Composite feature visualization processing method, device and equipment and storage medium

Abstract

The invention discloses a composite feature visualization processing method and device, equipment and a storage medium, and can accumulate and aggregate measurement features according to project IDs to generate calculation index data after original data is collected. And constructing two-dimensional coordinate data based on the calculation index data, generating query data according to the two-dimensional coordinate data, and constructing front-end visual coordinates based on the query data. According to the method, composite features obtained after multi-feature aggregation serve as measurement representation, two-dimensional visualization calculation is participated in measurement indexes, query data are generated through query separation, and T + 1 data supply is guaranteed. And through two-dimensional coordinates and composite features, multiple pieces of measurement feature data are subjected to quasi-real-time visual static reflection processing in the same measurement space, so that the quality-effect measurement result accuracy and the quality-effect insight ability are improved.

Description

Technical Field

[0001] The present application relates to the field of big data technology, and in particular to a composite feature visualization processing method, device, equipment and storage medium. Background Art

[0002] With the development of software engineering, the application of quality and efficiency measurement in the field of R&D effectiveness has also emerged. Quality and efficiency measurement refers to the process of quantitative analysis and measurement of the quality and effectiveness of products, services or processes in the field of quality management and effectiveness evaluation. In the field of software development, quality and efficiency measurement can help build, execute, evaluate and improve software development effectiveness, and achieve quality and efficiency improvements.

[0003] In order to perform quality and efficiency measurement, the quality and efficiency middle platform can integrate data from process control, feature mining, data storage and processing, policy management and other functions, and perform query analysis on the integrated data, thereby providing a complete quality and efficiency measurement solution. In the process of performing quality and efficiency measurement, measurement targets and measurement objects are involved. Among them, the measurement target is a set of attributes used to describe and evaluate software quality. The measurement object is the specific object to be measured and evaluated in the process of software quality and efficiency measurement. The measurement target and the measurement object have a matching relationship, that is, one measurement target can match multiple measurement objects. Faced with complex measurement targets and the numerous measurement objects that match them, it is necessary to build a portrait of the measurement target for data analysis.

[0004] Because a single measurement object can only represent the fine-grained representation of a certain quality and efficiency feature of the measurement target, it is impossible to comprehensively reflect the state transition performance of the characteristics of a certain type of problem from the perspective of expected insight into the problem itself. Therefore, the quality and efficiency measurement based on a single measurement object cannot provide effective intervention prediction decisions to the measurement user, resulting in reduced accuracy and insight of the quality and efficiency measurement results. Summary of the invention

[0005] In view of this, embodiments of the present application provide a composite feature visualization processing method, apparatus, device, and storage medium to solve the problems of low accuracy and insight of quality and efficiency measurement results.

[0006] According to one aspect of the present application, a composite feature visualization processing method is provided, the method comprising:

[0007] Collecting original data, wherein the original data includes online problem information and test defect information;

[0008] Generate calculation index data according to the original data, the calculation index data includes an index vector generated by accumulating and aggregating metric features according to the project ID; the metric features are features associated with online problem information and test defect information extracted from the original data;

[0009] Constructing two-dimensional coordinate data based on the calculation index data, wherein the two-dimensional coordinate data is generated according to a two-dimensional feature vector, wherein the two-dimensional feature vector includes hash values ​​generated by a hash function of any two of the metric features in the calculation index data;

[0010] Generate query data according to the two-dimensional coordinate data, and construct front-end visualization coordinates based on the query data.

[0011] According to another aspect of the present application, a composite feature visualization device is provided, the device comprising:

[0012] A data collection module is used to collect raw data, including online problem information and test defect information;

[0013] An indicator generation module, used to generate calculated indicator data according to the original data, wherein the calculated indicator data includes an indicator vector generated by accumulating and aggregating metric features according to the project ID; the metric features are features associated with online problem information and test defect information extracted from the original data;

[0014] A coordinate construction module, used to construct two-dimensional coordinate data based on the calculation index data, wherein the two-dimensional coordinate data is generated according to a two-dimensional feature vector, wherein the two-dimensional feature vector includes hash values ​​generated by a hash function of any two of the metric features in the calculation index data;

[0015] The visualization module is used to generate query data according to the two-dimensional coordinate data, and to construct front-end visualization coordinates based on the query data.

[0016] According to another aspect of the present application, a storage medium is provided, on which a computer program is stored, and when the program is executed by a processor, the above-mentioned visualization processing method of the composite features is implemented.

[0017] According to another aspect of the present application, a computer device is provided, including a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor, wherein the processor implements the above-mentioned composite feature visualization processing method when executing the program.

[0018] By means of the above technical scheme, the visualization processing method, device, equipment and storage medium of the composite features provided in the embodiment of the present application can generate calculation index data by accumulating and aggregating the metric features according to the project ID after collecting the original data. Then, two-dimensional coordinate data is constructed based on the calculation index data, and query data is generated based on the two-dimensional coordinate data, and front-end visualization coordinates are constructed based on the query data. The method uses the composite features after multi-feature aggregation as the metric representation, participates in the two-dimensional visualization calculation in the metric index, generates query data through query separation, and ensures T+1 data supply. And through two-dimensional coordinates and composite features, multiple metric feature data are processed in a quasi-real-time visual static manner in the same measurement space, thereby improving the accuracy of quality and efficiency measurement results and the ability to gain insight into quality and efficiency.

[0019] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0021] Figure 1 A schematic diagram of the process of quality and efficiency measurement provided by an embodiment of the present application is shown;

[0022] Figure 2 An application scenario diagram of the software development management system provided by an embodiment of the present application is shown;

[0023] Figure 3 A schematic diagram of the process flow of a composite feature visualization processing method provided in an embodiment of the present application is shown;

[0024] Figure 4 A schematic diagram of the software development management system processing flow provided by an embodiment of the present application is shown;

[0025] Figure 5 A schematic diagram of a process for generating calculation index data provided by an embodiment of the present application is shown;

[0026] Figure 6 A schematic diagram of a process for generating two-dimensional coordinate data provided by an embodiment of the present application is shown;

[0027] Figure 7 A schematic diagram of a query data generation process provided by an embodiment of the present application is shown;

[0028] Figure 8A schematic diagram of a query data visualization process provided by an embodiment of the present application is shown;

[0029] Fig. 9 A schematic diagram of a drill-down query data visualization processing flow provided in an embodiment of the present application is shown;

[0030] Fig.10 A schematic diagram of the structure of a composite feature visualization processing device provided in an embodiment of the present application is shown;

[0031] Fig.11 A schematic diagram of the structure of a computer device provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0032] The present application will be described in detail below with reference to the accompanying drawings and in combination with embodiments. It should be noted that the embodiments and features in the embodiments of the present application can be combined with each other without conflict.

[0033] In the embodiments of the present application, quality and efficiency measurement (QEM) refers to quantitative analysis and evaluation of various aspects of the software development process to ensure the quality of the software product and improve development efficiency. Quality and efficiency measurement can be used to understand the current status of development work, thereby identifying improvement points and tracking project progress.

[0034] Quality and efficiency measurement can include quality measurement, efficiency measurement, process measurement, project measurement, team measurement, continuous improvement, data-driven decision-making, standardization and automation. Among them, quality measurement focuses on the attributes of software products, such as functionality, reliability, ease of use, efficiency, maintainability and portability. The quality of software is evaluated by measuring indicators such as code complexity, defect rate, and test coverage. Efficiency measurement focuses on the efficiency of the software development process, including development speed, resource utilization, productivity, etc. Development efficiency is evaluated by measuring indicators such as development cycle, average output, and automated testing ratio.

[0035] Process measurement involves various stages in the software development process, such as requirements analysis, design, coding, testing, and maintenance. The efficiency and quality of the development process are evaluated by measuring the completion, workload, and progress of each stage. Project measurement focuses on the management and control of the entire software project, including project planning, cost, risk, and quality. The actual status of the project is evaluated by measuring project progress, cost control, and risk management. Team measurement focuses on the performance and collaboration of the development team, including the skills, communication, collaboration, and satisfaction of team members. The efficiency of the team's collaboration is evaluated by measuring the team's communication frequency, code review, and knowledge sharing.

[0036] Quality and efficiency measurement can be used to evaluate the current status and for continuous improvement. That is, through regular measurement and feedback, problems can be identified, actions can be taken, and improvement effects can be tracked. Quality and efficiency measurement can provide data-based insights to assist in making more objective and scientific decisions. In order to improve the accuracy and efficiency of measurement, standardized measurement methods and automated tools can be used to collect and analyze data.

[0037] The metrics described in the embodiments of the present application, also known as metrics, are a set of attributes used to describe and evaluate software quality. In some embodiments of the present application, the metrics can be used to generally refer to the quality of software online issues of the R&D group.

[0038] The measurement object described in the embodiments of the present application is the object to be measured and evaluated in the software quality measurement process. The measurement object can be the software product itself, or various stages in the software development process, or a specific practice in a software project.

[0039] The measurement indicators (Measurement Metrics) described in the embodiments of the present application refer to specific standards and values ​​used to quantitatively evaluate various aspects of the software development process. Measurement indicators are the specific implementation of metrics, and measurement indicators can provide a quantitative data to evaluate the quality, performance, development efficiency, and team productivity of the software. For example, measurement indicators may include the number of online issues, the number of test defects, etc.

[0040] Since metrics can evaluate software quality through a series of quantitative data, and these quantitative data can reflect the characteristics and performance of the software from different perspectives, metrics can be represented by multiple metric characteristic dimensions. For example, metrics can be used to represent the metric characteristic dimension of the problem type, and the specific content of the metric characteristic dimension can include dimensional values ​​such as security issues and functional issues.

[0041] In the process of quality and efficiency measurement, the concepts involved, such as measurement targets, measurement objects, and measurement indicators, can be represented by data of specific types and formats. The data that can represent specific concepts can be data generated during the software development process, along with the development and testing process, or data recorded or written by the software development organization according to the specific circumstances of the software development process.

[0042] In the process of performing quality and efficiency measurement, multi-dimensional data can be collected according to the quality and efficiency measurement process, and the collected data can be used to analyze and process the software development process to obtain data analysis results that can show the specific situation of software development. To this end, in some embodiments, a software development management system can be deployed on electronic devices such as servers or clients. The software development management system can be used to collect various data generated during the software development process, and convert, analyze, display and store the collected data.

[0043] like Figure 1 As shown, the data generated during the software development process can be collected from each development end by the built-in sensor or data interface of the software development management system, or can be sent from the development end to the software development management system. In the process of software development quality and efficiency measurement, the collected data may include: demand and design data, test data, defect data, process data, project and team data, etc. Among them, the demand and design data can be the number of requirements, the average number of test cases written for each requirement, the total number of test cases written for all requirements, etc. The test data can be the execution status of the test cases, including the number of executed test cases, the number of passed test cases, the number of failed test cases, the number of blocked test cases, the number of unexecuted test cases, etc.

[0044] Defect data refers to the number of identified test defects, critical defects, high defects, medium defects, low defects, etc. Process data includes maturity metrics (such as organizational metrics, resource metrics, training metrics, etc.), management metrics (such as project management metrics, quality management metrics, configuration management metrics, etc.) and lifecycle metrics (such as problem definition metrics, requirement analysis metrics, design metrics, manufacturing metrics, maintenance metrics, etc.). Project and team data can include burndown charts, velocity charts, standard deviations, throughput, cumulative flow charts, control charts, Kanban work-in-progress limit charts, build and deployment speed, test speed, code review time, unit test pass rate, integration test pass rate, etc.

[0045] The collected data can be used as the analysis basis for quality and efficiency measurement in the software development process. By analyzing these raw data, the quality of the software, development efficiency and team performance can be evaluated. The collection and analysis of these data can help identify problems, optimize processes, and improve software quality and development efficiency.

[0046] like Figure 1 As shown, the software development management system can perform feature extraction on the collected data. Among them, the feature refers to the data fragment extracted from the collected data according to the specific data analysis needs. The specific features extracted can be expressed in different data forms according to the feature type, feature purpose, feature extraction method, etc. For example, after obtaining the software test data, the software development management system can read the fields used to characterize the test defects in the test data, and count the fields read to obtain the test defect number feature. When extracting the test defect number feature, the field used to characterize the test defect can also be called a feature field.

[0047] Based on this, the extracted features that can be used for quality and efficiency measurement can be called measurement features. For example, measurement features can include but are not limited to problem ID, project ID, validity information, number of problem occurrences, time, etc.

[0048] Since some of the extracted features may have correlations, such as one feature may reflect an abnormal state in the software development process together with another feature, the metric features of each aspect may be integrated to obtain a feature vector containing multiple dimensions. For example, in order to reflect online problems in software development, features such as online problem ID, project ID, validity, abandonment, and time may be extracted to obtain a feature vector with the content (ID1, F1, c1, time). Among them, ID1 is used to represent online problems, F1 is used to represent test defect function features, c1 represents the number of occurrences of the test defect function, and time represents the occurrence time.

[0049] When displaying analysis results, the software development management system can comprehensively display the analysis results corresponding to two or more metric features. To this end, after extracting the metric features, two or more features can be aggregated to obtain aggregate features. Aggregate features, also known as composite features, can aggregate multiple related metrics or data points into a single metric value for easy analysis and understanding. Aggregate features can be used to comprehensively display multi-faceted data and simplify complex data, making it easier to analyze and process.

[0050] In a software development process management system, data can be collected from multiple data sources (such as JIRA, TAPD, etc.). Since the collected data may contain inconsistent data entities, it is necessary to make the data consistent through modeling, abstraction, mapping, and association. By performing feature aggregation, this data can be abstracted into the domain model of each link in the R&D process and stored in a highly correlated data warehouse to improve query performance, reading performance, and management convenience.

[0051] When performing feature aggregation, the software development management system needs to extract and combine indicators, that is, extract indicators from the data set and combine them into aggregated features based on scenario requirements. Aggregate features can combine multiple indicators into a comprehensive metric to meet the information needs of analysis and decision-making.

[0052] After the software development management system collects data and extracts features, it can convert, calculate, and analyze feature data based on the extracted features to obtain analysis result data. In order to present the analysis results, the software development management system can visualize the analysis results by running and displaying related program steps. The display methods of the analysis results may include data visualization reports, data dashboards, data visualization tools, chart tools, code-based visualization tools, cross-platform chart libraries, etc.

[0053] In a feasible implementation, the software development management system can be run on a single device, that is, the user can realize all the functions of the software development management system through a single client device. For example, the software development management system is an application installed on a personal computer. The software development management system application has a built-in application for collecting, analyzing, storing, and displaying software development data. By running the application, the computer can obtain the data generated during the software development process, as well as the data input by the user or stored locally, and perform analysis on the obtained data to obtain the corresponding analysis results. Then, based on the display program provided by the application, the analysis result data is displayed.

[0054] In another feasible implementation, the software development management system can be run on multiple devices, that is, the coordination between multiple devices is required to realize the full functions of the software development management system. Figure 2 As shown, the software development management system includes a server and multiple clients connected to the server. The multiple clients can be operated by different users respectively, and at least one of the multiple clients is used to perform software development operations. As the software development operation proceeds, software development process monitoring data will be generated. These data can be sent to the server through the network after preliminary processing locally on the client. The data is then further processed, distributed and stored by the server. Different clients can call different data from the server based on different user permissions. And the server can send specific data to the client according to specific data analysis needs.

[0055] It should be noted that in the embodiment of the present application, a software development management system is used as the execution subject of the visualization processing method of composite features. The software development management system can be implemented by running relevant applications on the client, or by running relevant applications on the server, or by cooperating with the client to run relevant applications. Therefore, from the perspective of hardware devices, the steps corresponding to the visualization processing method of composite features can be executed by the client, or by the server, or some steps can be executed by the client and some steps can be executed by the server.

[0056] When performing data analysis, the software development management system can perform analysis on a single measurement object according to the user's selection. For example, by setting a goal for the software development management system and setting the measurement object to test defects, the software development management system can extract features related to test defects from the collected data according to the set goal, and perform defect analysis based on the extracted features, such as determining the defect type, defect number, defect generation stage, etc., to obtain the test defect analysis results.

[0057] However, when quality and efficiency measurement is performed on a single measurement object, the single measurement object can only represent the fine-grained representation of the measurement target in a certain quality and efficiency characteristic. It is unable to comprehensively reflect the state transition performance of the characteristics of a certain type of problem from the perspective of the expected insight into the problem itself, resulting in reduced accuracy and insight in the quality and efficiency measurement results, making it inconvenient to make effective intervention prediction decisions.

[0058] In order to improve the accuracy and insight of quality and efficiency measurement results, a composite feature visualization method is provided in this embodiment. Figure 3 , Figure 4 As shown, the method includes:

[0059] S101. Collect original data.

[0060] After the software development management system is started, it can collect raw data. The raw data refers to data generated during the software development process that has not been processed by the software development management system. In the process of executing the quality and efficiency measurement of software development, the raw data includes online problem information and test defect information generated during the software development process. For example, the online problem information in the raw data includes: online problem ID, project ID, whether it is valid, whether it is abandoned, etc. The test defect information includes: test defect ID, project ID, whether it is valid, whether it is abandoned, etc.

[0061] In order to collect raw data, the client or server running the software development management system may be provided with a data acquisition module. The data acquisition module may include an interface unit, and the interface unit is used to connect to a sensor or an application programming interface (Application Programming Interface, API) of a software development tool. The software development management system can collect raw data through the data acquisition module.

[0062] In some embodiments, the software development management system can collect raw data in real time by means of data monitoring. That is, after the software development management system is running, it maintains the data collection state. When the client or other electronic device connected to the software development management system generates raw data, the software development management system can collect the generated raw data and pre-process or store the collected data. Such a raw data collection method can collect more comprehensive data and improve the accuracy of subsequent analysis results.

[0063] In other embodiments, the software development management system can also collect raw data in a directional manner through instruction control. That is, after the software development management system is running, it can receive control instructions input by the user, wherein some control instructions can be used to control the software development management system to collect data, which are called data collection instructions. After receiving the data collection instructions, the software development management system then executes the step of collecting raw data.

[0064] For example, a user can set data collection within a certain statistical period through a data collection instruction. Then the software development management system can respond to the data collection instruction and collect online problem information and test defect information generated by each software project within the statistical period. And the collected online problem information and test defect information are marked and stored for subsequent processing. This data collection method can be controlled by the user, and the original data is collected only when data collection is needed, thereby reducing the amount of data collection and processing and improving the operation speed of the system.

[0065] S102: Generate calculation index data according to the original data.

[0066] After collecting and obtaining the original data, the software development management system can perform feature tagging and feature extraction on the original data, thereby generating calculation index data based on the original data. The calculation index data may include an index vector. The index vector is used to represent the specific problems existing in each project in the software development data through feature parameters of multiple dimensions.

[0067] To this end, before generating the calculation index data, it is necessary to extract features from the original data to obtain metric features. That is, the metric features are features associated with online problem information and test defect information extracted from the original data. The extracted metric features are then accumulated and aggregated according to the project ID to obtain the calculation index data. That is, the calculation index data includes an indicator vector generated by accumulating and aggregating the metric features according to the project ID.

[0068] like Figure 5 As shown, in some embodiments, in order to generate calculation index data, the software development management system can extract the metric features from the raw data (S1201). The metric features include the problem ID, project ID, validity information, and other data that can reflect the software development process corresponding to the online problem information and test defect information.

[0069] Then, the metric features are filtered according to the validity information (S1202). The purpose of filtering the metric features is to exclude invalid features or obsolete features in the metric features. In a feasible implementation, a filter can be used to filter the metric features. That is, after extracting the metric features, the software development management system can set the filter parameters of the filter, and the filter parameters are used to set the conditions that the metric features that the filter should delete and retain should meet. For example, the filter parameters of the filter can be set to "filter (valid, not obsolete)", then the filter can retain valid and non-obsolete metric features based on the set filter parameters, and delete invalid or obsolete metric features.

[0070] After setting the filter parameters, the filter can be used to read the validity information of the metric feature. If the validity information is the first information used to characterize that the metric feature is invalid or has been discarded, the metric feature is deleted. If the validity information is the second information used to characterize that the metric feature is valid and not discarded, the metric feature is retained.

[0071] The validity information may be directly read from the original data. For example, the original data may include a field for characterizing the validity of the data, such as "Data validity". When the parameter value of this field is "true", it indicates that the current original data is valid; when the parameter value of this field is "false", it indicates that the current original data is invalid.

[0072] The validity information can also be generated based on the validity judgment results of the original data according to the preset validity judgment rules. For example, for some online problem projects and test defect projects with high timeliness, the timestamp of the original data can be read after the original data is collected to determine the generation time of the original data. The read generation time is then compared with the preset time threshold. If the generation time is earlier than the preset time threshold, it means that the current original data can no longer meet the timeliness requirements. Therefore, validity information can be generated to characterize the failure, that is, "Data validity = false" is set. Similarly, if the generation time is later than the preset time threshold, it means that the current original data meets the timeliness requirements. Therefore, validity information can be generated to characterize the validity, that is, "Data validity = true" is set.

[0073] After screening the metric features according to the validity information, the software development management system can aggregate the metric features according to the project ID to generate a composite feature (S1203), and count the count values ​​of the aggregated metric features. Then, a calculation indicator data vector is generated according to the composite feature, the project ID and the count value (S1204).

[0074] For example, for collecting online problem information and test defect information from raw data, online problem information includes: online problem ID, project ID, whether it is valid, whether it is abandoned, etc.; test defect information includes: test defect ID, project ID, whether it is valid, whether it is abandoned, etc. Then, by extracting the metric features in the raw data and performing aggregate calculations, that is:

[0075] Online problem ID1->F1 function problem->filter(valid, not abandoned)->COUNT=c1;

[0076] Online question ID1->F2 security question->filter(valid, not discarded)->COUNT=c2;

[0077] Among them, ID1 represents an online problem ID, ID2 represents a test defect problem ID, F1 is the test defect functional feature, F2 is the test defect safety feature, c1 represents the number of features that satisfy ID1 and F1; c2 represents the number of features that satisfy ID1 and F2.

[0078] Thus, the calculated indicator data including the following indicator vectors are obtained:

[0079] R1: (ID1, F1, c1->20, 2024-01); R2: (ID2, F1, c3->23, 2024-01);

[0080] R3: (ID1, F2, c2->2, 2024-01); R4: (ID2, F2, c4->3, 2024-01).

[0081] S103: construct two-dimensional coordinate data based on the calculation index data.

[0082] After obtaining the calculated indicator data, the specific indicator vectors in the calculated indicator data can be fused to obtain a composite vector, thereby constructing two-dimensional coordinate data based on the calculated indicator data. Due to differences in data sources, different indicator vectors may have inconsistent vector dimensions. Therefore, in order to fuse vectors with inconsistent dimensions, the software development management system can transform inputs of any length into outputs of fixed length based on a hash algorithm when fusing indicator vectors, that is, output the hash value or hash value of any two indicator vectors.

[0083] Based on this, the two-dimensional coordinate data is generated according to the two-dimensional feature vector, and the two-dimensional feature vector includes the hash value generated by the hash function of any two of the measurement features in the calculation index data. Figure 6As shown, in some embodiments, in order to generate two-dimensional coordinate data, any two metric features in the indicator data can be first extracted, and then a hash value is generated by a hash function to form a two-dimensional feature vector. The two-dimensional coordinate data is then formed by the generated two-dimensional feature vector. Specifically, when the software development management system performs indicator vector fusion, the hash function can be called and any two metric features can be extracted from the calculated indicator data. Then the two metric features and sets are calculated to generate a combined feature. The hash function is then used to perform hash value calculation on the combined feature to generate two-dimensional coordinate data.

[0084] For example, the pre-constructed two-dimensional feature is F3, which is used to represent the two-dimensional feature after the test defect functional feature F1 and the test defect safety feature F2 are merged, that is, F3 = HASH (F1 + F2). Then, the two-dimensional coordinates are constructed based on the two-dimensional features to obtain the following two-dimensional coordinate data:

[0085] Vec1: (ID1, F3, c1->20, 2024-01); Vec2: (ID2, F3, c3->23, 2024-01);

[0086] Vec3: (ID1, F3, c2->2, 2024-01); Vec4: (ID2, F3, c4->3, 2024-01).

[0087] S104, generating query data according to the two-dimensional coordinate data, and constructing front-end visualization coordinates based on the query data.

[0088] After generating the two-dimensional coordinate data, the software development management system can generate query data for users to perform query operations. According to the supervision needs of the software development process, users can retrieve information from the database. During the information retrieval process, they can use Structured Query Language (SQL) or other database query languages ​​to request a specific data set. The generated query data can facilitate the database to perform information retrieval, thereby realizing quasi-real-time visual static embodiment processing.

[0089] Take SQL database as an example. SQL database is a standard language for managing and operating relational databases, which provides a declarative way to define and retrieve data. In the process of executing data query, you can define the SELECT statement for SQL query, which includes a list of fields to be retrieved and a FROM statement to specify the table name of the data source. You can also use the WHERE clause to filter records, ORDER BY to sort the results, and GROUP BY and HAVING to group and further filter the data. After obtaining the SELECT statement for SQL query, the database can perform information retrieval according to the specific content specified by the SELECT statement, thereby retrieving relevant query data in the database, and then feeding the query data back to the user client for front-end visual data processing.

[0090] In order to improve query performance, the database will use indexes to quickly locate data to speed up queries. Therefore, after generating two-dimensional coordinate data, you can add indexes to the two-dimensional coordinate data to generate query data that is convenient for information retrieval. Figure 7 As shown, in some embodiments, in order to generate two-dimensional coordinate data, the software development management system may first construct a quasi-real-time query logic generation task, and then perform normalization processing on the target vector dimension in the two-dimensional coordinate data according to the quasi-real-time query logic generation task. Then, name information is added to the normalized two-dimensional coordinate data to generate query data, and then the query data is stored in a document-oriented data storage manner.

[0091] For example, after generating two-dimensional coordinate data, the software development management system can construct a T+1 query logic generation task to generate query data and store it in Mongo. The T+1 query logic generation task generates query logic based on the data of the previous cycle (such as T day) so that these queries can be executed in the next cycle (such as T+1 day) to obtain or process data. MongoDB is an open source non-SQL database that uses a document-oriented storage model. In the Mongo database, data can be stored in the BSON format. That is, the following data is obtained:

[0092] Vec1:(ID1, F3, normalization function (20, 2), 2024-01, project name 1);

[0093] Vec2:(ID2, F3, normalization function (23, 3), 2024-01, project name 2);

[0094] F3: (F1, F2);

[0095] The normalization function can be used to scale the data to a specified range, such as scaling the data to the interval [0, 1] or [-1, 1]. Scaling the normalization function can help improve the performance and convergence speed of the algorithm, reduce data redundancy and dependency, and improve the integrity and consistency of the data by decomposing the data into smaller tables. For example, the normalization function can be as follows:

[0096]

[0097] Where x is the input variable, which can be any real number. e is the base of the natural logarithm. The output value range of the above Sigmoid normalization function is between 0 and 1, so it can be used to map any real number into this interval to achieve normalization.

[0098] After the query data is generated and stored, the software development management system can construct the front-end visualization coordinates based on the query data. The front-end visualization coordinates can be pre-generated and stored according to the query data when the query data is generated and stored, or can be generated in real time according to the query instruction input by the user. Figure 8 As shown, in some embodiments, when constructing the front-end visualization coordinates based on the query data, the software development management system can obtain a first query instruction, and in response to the first query instruction, obtain the query data. Then create a visualization container, wherein the visualization container is provided with a scale, a coordinate axis, and a visualization style parameter. And bind the query data to the visualization container to generate the front-end visualization coordinates.

[0099] For example, after generating query data, the software development management system can build front-end visualization coordinates based on D3js, and use the HyperText Transfer Protocol (HTTP) protocol to query the generated query data. Specifically, when building front-end visualization coordinates based on D3.js, you can first add a Scalable Vector Graphics (SVG) canvas, that is, you need to add an SVG element in the HTML document as a visualization container. The software development management system can create and set the size of the SVG canvas through the select and append methods of D3.js. Then define the data and scale, that is, prepare the data set to be visualized, and define scales such as linear scales (scaleLinear) and band scales (scaleBand) to map data values ​​to SVG coordinates. Then use the axis generator (axis) of D3.js to create the axis, and specify the scale, label and style of the axis. Then add rectangular elements to represent data points according to the data set and scale. At the same time, text elements can be added to display data values. By setting the attributes of SVG elements, such as position (x, y), size (width, height), color (fill), etc., to reflect the characteristics of the data, and adding coordinate axes, adding the defined coordinate axes to the SVG canvas and setting the position, the query data can be visualized.

[0100] By applying the technical solution of this embodiment, the raw data in the software development process can be visualized based on the composite features of multiple metrics of the two-dimensional measurement object. The composite features after multi-feature aggregation are used as measurement representations. The quasi-real-time and interpretable characteristics of the composite features can be used to reflect the participation in two-dimensional visualization calculations in the measurement indicators, and query data can be generated through query separation to ensure T+1 data supply. In addition, through two-dimensional coordinates and composite features, multiple measurement feature data can be visualized and statically reflected in quasi-real time in the same measurement space, helping users get rid of the one-dimensional observation path of a single indicator and improve the accuracy of quality and efficiency measurement results and quality and efficiency insight capabilities.

[0101] Further, as a refinement and extension of the specific implementation of the above embodiment, in order to fully illustrate the specific implementation process of this embodiment, some embodiments of this application provide another composite feature visualization processing method, such as Fig. 9 As shown, the method may further include the following steps based on executing steps S101 to S104 in the above embodiment:

[0102] S201. Obtain a second query instruction.

[0103] The second query instruction is used to control the software development management system to perform a drill-down query. Drill-down query is a data analysis technology that allows users to drill down from high-level data summary information to more detailed data levels. Drill-down query can be used in multidimensional data sets or hierarchical data structures to help users explore detailed information of data. The software development management system can provide a drill-down query entry after performing visualization processing.

[0104] For example, the result display interface presented by the client may include a control for viewing detailed data. When the user clicks the control for viewing detailed data, the software development management system may be controlled to perform a drill-down query, that is, input a second query instruction.

[0105] Obviously, according to the specific interaction methods supported by the client or server, the user can input the second query instruction in different interaction methods. For example, the second query instruction can be input through one or more combinations of voice interaction, gesture interaction, touch interaction, and external device interaction.

[0106] S202. In response to the second query instruction, read input parameters from the second query instruction.

[0107] After obtaining the second query instruction, the software development management system may respond to the second query instruction and read an input parameter from the second query instruction, wherein the input parameter is vector dimension information specified in the query data.

[0108] For example, when the software development management system presents a defect display interface corresponding to a composite feature involving the test defect functional feature F1 and the test defect safety feature F2, the user can click on the view details control corresponding to the test defect to enter a second query instruction. The second query instruction can carry the control information of the test defect, so the input parameter can be read to include F1. Similarly, when the user clicks on the view details control corresponding to the test defect safety feature F2, the input second query instruction can include the test defect safety feature F2, that is, the input parameter includes F2.

[0109] Depending on the different contents of the controls presented in the display interface of the software development management system, the input parameters included in the second query instruction input by the user according to the controls are also different, and the input input parameters may include multiple specific feature items. For example, the input parameters may be one or more combinations of F1, F2, ID1, ID2, 2024-01.

[0110] S203: Query detailed data according to the input parameters.

[0111] After reading and obtaining the input parameter, the software development management system can query the detailed data according to the input parameter. The detailed data is the interpretable data in the drill-down query process. The detailed data includes at least one of the two-dimensional coordinate data, the calculation index data and the original data.

[0112] By applying the technical solution of this embodiment, the user can query detailed data in the database according to the second query instruction containing different input parameters, that is, query the original data generated in the software development process, or query the result data and any intermediate data in the quality and efficiency measurement, such as query data, calculation index data, and two-dimensional coordinate data. Different query results can be obtained with different second query instructions, thereby improving the flexibility of data visualization and meeting the diverse needs of different users.

[0113] Furthermore, as a specific implementation of the composite feature visualization processing method described in the above embodiment, the present application embodiment also provides a composite feature visualization processing device, such as Fig.10 As shown, the visualization processing device of the composite feature includes:

[0114] A data collection module is used to collect raw data, including online problem information and test defect information;

[0115] An indicator generation module, used to generate calculated indicator data according to the original data, wherein the calculated indicator data includes an indicator vector generated by accumulating and aggregating metric features according to the project ID; the metric features are features associated with online problem information and test defect information extracted from the original data;

[0116] A coordinate construction module, used to construct two-dimensional coordinate data based on the calculation index data, wherein the two-dimensional coordinate data is generated according to a two-dimensional feature vector, wherein the two-dimensional feature vector includes hash values ​​generated by a hash function of any two of the metric features in the calculation index data;

[0117] The visualization module is used to generate query data according to the two-dimensional coordinate data, and to construct front-end visualization coordinates based on the query data.

[0118] For example, a visualization processing device for composite features can collect valid online problem data and valid test defect data generated by each software project through a data collection module within a certain statistical period, mark the data features and store them in a database, and then construct two-dimensional feature coordinates based on the collected data through an indicator generation module and a coordinate construction module, and generate query data through query logic processing of the visualization module, thereby generating a visualization image based on D3js.

[0119] By applying the technical solution of this embodiment, the visualization processing device of composite features can generate calculation index data by accumulating and aggregating metric features according to the project ID after collecting the original data. Then, two-dimensional coordinate data is constructed based on the calculation index data, and query data is generated based on the two-dimensional coordinate data, and front-end visualization coordinates are constructed based on the query data. The device uses the composite features after multi-feature aggregation as the metric representation, participates in the two-dimensional visualization calculation in the metric index, generates query data through query separation, and ensures T+1 data supply. And through two-dimensional coordinates and composite features, multiple metric feature data are processed in a quasi-real-time visual static manner in the same measurement space, thereby improving the accuracy of quality and efficiency measurement results and the ability to gain insight into quality and efficiency.

[0120] It should be noted that for other corresponding descriptions of the functional units involved in the composite feature visualization processing device provided in the embodiment of the present application, reference can be made to the corresponding descriptions in the composite feature visualization processing method described in the above embodiment, and will not be repeated here.

[0121] like Fig.11 As shown, the embodiment of the present application also provides a computer device, which can be specifically a personal computer, a server, a network device, etc. The computer device includes a bus, a processor, a memory and a communication interface, and can also include an input and output interface and a display device. Among them, 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 location information. The network 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, the steps in each method embodiment are implemented.

[0122] Those skilled in the art will appreciate that the structure of the above-mentioned computer device is only a partial structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components, or combine certain components, or have a different arrangement of components.

[0123] In one embodiment, a computer-readable storage medium is provided. The computer-readable storage medium may be non-volatile or volatile, and stores a computer program thereon. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0124] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0125] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0126] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods 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 the present 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), magnetoresistive 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 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.

[0127] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0128] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be construed as limiting the scope of the present application. It should be noted that, for a person of ordinary skill in the art, several modifications and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A composite feature visualization processing method, characterized in that: The method comprises: Collecting original data, wherein the original data includes online problem information and test defect information; Generate calculation index data according to the original data, the calculation index data includes an index vector generated by accumulating and aggregating metric features according to the project ID; the metric features are features associated with online problem information and test defect information extracted from the original data; Constructing two-dimensional coordinate data based on the calculation index data, wherein the two-dimensional coordinate data is generated according to a two-dimensional feature vector, wherein the two-dimensional feature vector includes hash values ​​generated by a hash function of any two of the metric features in the calculation index data; Generate query data according to the two-dimensional coordinate data, and construct front-end visualization coordinates based on the query data.

2. The method according to claim 1, characterized in that: Generate calculation index data according to the original data, including: Extracting the metric feature from the original data, the metric feature including the problem ID, the project ID, and the validity information corresponding to the online problem information and the test defect information; filtering the metric feature according to the validity information; Aggregating the metric features according to the project IDs to which they belong to generate composite features, and counting the count values ​​of the aggregated metric features; A calculation index data vector is generated according to the composite feature, the project ID and the count value.

3. The method according to claim 2, characterized in that Filtering the metric feature according to the validity information includes: Set the filter parameters of the filter; reading validity information of the metric feature using the filter; If the validity information is first information, deleting the metric feature, the first information being used to indicate that the metric feature is invalid or has been discarded; If the validity information is second information, the metric feature is retained, and the second information is used to indicate that the metric feature is valid and not discarded.

4. The method according to claim 1, characterized in that: Constructing two-dimensional coordinate data based on the calculation index data includes: Call the hash function; Extract any two of the metric features from the calculated indicator data; Calculating the sum of the two metric features to generate a combined feature; A hash value calculation is performed on the combined feature using the hash function to generate the two-dimensional coordinate data.

5. The method according to claim 1, characterized in that: Generating query data according to the two-dimensional coordinate data includes: Build quasi-real-time query logic generation tasks; Generate a task according to the quasi-real-time query logic, and perform normalization processing on the target vector dimension in the two-dimensional coordinate data; Adding name information to the normalized two-dimensional coordinate data to generate query data; The query data is stored in a document-oriented data storage manner.

6. The method according to claim 1, characterized in that Constructing front-end visualization coordinates based on the query data includes: Obtain a first query instruction; In response to the first query instruction, acquiring the query data; Creating a visualization container, wherein the visualization container is provided with a scale, a coordinate axis, and visualization style parameters; The query data is bound to the visualization container to generate the front-end visualization coordinates.

7. The method according to claim 1, characterized in that The method further comprises: Get the second query instruction; In response to the second query instruction, read an input parameter from the second query instruction, where the input parameter is vector dimension information specified in the query data; Query detailed data according to the input parameters, the detailed data being interpretable data in the drill-down query process; the detailed data includes at least one of the two-dimensional coordinate data, the calculation index data and the original data.

8. A composite feature visualization device, characterized in that: The device comprises: A data collection module is used to collect raw data, including online problem information and test defect information; An indicator generation module, used to generate calculated indicator data according to the original data, wherein the calculated indicator data includes an indicator vector generated by accumulating and aggregating metric features according to the project ID; the metric features are features associated with online problem information and test defect information extracted from the original data; A coordinate construction module, used to construct two-dimensional coordinate data based on the calculation index data, wherein the two-dimensional coordinate data is generated according to a two-dimensional feature vector, wherein the two-dimensional feature vector includes hash values ​​generated by a hash function of any two of the metric features in the calculation index data; The visualization module is used to generate query data according to the two-dimensional coordinate data, and to construct front-end visualization coordinates based on the query data.

9. A computer device comprising a storage medium, a processor, and a computer program stored in the storage medium and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 7 is implemented.

10. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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