status, performance test indicators and security test vulnerabilities.

[0035] Data storage module: The data storage module includes current data storage and historical data storage. It stores the data collected after each test, using one or more of the database and file system to store the previous test data and software operation status data for subsequent analysis.

[0036] Data analysis module: The work of the data analysis module includes four steps: data preprocessing, real-time data analysis, historical data analysis, and comprehensive analysis. The data analysis module first cleans and preprocesses the collected data to remove noise and outliers, then analyzes the data of this test to evaluate the current status and performance of the software, and then calls historical data for auxiliary analysis, compares the difference between this data and historical data, finds out the trend and law of software performance changes, and finally combines the results of real-time data analysis and historical data analysis to obtain a comprehensive software status evaluation and optimization suggestions;

[0037] Result display module: used to visualize the analysis results in the form of charts, reports, etc., and also used to generate detailed analysis reports, including test results, data analysis process, problem findings and optimization suggestions;

[0038] System management module: used for user management, configuration management and log management;

[0039] The data analysis module specifically includes the following steps:

[0040] S1. Data initialization: define the time interval T for data collection and define the state parameter set S = {s1, s2, ..., s n}, initialize the historical data storage container H, which is used to store the collected data in the past period of time, and determine the similarity threshold θ, which is used to judge the similarity between the current data and the historical data;

[0041] S2. Real-time data collection: Data collection is performed at time intervals T. For each state parameter s i , the data collected at time t is recorded as d i,t , the collected data is formed into a data vector D t =(d 1,t ,d n,t ,…,d n,t );

[0042] S3. Data preprocessing: perform outlier detection on the collected data, use a box plot-based method to calculate the lower quartile Q1, median Q2, and upper quartile Q3 of each state parameter, define the outlier range, and if the data point exceeds the range, it is considered an outlier, marked and replaced, and the data is normalized. Use the min-max normalization method to map the data to the [0,1] interval. The formula is: in Represents the normalized data, min(S i ) and max(S i ) represent the parameters s i The minimum and maximum values ​​of

[0043] S4. Data fusion: The weighted average method is used for data fusion. For each state parameter s i , assign a weight w i ,satisfy The fused data

[0044] S5. Historical data matching analysis: Calculate the current fusion data F t The similarity with each historical fusion data in the historical data storage container H, if it is found t The historical fusion data whose similarity is greater than the similarity threshold θ is denoted as F t-hist ;

[0045] S6. Data correction: Based on historical fusion data F t-hist The structure and features of the current fusion data F t The corrected fusion data is recorded as F t,co ;

[0046] S7. Historical data update: Update the fused data collected and processed this time t,co Stored in the historical data storage container H.

[0047] In step S3, the outlier range is less than Q1-1.5×IQR or greater than Q3+1.5×IQR, where IQR=Q3-Q1.

[0048] In step S5, the Euclidean distance calculation method is used to calculate the current fusion data F t The similarity with each historical fusion data in the historical data storage container H is calculated as follows: for the current fusion data F t =(f t,1 ,f t,n ,…,f t,n ) and historical fusion data F hist =(f hist,1 ,f hist,2 ,…,f hist,n ), the Euclidean distance calculation formula is The similarity is also defined as The smaller the distance, the higher the similarity.

[0049] In step S6, the weighted average method is used for correction, and the corrected fusion data F t,co =α×F t +(1-α)×F t-hist , where α is the correction weight, which is adjusted according to the relative importance of current data and historical data. If the current data is more reliable, increase the α value; if the historical data is more valuable for reference, decrease the α value.

[0050] The data acquisition module uses embedded sensor technology. The sensors deployed in the hardware devices where the software runs monitor the temperature, voltage and current parameters of the hardware in real time, indirectly reflecting the software's use of hardware resources and the stability of the software's operation. The performance monitoring interface provided by the operating system is used to collect the software's memory usage in real time, including the usage of physical memory and virtual memory.

[0051] The data collection module also uses network monitoring equipment to collect the software's network traffic, network delay and packet loss rate indicators in real time; the data collection module installs response time measurement tools to measure response time at key nodes of software and user interaction, including user interface loading time and operation response time. The data storage module uses a relational database management system to store the collected data in a structured manner, and uses ETL data warehouse technology to extract, convert and load historical data from the original data source into the data warehouse.

[0052] The system management module provides fine-grained control of user permissions and performs permission control on each function and data to ensure that users can only access functions and data for which they have permission. The system management module is also used to configure system parameters, including data collection frequency and storage path. The frequency of data collection can be adjusted according to software performance and resource usage to avoid excessive impact on software performance. The storage path and storage method can be adjusted according to data storage requirements to improve data storage efficiency and reliability.

[0053] In order to further and better explain the above embodiments, the present invention also provides an implementation scheme, a status monitoring device based on software development, including: a measurement and control host, the measurement and control host is used to collect data of the monitored software, the measurement and control host includes an embedded sensor chip, a storage device, a display, a processing chip and an alarm, and also includes a remote control device and a control console, the remote control device is connected to the measurement and control host through a communication network interface, and the control console is connected to the measurement and control host through a human-machine interface.

[0054] 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.

[0055] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A status monitoring system for software development, characterized in that: include: Data collection module: used to collect various status data of the software in real time during the software development process. The status data includes running time, memory usage, CPU usage and response time. It is also used to collect test result data after each software test, including function test pass status, performance test indicators and security test vulnerabilities. Data storage module: The data storage module includes current data storage and historical data storage. It stores the data collected after each test, using one or more of the database and file system to store the previous test data and software operation status data for subsequent analysis. Data analysis module: The work of the data analysis module includes four steps: data preprocessing, real-time data analysis, historical data analysis, and comprehensive analysis. The data analysis module first cleans and preprocesses the collected data to remove noise and outliers, then analyzes the data of this test to evaluate the current status and performance of the software, and then calls historical data for auxiliary analysis, compares the difference between this data and historical data, finds out the trend and law of software performance changes, and finally combines the results of real-time data analysis and historical data analysis to obtain a comprehensive software status evaluation and optimization suggestions; Result display module: used to visualize the analysis results in the form of charts, reports, etc., and also used to generate detailed analysis reports, including test results, data analysis process, problem findings and optimization suggestions; System management module: used for user management, configuration management and log management; The data analysis module specifically includes the following steps: S1. Data initialization: define the time interval T for data collection and define the state parameter set S = {s1, s2, ..., s n }, initialize the historical data storage container H, which is used to store the collected data in the past period of time, and determine the similarity threshold θ, which is used to judge the similarity between the current data and the historical data; S2. Real-time data collection: Data collection is performed at time intervals T. For each state parameter s i , the data collected at time t is recorded as d i,t , the collected data is formed into a data vector D t =(d 1,t ,d n,t ,…,d n,t ); S3. Data preprocessing: perform outlier detection on the collected data, use a box plot-based method to calculate the lower quartile Q1, median Q2, and upper quartile Q3 of each state parameter, define the outlier range, and if the data point exceeds the range, it is considered an outlier, marked and replaced, and the data is normalized. Use the min-max normalization method to map the data to the [0,1] interval. The formula is: in Represents the normalized data, min(S i ) and max(S i ) represent the parameters s i The minimum and maximum values ​​of S4. Data fusion: The weighted average method is used for data fusion. For each state parameter s i , assign a weight w i ,satisfy The fused data S5. Historical data matching analysis: Calculate the current fusion data F t The similarity with each historical fusion data in the historical data storage container H, if it is found t The historical fusion data whose similarity is greater than the similarity threshold θ is denoted as F t-hist ; S6. Data correction: Based on historical fusion data F t-hist The structure and features of the current fusion data F t The corrected fusion data is recorded as F t,co ; S7. Historical data update: Update the fused data collected and processed this time t,co Stored in the historical data storage container H.

2. A software development-based status monitoring system as claimed in claim 1, characterized in that: In the step S3, the range of abnormal values ​​is less than Q1-1.5×IQR or greater than Q3+1.5×IQR, where IQR=Q3-Q1.

3. A software development-based status monitoring system as claimed in claim 1, characterized in that: In step S5, the Euclidean distance calculation method is used to calculate the current fusion data F t The similarity with each historical fusion data in the historical data storage container H is calculated as follows: for the current fusion data F t =(f t,1 ,f t,n ,…,f t,n ) and historical fusion data F hist =(f hist,1 ,f hist,2 ,…,f hist,n ), the Euclidean distance calculation formula is 4. A software development-based status monitoring system as claimed in claim 3, characterized in that: The similarity is also defined as The smaller the distance, the higher the similarity.

5. A software development-based status monitoring system as claimed in claim 1, characterized in that: In step S6, the weighted average method is used for correction, and the corrected fusion data F t,co =α×F t +(1-α)×F t-hist , where α is the correction weight, which is adjusted according to the relative importance of current data and historical data. If the current data is more reliable, increase the α value; if the historical data is more valuable for reference, decrease the α value.

6. A software development-based status monitoring system as claimed in claim 1, characterized in that: The data acquisition module uses embedded sensor technology. The sensors deployed in the hardware devices where the software runs monitor the temperature, voltage and current parameters of the hardware in real time, which indirectly reflects the software's use of hardware resources and the stability of the software's operation; Utilize the performance monitoring interface provided by the operating system to collect the software's memory usage in real time, including the usage of physical memory and virtual memory.

7. A software development-based status monitoring system as claimed in claim 6, characterized in that: The data collection module also collects the software's network traffic, network delay and data packet loss rate indicators in real time through network monitoring equipment; the data collection module measures the response time at key nodes of software-user interaction by installing a response time measurement tool, including the loading time of the user interface and the operation response time.

8. A software development-based status monitoring system as claimed in claim 1, characterized in that: The data storage module uses a relational database management system to store the collected data in a structured manner, and uses ETL data warehouse technology to extract, convert and load historical data from the original data source into the data warehouse.

9. A software development-based status monitoring system as claimed in claim 8, characterized in that: The system management module provides fine-grained control of user permissions and performs permission control on each function and data, ensuring that users can only access functions and data for which they have permission.

10. A software development-based status monitoring system as claimed in claim 9, characterized in that: The system management module is also used to configure system parameters, including data collection frequency and storage path. The frequency of data collection can be adjusted according to software performance and resource usage to avoid excessive impact on software performance. The storage path and storage method can be adjusted according to data storage requirements to improve data storage efficiency and reliability.

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