CN119578976A

CN119578976A - A composite feature visualization processing method, device, equipment and storage medium

Info

Publication number: CN119578976A
Application number: CN202411629038.5A
Authority: CN
Inventors: 李成杰
Original assignee: Ping An Life Insurance Company of China Ltd
Current assignee: Ping An Life Insurance Company of China Ltd
Priority date: 2024-11-14
Filing date: 2024-11-14
Publication date: 2025-03-07

Abstract

The present application discloses a composite feature visualization processing method, device, equipment and storage medium, which can generate calculation index data by accumulating and aggregating metric features according to project ID after collecting original data. Two-dimensional coordinate data is then 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 visualized and statically reflected in quasi-real time in the same measurement space, improving the accuracy of quality and efficiency measurement results and quality and efficiency insight capabilities.

Description

Visualization processing method, device and equipment for composite characteristics and storage medium

Technical Field

The present application relates to the field of big data technologies, and in particular, to a method, an apparatus, a device, and a storage medium for visualization processing of composite features.

Background

With the development of software engineering, quality and efficiency metrics have also been applied in the field of research and development efficacy. Quality and effectiveness metrics refer to the process of quantitatively analyzing and measuring the quality and effectiveness of a product, service or process in the field of quality management and performance assessment. In the field of software development, the quality and efficiency measurement can help to construct, execute, evaluate and improve the software development efficiency, and improve the quality and efficiency.

In order to execute the quality and efficiency measurement, the data with the functions of flow control, feature mining, data storage and processing, strategy management and the like can be integrated through a quality and efficiency middle stage, and query analysis is executed on the integrated data, so that a complete quality and efficiency measurement solution is provided. In performing quality and efficiency metrics, both metrics and metrics objects are involved. Wherein the metrics are a set of attributes that are used to describe and evaluate the quality of the software. A metrology object is an object that is specifically measured and evaluated in a software quality-effect metrology process. The metrics and the metrics objects have a matching relationship, i.e., one metric may be matched with multiple metrics objects. In the face of complex metrics and numerous metrics matching them, it is necessary to construct representations of the metrics for performing data analysis.

Because of the single metric object, only a fine granularity characterization of the metric at a certain quality-effect feature can be represented. The state transition performance of the characteristics of a certain class of problems cannot be comprehensively reflected from the expected insight-oriented problem, so that the quality and efficiency measurement based on a single measurement object cannot give an effective intervention pre-judgment decision to a measurement user, and the accuracy and insight of the quality and efficiency measurement result are reduced.

Disclosure of Invention

In view of the above, the embodiments of the present application provide a method, an apparatus, a device, and a storage medium for visualization processing of composite features, so as to solve the problem that accuracy and insight of quality and efficiency measurement results are low.

According to one aspect of the present application, there is provided a method of visualization processing of composite features, the method comprising:

Collecting original data, wherein the original data comprises on-line problem information and test defect information;

Generating calculation index data according to the original data, wherein the calculation index data comprises index vectors generated by accumulating and aggregating measurement features according to item IDs, and the measurement features are features related to on-line 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 two-dimensional feature vectors, and the two-dimensional feature vectors comprise hash values generated by hash functions of any two measurement features in 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 another aspect of the present application, there is provided a visualization apparatus of composite features, the apparatus comprising:

The data acquisition module is used for acquiring original data, wherein the original data comprises on-line problem information and test defect information;

The system comprises an index generation module, a test defect information generation module and a test defect information generation module, wherein the index generation module is used for generating calculation index data according to the original data, and the calculation index data comprises index vectors generated according to item ID accumulated and aggregated measurement characteristics;

The coordinate construction module is used for constructing two-dimensional coordinate data based on the calculation index data, the two-dimensional coordinate data are generated according to two-dimensional feature vectors, and the two-dimensional feature vectors comprise hash values generated by hash functions of any two measurement features in the calculation index data;

And the visualization module is used for generating query data according to the two-dimensional coordinate data and constructing front-end visualization coordinates based on the query data.

According to still another aspect of the present application, there is provided a storage medium having stored thereon a computer program which, when executed by a processor, implements the above-described method of visualizing the composite feature.

According to still another aspect of the present application, there is provided a computer apparatus including a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor, the processor implementing the method of visualizing the composite feature described above when executing the program.

By means of the technical scheme, the method, the device, the equipment and the storage medium for processing the composite characteristic visualization can accumulate aggregate measurement characteristics according to item IDs after original data are acquired to generate calculation index data. And constructing two-dimensional coordinate data based on the calculated 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, the composite characteristics after multi-characteristic aggregation are used as measurement characterization, two-dimensional visual calculation is participated in measurement indexes, query data are generated through query separation, and T+1 data supply is guaranteed. And the two-dimensional coordinates and the composite features are used for carrying out quasi-real-time visual static representation processing on a plurality of measurement feature data in the same measurement space, so that the accuracy of quality and effect measurement results and quality and effect insight are improved.

The foregoing description is only an overview of the present application, and is intended to be implemented in accordance with the teachings of the present application in order that the same may be more clearly understood and to make the same and other objects, features and advantages of the present application more readily apparent.

Drawings

The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the application and do not constitute a limitation on the application. In the drawings:

FIG. 1 is a schematic flow chart of a quality and effectiveness metric according to an embodiment of the present application;

FIG. 2 shows an application scenario diagram of a software development management system provided by an embodiment of the present application;

FIG. 3 is a schematic flow chart of a method for visualizing composite features according to an embodiment of the present application;

FIG. 4 is a schematic diagram of a software development management system according to an embodiment of the present application;

FIG. 5 is a schematic flow chart of generating calculation index data according to an embodiment of the present application;

FIG. 6 is a schematic flow chart of generating two-dimensional coordinate data according to an embodiment of the present application;

FIG. 7 is a schematic diagram of a query data generation flow provided by an embodiment of the present application;

FIG. 8 is a schematic diagram of a query data visualization process according to an embodiment of the present application;

Fig. 9 is a schematic diagram of a processing flow of visualization of drill-down query data according to an embodiment of the present application;

FIG. 10 is a schematic structural diagram of a visual processing device for composite features according to an embodiment of the present application;

fig. 11 shows a schematic structural diagram of a computer device according to an embodiment of the present application.

Detailed Description

The application will be described in detail hereinafter with reference to the drawings in conjunction with embodiments. It should be noted that, without conflict, the embodiments of the present application and features of the embodiments may be combined with each other.

In the embodiment of the application, quality AND EFFICIENCY Measurement (QEM) refers to quantitative analysis and evaluation of various aspects in the software development process, so as to ensure the Quality of software products and improve the development efficiency. The quality and efficiency metrics can be used to learn the current development work status, thereby identifying points of improvement and tracking project progress.

Quality metrics may include quality metrics, efficiency metrics, process metrics, project metrics, team metrics, persistence improvements, data driven decisions, standardization, and automation. Among other things, quality metrics focus on attributes of software products such as functionality, reliability, ease of use, efficiency, maintainability, and portability. The quality of the software is evaluated by measuring the indexes such as complexity, defect rate, test coverage rate and the like of the code. Efficiency metrics focus on the efficiency of the software development process, including development speed, resource utilization, productivity, and the like. Development efficiency is assessed by metrics such as development cycle, average yield, automated test rate, etc.

Process metrics relate to various stages in the software development process such as demand analysis, design, coding, testing, and maintenance. The efficiency and quality of the development process is assessed by measuring the completion, workload, progress, etc. of each stage. Project metrics focus on the management and control of the entire software project, including project planning, cost, risk, quality, and the like. The actual condition of the project is assessed by measuring project progress, cost control, risk management, etc. Team metrics focus on performance and collaboration of development teams, including team member skills, communications, collaboration, and satisfaction. The team's collaboration efficiency is assessed by measuring the team's communication frequency, code censoring, knowledge sharing, etc.

The quality and efficiency metrics may be used to evaluate the current state, as well as to continue improvement. I.e., through periodic metrics and feedback, identify problems, take action, and track improvement effects. The quality and efficiency metrics may provide insight into data-based, aiding in making more objective and scientific decisions. To improve the accuracy and efficiency of metrology, standardized metrology methods and automated tools may be employed to collect and analyze data.

The metrics, also known as metrics, described in embodiments of the present application are a set of attributes that are used to describe and evaluate the quality of software. In some embodiments of the present application, metrics may be used to generally refer to the quality of a problem on the software line of a research community.

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

The metric (Measurement Metrics) described in embodiments of the present application refers to specific criteria and values used to quantitatively evaluate various aspects of the software development process. Metrics are a specific implementation of Metrics (Metrics) that can provide a quantified type of data to evaluate the quality, performance, development efficiency, and team productivity of software. For example, the metrics may include the number of problems on-line, the number of test defects, and so on.

Since the metrics can evaluate software quality through a series of quantized data that can reflect the characteristics and performance of the software from different angles, the metrics can be represented through a plurality of metric feature dimensions. For example, the metric index may be used to characterize an index feature dimension of a problem type, and the specific content of the index feature dimension may include dimension values of security problems, functional problems, and the like.

In performing quality and efficiency metrics, the concepts of the metrics, the metrics objects, and metrics indicators involved may be represented by specific types and formats of data. The data capable of representing the specific concept may be data generated along with development and test processes in a software development process, or may be data recorded or written by a software development mechanism according to specific conditions of the software development process.

In the process of executing the quality and efficiency measurement, multidimensional data can be acquired according to the quality and efficiency measurement process, and the acquired data can be used for analyzing and processing the software development process to obtain a data analysis result capable of showing the specific situation of the software development. To this end, in some embodiments, a software development management system may be deployed at an electronic device, such as a server or client. The software development management system can be used for collecting various data generated in the software development process, and converting, analyzing, displaying and storing the collected data.

As shown in fig. 1, data generated in the software development process can be acquired from each development end by a sensor or a data interface built in the software development management system, and can also be sent to the software development management system by the development end. In the software development quality and effectiveness metrics process, the collected data may include demand and design data, test data, defect data, process data, project and team data, and the like. The requirements and design data may be the number of requirements, the average number of test cases written per requirement, the total number of test cases written for all requirements, etc. The test data may be the execution status of test cases, including the number of test cases executed, the number of test cases passed, the number of failed test cases, the number of blocked test cases, the number of test cases not executed, and the like.

The defect data is the number of identified test defects, the number of critical defects, the number of high defects, the number of medium defects, the number of low defects, etc. The process data includes maturity metrics (e.g., organizational metrics, resource metrics, training metrics, etc.), management metrics (e.g., project management metrics, quality management metrics, configuration management metrics, etc.), and lifecycle metrics (e.g., problem definition metrics, demand analysis metrics, design metrics, manufacturing metrics, maintenance metrics, etc.). Project and team data may include burnout charts, rate charts, standard deviation, throughput, cumulative flow charts, control charts, signs on work-in-process restriction charts, build and deployment speeds, test speeds, code signing duration, unit test pass rates, integrated test pass rates, and the like.

The collected data can be used as an analysis basis of quality and efficiency metrics in the software development process, and the quality, development efficiency and team performance of the software can be evaluated by analyzing the raw data. The collection and analysis of such data helps identify problems, optimize flow, improve software quality and development efficiency.

As shown in fig. 1, the software development management system may perform feature extraction on the collected data. Wherein, the characteristics refer to data fragments extracted from the collected data according to specific data analysis requirements. The extracted specific features may be represented in different data forms according to the feature type, feature use, feature extraction mode, and the like. For example, after the software development management system obtains the software test data, the field for characterizing the test defect in the test data may be read, and the number of test defects may be counted according to the read field, so as to obtain the test defect number feature. In extracting the characteristics of the number of test defects, the field for characterizing the test defects may also be referred to as a characteristic field.

Based on this, among the extracted features, the features that can be used for quality and efficiency metrics may be referred to as metric features. For example, metric features may include, but are not limited to, issue ID, item ID, validity information, number of occurrences of an issue, time, etc.

Because the extracted features can have an association relationship between part of the features, for example, one part of the features and the other part of the features can jointly reflect a certain abnormal state in the software development process, the metric features of the aspects can be integrated to obtain feature vectors containing multiple dimensions. For example, to reflect an online problem of software development, a feature vector with content (ID 1, F1, c1, time) can be obtained by extracting features in terms of online problem ID, belonging item ID, whether valid, whether obsolete, time, and the like. Wherein ID1 is used to represent an on-line problem, F1 is used to represent a test defect function feature, c1 represents the number of occurrences of the test defect function, and time represents the time of occurrence.

When the analysis result is displayed, the software development management system can comprehensively display analysis results corresponding to two or more measurement features, and therefore, after the measurement features are extracted, the two or more features can be aggregated to obtain an aggregated feature. The aggregate feature, also known as a composite feature, may aggregate multiple related metrics or data points into a single metric value for ease of analysis and understanding. The multi-aspect data can be comprehensively displayed through the aggregation characteristics, and complex data can be simplified, so that the data is easier to analyze and process.

In a software development process management system, data may be collected from a plurality of data sources (e.g., JIRA, TAPD, etc.). Since the collected data may contain inconsistent data entities, it is necessary to have some consistency in the data through modeling, abstraction, mapping, and correlation. By executing feature aggregation, the data can be abstracted into a domain model of each link of the research and development process and stored in a high-relevance data warehouse, so that the inquiry performance, the reading performance and the management convenience are improved.

When feature aggregation is performed, the software development management system needs to perform index extraction and combination, namely, index extraction is performed from a data set, and the data set is combined into an aggregation feature facing to scene requirements. Multiple indexes can be combined into a comprehensive measurement value through the aggregation characteristic so as to meet the information requirement of analysis and decision.

After the software development management system performs data acquisition and feature extraction, feature data conversion, calculation and analysis can be performed based on the extracted features, so that analysis result data can be obtained. In order to present the analysis results, the software development management system may perform visual presentation of the analysis results by running display-related program steps. The presentation of the analysis results may include data visualization reports, data signs, data visualization tools, chart tools, code-based visualization tools, cross-platform chart libraries, and the like.

In one possible implementation, the software development management system may run as a single device, i.e., the user may implement all of the functionality of the software development management system through a single client device. For example, a software development management system is an application installed on a personal computer. An application program for collecting, analyzing, storing and displaying software development data is built in the software development management system application. By running the application, the computer can acquire data generated in the software development process, acquire data input by a user or locally stored, and perform analysis on the acquired data to obtain a corresponding analysis result. And displaying the analysis result data based on a display program provided by the application.

In another possible embodiment, the software development management system may operate with multiple devices, i.e., a cooperative coordination between multiple devices is required to implement the full functionality of the software development management system. For example, as shown in fig. 2, the software development management system includes a server and a plurality of clients connected to the server, the plurality of clients may be operated by different users, respectively, and at least one of the plurality of clients is used to perform a software development operation. As the software development operation proceeds, software development process monitoring data is generated, which may be initially processed locally at the client and then transmitted to the server via the network. And further processing, distributing and storing the data through the server. Different clients may invoke different data from the server based on different user rights. And the server may send specific data to the client as needed for specific data analysis.

In the embodiment of the present application, a software development management system is used as an execution subject of a visualization processing method of a composite feature. The software development management system can be realized by running related application programs through a client, can be realized by running related application programs through a server, and can also be realized by cooperatively running related application programs through the server and the client. Therefore, from the perspective of the hardware device, the steps corresponding to the visualization processing method of the composite feature may be executed by the client, or may be executed by the server, or some steps may be executed by the client, or some steps may be executed by the server.

The software development management system may perform analysis on a single metrology object upon data analysis, based on user selection. For example, by setting a target for the software development management system and setting a measurement object as a test defect, the software development management system may extract features related to the test defect from the collected data according to the set target, and perform defect analysis based on the extracted features, such as determining the content of defect type, defect number, defect generation stage, and the like, so as to obtain a test defect analysis result.

However, when the quality and efficiency measurement is performed on a single measurement object, the single measurement object can only represent the fine granularity representation of the measurement object in a certain quality and efficiency characteristic, and the state transition performance of the characteristic of a certain type of problem can not be comprehensively reflected from the expected insight-oriented problem, so that the accuracy and insight of the quality and efficiency measurement result are reduced, and the effective intervention pre-judgment decision is inconvenient to make.

In order to improve accuracy and insight of quality and efficiency measurement results, in this embodiment, a method for visualizing composite features is provided, as shown in fig. 3 and fig. 4, where the method includes:

S101, collecting original data.

After the software development management system is started to run, the original data can be collected. The original data is data which is generated in the software development process and is not processed by the software development management system. In the quality and efficiency measurement process of executing software development, the original data comprises on-line problem information and test defect information generated in the software development process. For example, the online question information in the original data includes an online question ID, an item ID to which the online question information belongs, whether the online question information is valid, whether the online question information is discarded, and the like. The test defect information includes test defect ID, belonging item ID, whether valid, whether obsolete, etc.

In order to collect raw data, a client or server running a software development management system may be provided with a data collection module. The data acquisition module may include an interface unit for interfacing with the sensor or with an application programming interface (Application Programming Interface, API) of the software development tool. The software development management system can collect the original data through the data collection module.

In some embodiments, the software development management system may collect raw data in real time by way of data monitoring. Namely, after the software development management system is operated, the data acquisition state is maintained. When a client or other electronic equipment connected to the software development management system generates original data, the software development management system can collect the generated original data and preprocess or store the collected data. The original data acquisition mode can acquire more comprehensive data, and the accuracy of the subsequent analysis results is improved.

In other embodiments, the software development management system may also direct the collection of raw data in a command-controlled manner. That is, after the software development management system is operated, a control instruction input by a user can be received, wherein part of the control instruction can be used for controlling the software development management system to perform data acquisition, which is called a data acquisition instruction. The software development management system executes the step of collecting the original data after receiving the data collection instruction.

For example, a user may set up data collection within a certain statistical period through data collection instructions. The software development management system can respond to the data acquisition instruction to acquire the on-line problem information and the test defect information generated by each software project in the statistical period. And marking the acquired on-line problem information and the test defect information into a warehouse for subsequent processing. The data acquisition mode can be controlled by a user, and the original data is acquired when the data acquisition is needed, so that the data acquisition and processing amount is reduced, and the running speed of the system is improved.

S102, calculating index data according to the original data.

After the original data is acquired, the software development management system can perform feature marking and feature extraction on the original data, so that calculation index data are generated according to the original data. Wherein the calculated index data may comprise an index vector. The index vector is used for representing specific problems existing in each item in the software development data through characteristic parameters of multiple dimensions.

For this purpose, before generating the calculated index data, feature extraction from the raw data is required to obtain metric features. I.e. the metrology features are features associated with on-line problem information and test defect information extracted from the raw data. And accumulating and aggregating the extracted measurement features according to the item IDs, thereby obtaining calculation index data. I.e. the calculated index data comprises an index vector generated by accumulating aggregated metric features according to item ID.

As shown in fig. 5, in some embodiments, to generate the computational index data, the software development management system may extract the metric features from the raw data (S1201). The measurement features comprise problem IDs, item IDs, validity information and the like which correspond to the online problem information and the test defect information and can reflect the software development process.

The metric features are then filtered based on the validity information (S1202). The purpose of screening the metrology features is to exclude failed or obsolete features in the metrology features. In one possible embodiment, a filter may be used to filter the metric features. That is, after the metric features are extracted and obtained, the software development management system may set the filtering parameters of the filter, where the filtering parameters are used to set the conditions that should be satisfied by the metric features that should be deleted and retained by the filter. For example, the filter parameter of the filter may be set to "filter (valid, not obsolete)", and the filter may retain valid and not obsolete metric features and delete invalid or obsolete metric features based on the set filter parameter.

After setting the filtering parameters, the filter may be used to read the validity information of the metric feature. And deleting the metric feature if the validity information is first information used for representing that the metric feature is invalid or abandoned. The metrology feature is retained if the validity information is second information that characterizes the metrology feature as valid and not discarded.

The validity information may be directly obtained by reading from the original data, for example, a field for characterizing the validity of the data may be included in the original data, for example, "DATA VALIDITY", where the parameter value of the field is "true" and indicates that the current original data is valid, and where the parameter value of the field is "false" and indicates that the current original data is invalid.

The validity information can also carry out validity judgment on the original data according to a preset validity judgment rule, and is generated according to a judgment result. For example, for some on-line problem items and test defect items with higher timeliness, the time stamp of the original data may be read after the original data is collected to determine the generation time of the original data. Comparing the read generation time with a preset time threshold, if the generation time is earlier than the preset time threshold, the current original data cannot meet the timeliness requirement, so that validity information for representing failure can be generated, namely 'DATA VALIDITY =false' is set. Similarly, if the time of occurrence is later than a preset time threshold, it is indicated that the current raw data meets the timeliness requirement, so validity information for representing validity can be generated, i.e. setting "DATA VALIDITY =true".

After screening the metric features according to the validity information, the software development management system may aggregate the metric features according to the belonging item IDs, generate a composite feature (S1203), and count the aggregated count values of the metric features. And generating a calculation index data vector according to the composite feature, the item ID and the count value (S1204).

For example, for collecting the on-line problem information and the test defect information of the original data line, the on-line problem information comprises an on-line problem ID, an item ID, whether the on-line problem ID is valid, whether the on-line problem ID is abandoned, and the like, and the test defect information comprises a test defect ID, whether the item ID, the on-line problem ID, whether the on-line problem ID is valid, whether the on-line problem ID is abandoned, and the like. Then, by extracting the metric features in the original data, and performing aggregation calculation, namely:

on-line problem ID1- > F1 function problem- > filter (valid, not discarded) - > count=c1;

on-line problem ID1- > F2 security problem- > filter (valid, not discarded) - > count=c2;

wherein ID1 represents an on-line problem ID, ID2 represents a test defect problem ID, F1 is a test defect functional feature, F2 is a test defect safety feature, c1 represents the number of features satisfying ID1 and F1, and c2 represents the number of features satisfying ID1 and F2.

Thereby obtaining calculated index data comprising the following index vectors:

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

R3:(ID1,F2,c2->2,2024-01);R4:(ID2,F2,c4->3,2024-01)。

S103, constructing two-dimensional coordinate data based on the calculation index data.

After the calculation index data is obtained, specific index vectors in the calculation index data can be fused to obtain a composite vector, so that two-dimensional coordinate data is constructed based on the calculation index data. Because of the difference of data sources, vector dimensionality may be non-uniform between different index vectors, so in order to fuse the vectors with non-uniform dimensionality, the software development management system may transform an input with any length into an output with a fixed length through a hash algorithm based on the hash algorithm when fusing the index vectors, that is, output a hash value or a hash value of any two index vectors.

Based on the above, the two-dimensional coordinate data is generated according to a two-dimensional feature vector, and the two-dimensional feature vector comprises hash values generated by a hash function of any two measurement features in the calculation index data. That is, as shown in fig. 6, in some embodiments, in order to generate two-dimensional coordinate data, any two metric features in the index data may be extracted first, and then a hash value is generated through a hash function to form a two-dimensional feature vector. And then the two-dimensional coordinate data is formed by the generated two-dimensional feature vector. Specifically, when the index vector fusion is performed, the software development management system can call a hash function and extract any two measurement features from the calculated index data. The two metric features and the set are then computed to generate a combined feature. Hash value computation is then performed on the combined features using a hash function to generate two-dimensional coordinate data.

For example, the pre-constructed two-dimensional feature is F3, which is used to represent the two-dimensional feature after the fusion of the test defect functional feature F1 and the test defect safety feature F2, i.e., f3=hash (f1+f2). And constructing a two-dimensional coordinate based on the two-dimensional characteristics to obtain the following two-dimensional coordinate data:

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

Vec3:(ID1,F3,c2->2,2024-01);Vec4:(ID2,F3,c4->3,2024-01)。

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

After generating the two-dimensional coordinate data, the software development management system may generate query data for the user to perform a query operation. Wherein, according to regulatory requirements for the software development process, a user may conduct information retrieval from a database, during which a structured query language (Structured Query Language, SQL) or other database query language may be used to request a particular data set. The generated query data can facilitate the database to perform information retrieval, thereby realizing the quasi-real-time visual static embodiment processing.

Taking the example of an SQL database, which is a standard language for managing and manipulating relational databases, a declarative way is provided for defining and retrieving data. In executing a data query, the table name of the data source may be specified by defining a SELECT statement for the SQL query, the SELECT statement including a list of fields to retrieve, and a FROM statement. The record may also be filtered using the WHERE clause, the result ordered BY ORDER BY, and the data grouped and further filtered BY GROUP BY and HAVING. After the database obtains the SELECT statement for the SQL query, information retrieval can be performed according to specific content specified by the SELECT statement, so that relevant query data is retrieved in the database, and then the query data is fed back to the user client for front-end visual data processing.

In order to improve query performance, the database uses the index to quickly locate data for speeding up the query. Thus, after the two-dimensional coordinate data is generated, an index may be added to the two-dimensional coordinate data, thereby generating query data that facilitates performing information retrieval. In some embodiments, as shown in fig. 7, to generate two-dimensional coordinate data, a software development management system may first construct a quasi-real-time query logic generation task, and then perform normalization processing on a target vector dimension in the two-dimensional coordinate data according to the quasi-real-time query logic generation task. And adding name information to the normalized two-dimensional coordinate data to generate query data, and storing the query data according to a document-oriented data storage mode.

For example, after generating the two-dimensional coordinate data, the software development management system may construct a t+1 query logic generation task to generate query data and put the query data into Mongo. Wherein the t+1 query logic generation task is to generate query logic based on the data of the previous cycle (e.g., T day) so as to execute the queries in the next cycle (e.g., t+1 day) to acquire or process the data. MongoDB is an open-source non-SQL database, a storage model using document steering, in which data storage can be performed in accordance with BSON format. Namely, the following data were obtained:

vec1 (ID 1, F3, normalization function (20, 2), 2024-01, project name 1);

Vec2 (ID 2, F3, normalization function (23, 3), 2024-01, project name 2);

F3:(F1,F2);

Wherein the normalization function may be used to scale the data to a specified range, such as to scale the data to the [0,1] interval or the [ -1,1] interval. Scaling of the normalization function may help improve the performance and convergence speed of the algorithm for reducing data redundancy and dependency, improving the integrity and consistency of the data by breaking the data down into smaller tables. For example, the normalization function may be as follows:

Where x is an input variable and may be any real number. e is the base of the natural logarithm. The output value range of the Sigmoid normalization function is between 0 and 1, so that the Sigmoid normalization function can be used for mapping any real number into the interval to realize normalization.

After generating and storing the query data, the software development management system can construct front-end visual coordinates based on the query data. The front-end visual coordinates may be generated and stored in advance according to the query data when the query data is generated and stored, or may be generated in real time according to a query instruction input by a user. Thus, as shown in FIG. 8, in some embodiments, in constructing front-end visualization coordinates based on the query data, a software development management system may obtain a first query instruction and obtain the query data in response to the first query instruction. And recreating a visual container, wherein the visual container is provided with a scale, coordinate axes and visual style parameters. And binding the query data to the visualization container to generate the front end visualization coordinates.

For example, after generating the query data, the software development management system may construct front-end visualization coordinates based on D3js, and query the already generated query data using the hypertext transfer protocol (HyperText Transfer Protocol, HTTP) protocol. Specifically, when front-end visual coordinates are built based on d3.Js, a scalable vector graphics (Scalable Vector Graphics, SVG) canvas may be added first, i.e., an SVG element needs to be added to the HTML document as a visual container. The software development management system may create and set the size of the SVG canvas through the D3.js select and add method. The data and scale are redefined, i.e., the data set to be visualized is prepared, and scales (scales) such as a linear Scale (SCALELINEAR), a ribbon scale (scaleBand) and the like are defined to map the data values to the SVG coordinates. And creating a coordinate axis by using a coordinate axis generator (axis) of D3.js, and designating scales, labels and patterns of the coordinate axis. Rectangular elements are added to represent data points according to the data set and the scale. At the same time, text elements may be added to display the data values. The attributes of the SVG element, such as position (x, y), size (width, height), color (fill), etc., are set to reflect the characteristics of the data. And adding coordinate axes, adding the defined coordinate axes into the SVG canvas, and setting positions to realize the visual processing of query data.

By applying the technical scheme of the embodiment, the visualization processing can be performed on the original data in the software development process based on the composite characteristics of the two-dimensional measurement object multi-measurement object. The composite characteristics after multi-characteristic aggregation are used as measurement characterization, the characteristics of quasi-real time and interpretability of the composite characteristics can be utilized, the characteristics are reflected in measurement indexes to participate in two-dimensional visual calculation, query data are generated through query separation, and the T+1 data supply is ensured. And through two-dimensional coordinates and composite features, a plurality of measurement feature data are subjected to quasi-real-time visual static representation processing in the same measurement space, so that a user is helped to get rid of a one-dimensional observation path of a single index, and the accuracy rate and quality and effect insight of quality and effect measurement results are improved.

Further, as a refinement and extension of the foregoing embodiment, in order to fully describe the implementation process of this embodiment, another composite feature visualization processing method is provided in some embodiments of the present application, as shown in fig. 9, where the method may further include the following steps based on executing steps S101 to S104 in the foregoing embodiment:

S201, acquiring a second query instruction.

The second query instruction is used for controlling the software development management system to execute the drill-down query. Wherein drill-down queries are a data analysis technique that allows users to go deep from high-level data summary information to a more detailed data plane. The drill-down query may be used in a multi-dimensional dataset or a hierarchical data structure to help users explore detailed information of the data. The software development management system may provide a drill-down query portal after performing the visualization process.

For example, a control for viewing the detail data may be included in the result display interface presented by the client, and after the user clicks the control for viewing the detail data, the software development management system may be controlled to execute the drill-down query, i.e. input the second query instruction.

Obviously, the user may input the second query instruction according to different interaction modes according to the specific interaction mode supported by the client or the server. For example, the second query instruction may be input through one or more of voice interaction, gesture interaction, touch interaction, and external device interaction.

S202, responding to the second query instruction, and reading parameter input parameters from the second query instruction.

After the second query instruction is acquired, the software development management system can respond to the second query instruction and read parameter entering parameters from the second query instruction, wherein the parameter entering parameters are vector dimension information specified in the query data.

For example, the software development management system may click on a view detail control corresponding to the test defect to input a second query instruction when presenting a defect display interface corresponding to a composite feature related to the test defect functional feature F1 and the test defect security feature F2. The second query instruction may carry control information of the test defect, so that the parameter input parameter may be read to include F1. Similarly, when the user clicks the view detail control corresponding to the test defect safety feature F2, the input second query instruction may include the test defect safety feature F2, that is, the parameter includes F2.

According to the difference of the control content presented in the display interface of the software development management system, the parameter input by the user according to the second query instruction input by the control is different, and the parameter input can include a plurality of specific characteristic items, for example, the parameter input can be one or more of F1, F2, ID1, ID2 and 2024-01.

S203, inquiring detail data according to the parameter.

After the parameter is obtained by reading, the software development management system can inquire the detail data according to the parameter. The detail data are interpretable data in the process of drill-down inquiry. The detail data includes at least one of the two-dimensional coordinate data, the calculation index data, and the raw data.

By applying the technical scheme of the embodiment, a user can query detail data, namely, original data generated in the process of querying software development, or query result data in quality and effect measurement and any intermediate data, such as query data, calculation index data, two-dimensional coordinate data and the like, in a database according to a second query instruction containing different parameter entering parameters. Different query results can be obtained by different second query instructions, so that the flexibility of data visualization is improved, and the diversified requirements of different users are met.

Further, as a specific implementation of the method for visualizing the composite feature in the foregoing embodiment, an embodiment of the present application further provides a device for visualizing the composite feature, as shown in fig. 10, where the device for visualizing the composite feature includes:

For example, the visualization processing device of the composite feature may collect, in a certain statistical period, effective online problem data and effective test defect data generated by each software project through the data collection module, mark the data feature for storage, construct two-dimensional feature coordinates according to the collected data through the index generation module and the coordinate construction module, and query the logic process through the visualization module to generate query data, thereby generating a visual image based on D3 js.

By applying the technical scheme of the embodiment, the visualization processing device of the composite characteristic can accumulate the aggregate measurement characteristic according to the item ID to generate calculation index data after the original data is acquired. And constructing two-dimensional coordinate data based on the calculated index data, generating query data according to the two-dimensional coordinate data, and constructing front-end visual coordinates based on the query data. The device takes the composite characteristics after multi-characteristic aggregation as measurement characterization, participates in two-dimensional visual calculation in measurement indexes, generates query data through query separation, and ensures the T+1 data supply. And the two-dimensional coordinates and the composite features are used for carrying out quasi-real-time visual static representation processing on a plurality of measurement feature data in the same measurement space, so that the accuracy of quality and effect measurement results and quality and effect insight are improved.

It should be noted that, for other corresponding descriptions of each functional unit related to the visualization processing device for composite features provided in the embodiment of the present application, reference may be made to corresponding descriptions in the visualization processing method for composite features described in the foregoing embodiment, which are not repeated herein.

As shown in fig. 11, the embodiment of the present application further provides a computer device, which may specifically be a personal computer, a server, a network device, etc., where the computer device includes a bus, a processor, a memory, a communication interface, and may also include an input/output interface and a display device. Wherein the processor of the computer device is configured 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, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer device is for storing location information. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement the steps in the method embodiments.

It will be appreciated by those skilled in the art that the structure of the computer device described above is merely a partial structure related to the present application and does not constitute a limitation of the computer device to which the present application is applied, and that a specific computer device may include more or fewer components, or may combine certain components, or have different arrangements of components.

In one embodiment, a computer readable storage medium is provided, which may be non-volatile or volatile, and on which a computer program is stored, which computer program, when being executed by a processor, carries out the steps of the method embodiments described above.

In an embodiment, a computer program product is provided, comprising a computer program which, when executed by a processor, implements the steps of the method embodiments described above.

The user information (including but not limited to user equipment information, user personal information, etc.) and the data (including but not limited to data for analysis, stored data, presented data, etc.) related to the present application are information and data authorized by the user or sufficiently authorized by each party.

Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, database, or other medium used in embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high density embedded nonvolatile Memory, resistive random access Memory (ReRAM), magneto-resistive random access Memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric Memory (Ferroelectric Random Access Memory, FRAM), phase change Memory (PHASE CHANGE Memory, PCM), graphene Memory, and the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, and the like. By way of illustration, and not limitation, RAM can be in various forms such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), etc. The databases referred to in the embodiments provided herein may include at least one of a relational database and a non-relational database. The non-relational database may include, but is not limited to, a blockchain-based distributed database, and the like. The processor referred to in the embodiments provided in the present 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, or the like, but is not limited thereto.

The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.

The foregoing examples illustrate only a few embodiments of the application and are described in detail herein without thereby limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of the application should be assessed as that of the appended claims.

Claims

1. A method for visualization processing of composite features, the method comprising:

2. The method of claim 1, wherein generating calculated metric data from the raw data comprises:

extracting the measurement characteristics from the original data, wherein the measurement characteristics comprise a problem ID, a belonging project ID and validity information, which correspond to the online problem information and the test defect information;

screening the measurement features according to the validity information;

aggregating the metric features according to the belonging item ID, generating a composite feature, and counting the aggregated count value of the metric features;

And generating a calculation index data vector according to the composite characteristic, the belonged item ID and the count value.

3. The method of claim 2, wherein screening the metric features based on the validity information comprises:

setting filtering parameters of a screener;

reading validity information of the metric feature by using the screener;

Deleting the measurement feature if the validity information is first information, wherein the first information is used for representing that the measurement feature is invalid or abandoned;

And if the validity information is second information, reserving the measurement characteristics, wherein the second information is used for representing that the measurement characteristics are valid and not abandoned.

4. The method of claim 1, wherein constructing two-dimensional coordinate data based on the calculated index data comprises:

Calling a hash function;

Extracting any two measurement features from the calculation index data;

Computing two of the metric feature sets to generate a combined feature;

Performing a hash value calculation on the combined feature using the hash function to generate the two-dimensional coordinate data.

5. The method of claim 1, wherein generating query data from the two-dimensional coordinate data comprises:

constructing a quasi real-time query logic generation task;

Generating a task according to the quasi real-time query logic, and executing normalization processing on the dimension of the target vector in the two-dimensional coordinate data;

adding name information to the normalized two-dimensional coordinate data to generate query data;

and storing the query data according to a document-oriented data storage mode.

6. The method of claim 1, wherein constructing front-end visualization coordinates based on the query data comprises:

acquiring a first query instruction;

Responding to the first query instruction, and acquiring the query data;

Creating a visual container, wherein the visual container is provided with a scale, coordinate axes and visual style parameters;

Binding the query data to the visualization container to generate the front end visualization coordinates.

7. The method according to claim 1, wherein the method further comprises:

Acquiring a second query instruction;

Responding to the second query instruction, and reading parameter input parameters from the second query instruction, wherein the parameter input parameters are vector dimension information appointed in the query data;

and inquiring detail data according to the parameter, wherein the detail data is interpretable data in the drilling inquiry process, and the detail data comprises at least one of the two-dimensional coordinate data, the calculation index data and the original data.

8. A composite feature visualization apparatus, the apparatus comprising:

9. A computer device comprising a storage medium, a processor and a computer program stored on the storage medium and executable on the processor, characterized in that the processor implements the method of any one of claims 1 to 7 when executing the computer program.

10. A storage medium having stored thereon a computer program, which when executed by a processor, implements the method of any of claims 1 to 7.