CN112685295A

CN112685295A - Data analysis method and device, electronic equipment and storage medium

Info

Publication number: CN112685295A
Application number: CN202011565389.6A
Authority: CN
Inventors: 苏璟文
Original assignee: Beijing Dajia Internet Information Technology Co Ltd
Current assignee: Beijing Dajia Internet Information Technology Co Ltd
Priority date: 2020-12-25
Filing date: 2020-12-25
Publication date: 2021-04-20

Abstract

The disclosure relates to a data analysis method, a data analysis device, an electronic device and a storage medium. The method comprises the following steps: obtaining test results of at least two analysis objects, wherein the test results comprise index result information corresponding to target test indexes of the at least two analysis objects; determining index baseline information corresponding to the target test index according to the index result information corresponding to the target test index; acquiring index attribute information of a target test index; determining index quality information corresponding to the target test index of each analysis object according to index result information corresponding to the target test index of each analysis object, index baseline information corresponding to the target test index and index attribute information of the target test index; and determining the quality analysis result of each analysis object according to the index quality information corresponding to the target test index of each analysis object. According to the technical scheme provided by the disclosure, the index baseline information can be dynamically determined, and the accuracy and efficiency of data analysis are improved.

Description

Data analysis method and device, electronic equipment and storage medium

Technical Field

The present disclosure relates to the field of data testing technologies, and in particular, to a data analysis method and apparatus, an electronic device, and a storage medium.

Background

At present, data iteration is faster and faster, so that data analysis for ensuring data iteration quality is also emphasized, for example, software quality analysis is emphasized in software version iteration and software testing. In the related art, at least one index is generally set for measuring data quality, but at present, baseline values of all indexes are set manually, so that the baseline values are single and fixed, the accuracy of data analysis results is low, and the data analysis efficiency is low.

Disclosure of Invention

The present disclosure provides a data analysis method, an apparatus, an electronic device, and a storage medium, to at least solve a problem in the related art how to improve the accurate setting of a pointer baseline in data analysis to improve the accuracy and efficiency of data analysis. The technical scheme of the disclosure is as follows:

according to a first aspect of the embodiments of the present disclosure, there is provided a data analysis method, including:

obtaining test results of at least two analysis objects, wherein the test results comprise index result information corresponding to target test indexes of the at least two analysis objects;

determining index baseline information corresponding to the target test index according to index result information corresponding to the target test index;

acquiring index attribute information of the target test index;

determining index quality information corresponding to the target test index of each analysis object according to index result information corresponding to the target test index of each analysis object, index baseline information corresponding to the target test index and index attribute information of the target test index;

and determining the quality analysis result of each analysis object according to the index quality information corresponding to the target test index of each analysis object.

In one possible implementation manner, the index baseline information includes index baseline data and a baseline threshold corresponding to the index baseline data; the step of determining the index baseline information corresponding to the target test index according to the index result information corresponding to the target test index comprises:

according to the index result information corresponding to the target test index, determining index result statistical information corresponding to the target test index;

determining the index baseline data corresponding to the target test index according to the index result statistical information;

and setting the baseline threshold corresponding to the index baseline data.

In a possible implementation manner, the step of determining, according to the index result information corresponding to the target test index of each analysis object, the index baseline information corresponding to the target test index, and the index attribute information of the target test index, the index quality information corresponding to the target test index of each analysis object includes:

acquiring an upper threshold and a lower threshold corresponding to the baseline threshold;

determining index upper limit data and index lower limit data corresponding to the target test index according to the index baseline data corresponding to the target test index, the baseline threshold value corresponding to the target test index, the upper limit threshold value and the lower limit threshold value;

when the index result information is greater than or equal to the index upper limit data and the index attribute information of the target test index corresponding to the index result information is a positive attribute, or when the index result information is less than or equal to the index lower limit data and the index attribute information of the target test index corresponding to the index result information is a negative attribute, determining the index quality information corresponding to the target test index of the analysis object corresponding to the index result information as the upper limit threshold;

when the index result information is larger than the index lower limit data and smaller than the index upper limit data, determining index quality information corresponding to the target test index of each analysis object according to the index result information, the index baseline data corresponding to the index result information, the corresponding baseline threshold value and the index attribute information of the corresponding target test index;

when the index result information is less than or equal to the index lower limit data and the index attribute information of the target test index corresponding to the index result information is a positive attribute, or when the index result information is greater than or equal to the index upper limit data and the index attribute information of the target test index corresponding to the index result information is a negative attribute, determining the index quality information corresponding to the target test index of the analysis object corresponding to the index result information to be the lower limit threshold.

In a possible implementation manner, before the step of determining a quality analysis result of each analysis object according to the index quality information corresponding to the target test index of each analysis object, the data analysis method further includes:

acquiring a weight value corresponding to the target test index;

the step of determining the quality analysis result of each analysis object according to the index quality information corresponding to the target test index of each analysis object comprises the following steps:

and determining the quality analysis result of each analysis object according to the index quality information corresponding to the target test index of each analysis object and the weight value corresponding to the target test index.

In a possible implementation manner, the step of obtaining a weight value corresponding to the target test indicator includes:

acquiring the priority of the target test indexes and the index number of the target test indexes;

determining the weight number according to the index number of the target test index;

acquiring a weight set, wherein the weight set comprises a plurality of weights;

extracting the weight of the weight number from a plurality of weights of the weight set as a target weight;

and acquiring a weight value corresponding to the target test index according to the priority of the target test index and the target weight.

In one possible implementation, the plurality of weights are a plurality of variable weights, and the variable weights include variable parameters; the step of obtaining the weight value corresponding to the target test index according to the priority of the target test index and the target weight comprises:

according to the priority of the target test index, distributing corresponding target weight to the target test index, wherein the target weight is extracted from the variable weights;

determining the weight sum of the target weight corresponding to the target test index;

determining the value of the variable parameter according to a weight threshold and the weight sum;

determining a value of a target weight corresponding to the target test index according to the value of the variable parameter;

and taking the value of the target weight corresponding to the target test index as the weight value corresponding to the target test index.

In a possible implementation manner, the variable weights are a plurality of variable weights distributed at intervals, and the variable weights further include a step parameter; the step of determining the value of the variable parameter based on the weight threshold and the weight sum comprises:

and determining the value of the variable parameter according to the weight threshold, the step parameter and the weight sum.

According to a second aspect of the embodiments of the present disclosure, there is provided a data analysis apparatus including:

the test result acquisition module is configured to execute the test result acquisition of at least two analysis objects, wherein the test result comprises index result information corresponding to target test indexes of the at least two analysis objects;

the index baseline information determining module is configured to determine index baseline information corresponding to the target test index according to index result information corresponding to the target test index;

an index attribute information acquisition module configured to perform acquisition of index attribute information of the target test index;

the index quality information determination module is configured to determine index quality information corresponding to the target test index of each analysis object according to index result information corresponding to the target test index of each analysis object, index baseline information corresponding to the target test index and index attribute information of the target test index;

and the quality analysis result determining module is configured to execute index quality information corresponding to the target test index of each analysis object and determine the quality analysis result of each analysis object.

In one possible implementation manner, the index baseline information includes index baseline data and a baseline threshold corresponding to the index baseline data; the indicator baseline information determination module comprises:

an index result statistical information determination unit configured to perform determining index result statistical information corresponding to the target test index according to the index result information corresponding to the target test index;

an index baseline data determination unit configured to perform determining the index baseline data corresponding to the target test index according to the index result statistical information;

a baseline threshold setting unit configured to perform setting of the baseline threshold corresponding to the index baseline data.

In one possible implementation manner, the indicator quality information determining module includes:

an upper threshold value and lower threshold value acquisition unit configured to perform acquisition of upper and lower threshold values corresponding to the baseline threshold value;

an index upper limit data and index lower limit data determining unit configured to perform determining index upper limit data and index lower limit data corresponding to the target test index according to index baseline data corresponding to the target test index, a baseline threshold value corresponding to the target test index, the upper limit threshold value and the lower limit threshold value;

a first index quality information determination unit configured to perform, when the index result information is greater than or equal to the index upper limit data and the index attribute information of the target test index corresponding to the index result information is a positive attribute, or when the index result information is less than or equal to the index lower limit data and the index attribute information of the target test index corresponding to the index result information is a negative attribute, determining the index quality information corresponding to the target test index of the analysis object corresponding to the index result information as the upper limit threshold value;

a second index quality information determination unit configured to determine, when the index result information is greater than the index lower limit data and less than the index upper limit data, index quality information corresponding to a target test index of each analysis object according to the index result information, index baseline data corresponding to the index result information, a corresponding baseline threshold value, and index attribute information of a corresponding target test index;

a third index quality information determination unit configured to perform, when the index result information is less than or equal to the index lower limit data and the index attribute information of the target test index corresponding to the index result information is a positive attribute, or when the index result information is greater than or equal to the index upper limit data and the index attribute information of the target test index corresponding to the index result information is a negative attribute, determining that the index quality information corresponding to the target test index of the analysis object corresponding to the index result information is the lower limit threshold value.

In one possible implementation, the data analysis apparatus further includes:

the weighted value obtaining module is configured to execute obtaining of a weighted value corresponding to the target test index;

the analysis result determination module includes: an analysis result determination unit configured to perform determination of a quality analysis result of each analysis object according to index quality information corresponding to a target test index of each analysis object and a weight value corresponding to the target test index.

In one possible implementation manner, the weight value obtaining module includes:

a priority and index number acquisition unit configured to perform acquisition of a priority of the target test index and an index number of the target test index;

a weight number determination unit configured to perform determination of a weight number according to an index number of the target test index;

a weight set acquisition unit configured to perform acquisition of a weight set including a plurality of weights;

a target weight acquisition unit configured to perform extraction of the weight number of weights from a plurality of weights of the weight set as a target weight;

and the weight value acquisition unit is configured to acquire a weight value corresponding to the target test index according to the priority of the target test index and the target weight.

In one possible implementation, the plurality of weights are a plurality of variable weights, and the variable weights include variable parameters; the weight value acquiring unit includes:

a target weight assignment subunit configured to perform assigning a corresponding target weight to the target test indicator according to a priority of the target test indicator, the target weight being a weight extracted from the plurality of variable weights;

a weight sum determination subunit configured to perform determining a weight sum of target weights corresponding to the target test metrics;

a first variable parameter value determination subunit configured to perform determining a value of the variable parameter in accordance with a weight threshold and the weight sum;

a target weight value determining subunit configured to determine a target weight value corresponding to the target test indicator according to the variable parameter value;

a weight value determining subunit configured to perform, as a weight value corresponding to the target test indicator, a value of a target weight corresponding to the target test indicator.

In a possible implementation manner, the variable weights are a plurality of variable weights distributed at intervals, and the variable weights further include a step parameter; the first variable parameter value determination subunit includes:

a second variable parameter value determination subunit configured to perform determining a value of the variable parameter in accordance with the weight threshold, the step size parameter, and the weight sum.

According to a third aspect of the embodiments of the present disclosure, there is provided an electronic apparatus including: a processor; a memory for storing the processor-executable instructions; wherein the processor is configured to execute the instructions to implement the method of any of the first aspects above.

According to a fourth aspect of the embodiments of the present disclosure, there is provided a storage medium, wherein instructions that, when executed by a processor of an electronic device, enable the electronic device to perform the method of any one of the first aspects of the embodiments of the present disclosure.

According to a fifth aspect of the embodiments of the present disclosure, there is provided a computer program product containing instructions which, when run on a computer, cause the computer to perform the method of any one of the first aspects of the embodiments of the present disclosure.

The technical scheme provided by the embodiment of the disclosure at least brings the following beneficial effects:

the index baseline information corresponding to the target test index can be automatically determined by determining the index baseline information corresponding to the target test index according to the index result information corresponding to the target test index of at least two analysis objects; and the index baseline information is related to the test results of the at least two analysis objects, so that the index baseline information can be dynamically determined according to the test results of different analysis objects, and thus, the index baseline information can be more effectively used for analyzing the quality of the at least two analysis objects, and the accuracy and efficiency of data analysis can be improved.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the disclosure and are not to be construed as limiting the disclosure.

FIG. 1 is a schematic diagram illustrating an application environment in accordance with an exemplary embodiment.

FIG. 2 is a flow chart illustrating a method of data analysis in accordance with an exemplary embodiment.

FIG. 3 is a flow chart illustrating a method of data analysis in accordance with an exemplary embodiment.

Fig. 4 is a flowchart illustrating a method for determining index baseline information corresponding to a target test index according to index result information corresponding to the target test index, according to an exemplary embodiment.

Fig. 5 is a flowchart illustrating a method for determining index quality information corresponding to a target test index of each analysis object according to index result information corresponding to the target test index of each analysis object, index baseline information corresponding to the target test index, and index attribute information of the target test index, according to an exemplary embodiment.

FIG. 6 is a flowchart illustrating a method for determining a weight value corresponding to a target test indicator according to an example embodiment.

FIG. 7 is a flowchart illustrating a method for determining a weight value corresponding to a target test indicator according to a priority of the target test indicator and a target weight according to an exemplary embodiment.

FIG. 8 is a block diagram illustrating a data analysis device according to an exemplary embodiment.

FIG. 9 is a block diagram illustrating an electronic device for data analysis in accordance with an exemplary embodiment.

Detailed Description

In order to make the technical solutions of the present disclosure better understood by those of ordinary skill in the art, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below with reference to the accompanying drawings.

It should be noted that the terms "first," "second," and the like in the description and claims of the present disclosure and in the above-described drawings are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the disclosure described herein are capable of operation in sequences other than those illustrated or otherwise described herein. The implementations described in the exemplary embodiments below are not intended to represent all implementations consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present disclosure, as detailed in the appended claims.

Referring to fig. 1, fig. 1 is a schematic diagram illustrating an application environment according to an exemplary embodiment, which may include a server 01 and a terminal 02, as shown in fig. 1.

In an alternative embodiment, server 01 may be used for data analysis processing. Specifically, the server 01 may be an independent physical server, a server cluster or a distributed system formed by a plurality of physical servers, or a cloud server providing basic cloud computing services such as a cloud service, a cloud database, cloud computing, a cloud function, cloud storage, a Network service, cloud communication, a middleware service, a domain name service, a security service, a CDN (Content Delivery Network), a big data and artificial intelligence platform, and the like.

In an alternative embodiment, the terminal 02 may be configured to receive the quality analysis results of the at least two analysis objects sent by the server 01, and may display the quality analysis results of the at least two analysis objects. In one example, the terminal 02 may be used to upload test results of at least two analysis objects to trigger the data analysis process. Specifically, the terminal 02 may include, but is not limited to, a smart phone, a desktop computer, a tablet computer, a notebook computer, a smart speaker, a digital assistant, an Augmented Reality (AR)/Virtual Reality (VR) device, a smart wearable device, and other types of electronic devices. Optionally, the operating system running on the electronic device may include, but is not limited to, an android system, an IOS system, linux, windows, and the like.

In addition, it should be noted that fig. 1 illustrates only one application environment of the image processing method provided by the present disclosure.

In the embodiment of the present specification, the server 01 and the terminal 02 may be directly or indirectly connected through a wired or wireless communication method, and the disclosure is not limited herein.

FIG. 2 is a flow chart illustrating a method of data analysis in accordance with an exemplary embodiment. As shown in fig. 2, the data analysis method may include the following steps.

In step S201, test results of at least two analysis objects are obtained, where the test results may include index result information corresponding to target test indexes of the at least two analysis objects.

In the embodiments of the present specification, the analysis object may be software, hardware, or the like. The target test index may refer to an index for measuring the quality of the analysis object. For example, when the analysis object is software, the target test indexes may include indexes such as response time, the maximum number of concurrent users, throughput, the number of bug bugs, and the like. When the analysis object is hardware, the target test index may include indexes such as security and response time. The present disclosure does not limit this, and the target test index may be selected according to actual requirements.

In this embodiment of the present specification, test results of at least two analysis objects may be obtained, for example, the at least two analysis objects may be at least two pieces of software corresponding to at least two software teams, and the at least two pieces of software corresponding to the at least two software teams may be tested to obtain the test results of the at least two pieces of software. The test result may include index result information corresponding to the target test index of the at least two pieces of software. For example, the target test index may include a target test index X and a target test index B, and the test result of each analysis object may include index result information corresponding to the target test index X and index result information corresponding to the target test index B. Index result information corresponding to target test indexes X of at least two analysis objects may include X₁、x₂…x_n(ii) a The index result information corresponding to the target test indexes B of the at least two analysis objects may include B₁、b₂…b_nN may be the number of analysis objects, and n may be greater than or equal to 2. Wherein x is₁And b₁The index result information may be index result information corresponding to the target test index X and the target test index B of the first analysis object, and so on, X_nAnd b_nMay be the order of the nth analysis objectAnd index result information corresponding to the target test index X and the target test index B respectively.

Note that the index result information may be quantitative information of the test result of the target test index. The present disclosure is not limited thereto.

In this embodiment of the present specification, for the timing of obtaining the test results of the at least two analysis objects, in an example, the server may obtain the test results of the at least two analysis objects when detecting the test results of the at least two analysis objects, so as to trigger the data analysis processing procedure.

In another example, when the user views the test results of each analysis object on the terminal side, the test results of all or part of the analysis objects are selected from the test results of each analysis object as the test results of at least two analysis objects, and a data analysis processing procedure for the at least two analysis objects may be triggered, so that the test results of the at least two analysis objects may be obtained. Or, the user may upload the test results of the at least two analysis objects on the terminal side to trigger the data analysis processing procedure, so that the test results of the at least two analysis objects may be obtained.

The above are merely examples, and the present disclosure is not limited as long as the quality of at least two analysis objects can be effectively monitored and managed in time.

In step S203, index baseline information corresponding to the target test index is determined according to the index result information corresponding to the target test index.

In this embodiment, the index baseline information corresponding to the target test index may be determined according to the index result information corresponding to the target test index. In one example, the index result information corresponding to the target test index X described above may include X, for example₁、x₂…x_nX may be₁、x₂…x_nThe mean value of (a) is used as the index baseline information corresponding to the target test index X, which is not limited in this disclosure. The index baseline information corresponding to the target test index can be used to characterize the qualification information of the target test index.

In step S205, index attribute information of the target test index is acquired.

In this embodiment of the present specification, the index attribute information of the target test index may represent an association relationship between index result information corresponding to the target test index and index quality information corresponding to the target test index. In one example, the indicator attribute information may include a positive indicator attribute and a negative indicator attribute. When the target test index is a forward index attribute, the index result information and the index quality information are in positive correlation; when the target test index is a negative index attribute, the index result information and the index quality information are in negative correlation.

For example, when the analysis object is software, the target test indexes may include indexes such as response time, the maximum number of concurrent users, throughput, the number of bug bugs, and the like. The index attribute information of the response time and the number of the bug bugs can be negative index attributes, and the longer the response time is and/or the more the number of the bug bugs is, the lower the corresponding index quality is; the index attribute information of the maximum number of concurrent users and the throughput can be a forward index attribute, and the greater the maximum number of concurrent users and/or the higher the throughput, the higher the corresponding index quality.

In step S207, the index quality information corresponding to the target test index of each analysis object is determined according to the index result information corresponding to the target test index of each analysis object, the index baseline information corresponding to the target test index, and the index attribute information of the target test index.

In this embodiment of the present specification, a baseline score corresponding to the index baseline information may be obtained, and the baseline score may be preset. The difference between the index result information and the index baseline information can be determined, and if the index attribute information is a forward index attribute, the sum of the baseline score and the difference can be used as index quality information; if the index attribute information is a reverse index attribute, the difference between the baseline score and the difference can be used as index quality information. As one example, the baseline score corresponding to the metric baseline information may be set to 6. For example, the metric baseline information for throughput may be 0.6, with a corresponding baseline score of 6; the throughput in the index result information is 0.5, and the difference between the throughput in the index result information and the index baseline information can be determined to be-0.1 to-0.6; since the index of throughput is a forward index, 6+ (-0.1) ═ 5.9 can be used as index quality information corresponding to throughput. This is merely one example of the present disclosure and is not intended to limit the present disclosure.

In step S209, a quality analysis result of each analysis object is determined based on the index quality information corresponding to the target test index of each analysis object.

In this embodiment of the present specification, when the number of the target test indexes is one, the index quality information corresponding to the target test index of each analysis object may be used as the quality analysis result of each analysis object; when the number of the target test indexes is at least two, the sum of the index quality information corresponding to the target test index of each analysis object may be used as the quality analysis result of each analysis object. The present disclosure is not limited thereto.

Optionally, after step S209, at least two analysis objects may be sorted according to the quality analysis result of each analysis object, so as to sort teams of at least two analysis objects, thereby efficiently and accurately implementing quality sorting of the analysis objects and sorting of teams to which the analysis objects belong, and implementing timely and effective monitoring and management of the quality of at least two analysis objects. For example, when the analysis object is software, software quality sequencing and sequencing of teams to which the software belongs can be efficiently and accurately realized, and an effective quality management method is provided for software development, so that guarantee is provided for software quality improvement.

FIG. 3 is a flow chart illustrating a method of data analysis in accordance with an exemplary embodiment. In one possible implementation, as shown in fig. 3, before step S209, the data analysis method may include the following steps:

in step S301, a weight value corresponding to the target test indicator is obtained;

accordingly, step S209 may include the steps of: in step S303, a quality analysis result of each analysis object is determined according to the index quality information corresponding to the target test index of each analysis object and the weight value corresponding to the target test index.

In this embodiment of the present specification, a mapping relationship between a target test indicator and a weight value may be obtained, so that the weight value corresponding to the target test indicator may be determined according to the mapping relationship. The corresponding weight value may be set according to the priority of the target test indicator, and the priority may be set according to the requirement of the analysis object, which is not limited by the present disclosure.

In this embodiment, a quality analysis result of each analysis object may be determined according to the index quality information corresponding to the target test index of each analysis object and the weight value corresponding to the target test index. For example, the index quality information corresponding to the target test index of one analysis object includes the index quality information f corresponding to the target test index X_xIndex quality information f corresponding to target test index B_bThen the mass analysis result of the one analysis object may be α × f_x+β×f_bWhere α may be a weighted value corresponding to the target test indicator X, and β may be a weighted value corresponding to the target test indicator B. The sum of α and β may be 1.

The quality analysis result of each analysis object is determined by setting the weight value corresponding to the target test index and according to the weight value corresponding to the target test index and the index quality information corresponding to the target test index, so that the quality analysis result can be more accurate; the weight values of different target test indexes can be set according to actual requirements, and then the specific gravity of different target test indexes can be adjusted in a targeted mode.

Fig. 4 is a flowchart illustrating a method for determining index baseline information corresponding to a target test index according to index result information corresponding to the target test index, according to an exemplary embodiment. In one possible implementation, the metric baseline information may include metric baseline data and a baseline threshold corresponding to the metric baseline data. The baseline threshold may represent a qualified score in a uniform score evaluation system, for example, when the uniform score evaluation system is 0-100 minutes, the baseline threshold may be 60 minutes; the baseline threshold may be 90 points when the uniform score evaluation system is 0-150 points. The present disclosure is not limited thereto.

As shown in fig. 4, in one possible implementation manner, step S203 may include:

in step S401, according to the index result information corresponding to the target index, the index result statistical information corresponding to the target test index is determined.

In one example, the metric result statistics may include a mean and a standard deviation of the metric result information. For example, an index result corresponding to a target test index X may include X₁、x₂…x_nThe average value corresponding to the target test index X can be obtained

Standard deviation corresponding to target test index X

n may be the number of analysis objects. The index result statistical information is not limited in the present disclosure, and may be, for example, a variance of the index result information, an intermediate value of the index result information, or the like.

In step S403, according to the index result statistical information, index baseline data corresponding to the target test index is determined.

In one example, the sum of the average value and the standard deviation in the index result statistical information may be used as the index baseline data corresponding to the target test index, for example, the index baseline data a corresponding to the target test index is x + s. The present disclosure is not limited thereto.

In step S405, a baseline threshold corresponding to the index baseline data is set.

In the embodiment of the present specification, the index baseline data may be used to represent a qualified line of the target test index, that is, when the index result information reaches the index baseline data, the target test index of the analysis object may be considered to be qualified; when the index result information does not reach the index baseline data, the target test index of the analysis object can be considered as unqualified.

In this embodiment of the present description, the corresponding baseline threshold is set for the index baseline data, because units of the index result information of different target test indexes may not be uniform, so that the index baseline data of different target test indexes are selected to be uniformly mapped into a score evaluation system, such as a score evaluation system of 0 to 100, so that a uniform score evaluation system of 0 to 100 can be used to determine uniform index quality information for the index result information. Then, since the index baseline data may be used to characterize the qualified line of the target test index, the baseline threshold corresponding to the index baseline data may be set as the passing score in the score evaluation system, such as 60. The present disclosure is not limited thereto.

By determining the index baseline information corresponding to the target test index and setting the index baseline information to comprise the index baseline data and the corresponding baseline threshold value, the index result information corresponding to different target test indexes can be determined by a unified score evaluation system to determine the index quality information, and the consistency of the index quality information is ensured. Therefore, the index result information can be more accurately and effectively analyzed, and the accuracy of quality sequencing of the analysis objects can be improved.

Fig. 5 is a flowchart illustrating a method for determining index quality information corresponding to a target test index of each analysis object according to index result information corresponding to the target test index of each analysis object, index baseline information corresponding to the target test index, and index attribute information of the target test index, according to an exemplary embodiment. As shown in fig. 5, in a possible implementation manner, the step S207 may include:

in step S501, an upper threshold and a lower threshold corresponding to the baseline threshold are acquired.

In this embodiment of the present specification, an upper threshold and a lower threshold corresponding to the baseline threshold may be obtained, and the upper threshold and the lower threshold corresponding to the baseline threshold may be preset, for example, may be set according to a score range corresponding to the baseline threshold. For example, a baseline threshold of 60, an upper threshold of 100 and a lower threshold of 0 may be obtained for the corresponding score range. The present disclosure is not limited thereto.

In step S503, the index upper limit data and the index lower limit data corresponding to the target test index are determined according to the index baseline data corresponding to the target test index, the baseline threshold value, the upper limit threshold value, and the lower limit threshold value corresponding to the target test index.

In one example, when the target test indicator is a forward attribute, the indicator upper limit data a corresponding to the target test indicator may be determined according to formula (1)_maxThe lower limit data A of the target test index can be determined according to the formula (2)_min。

Wherein, Y_{Base line}May be a baseline threshold; y is_{Upper limit of}May be an upper threshold; y is_{Lower limit of}May be a lower threshold; a. the_maxMay be index upper limit data; a. the_minMay be index lower limit data; a may be the indicator baseline data.

From the equations (1) and (2), it is possible to obtain

Taking 0-100 as an example, the upper threshold may be 100, the lower threshold may be 0, and the baseline threshold may be 60. Thus, 10 can be determinedIndex upper limit data corresponding to 0

For example, the metric baseline data a of the throughput metric is 0.6, the corresponding baseline threshold value is 60, and the metric upper limit data that can determine the throughput metric may be

The index lower limit data of the throughput index may be 0.

When the target test index is a negative attribute, the index upper limit data a corresponding to the target test index can be determined according to the formula (3)_maxThe lower limit data A of the target test index can be determined according to the formula (4)_min。

From the equations (3) and (4), it is possible to obtain

Taking 0-100 as an example, the upper threshold may be 100, the lower threshold may be 0, and the baseline threshold may be 60. Thus, the upper limit data of the index corresponding to 100 can be determined

For example, if the index baseline data a of the bug index is 6, and the corresponding baseline threshold value is 60, it may be determined that the index upper limit data of the bug index may be 2 × 6 — 12; lower limit of bug indexThe data may be 6/3 ═ 2.

Through the formulas (1) to (4), when a score evaluation system is determined, a baseline threshold corresponding to index baseline data of a target test index, an upper limit threshold corresponding to index upper limit data of the target test index and a lower limit threshold corresponding to index lower limit data of the target test index can be determined; thus, the index data range of the target test index can be mapped to a threshold range in the score evaluation system, such as a range of 0 to 100. Therefore, when the index quality information is determined subsequently, the range of the index result information can be determined according to the index upper limit data and the index lower limit data, so that the index quality information of the target test index can be determined based on the upper limit threshold, the lower limit threshold and the baseline threshold according to the formula corresponding to the range.

In step S505, when the index result information is greater than or equal to the index upper limit data and the index attribute information of the target test index corresponding to the index result information is a positive attribute, or when the index result information is less than or equal to the index lower limit data and the index attribute information of the target test index corresponding to the index result information is a negative attribute, it is determined that the index quality information corresponding to the target test index of the analysis object corresponding to the index result information is the upper limit threshold.

In step S507, when the index result information is greater than the index lower limit data and less than the index upper limit data, the index quality information corresponding to the target test index of each analysis object is determined according to the index result information, the index baseline data corresponding to the index result information, the corresponding baseline threshold value, and the index attribute information of the corresponding target test index.

In this embodiment, a difference between the indicator result information and the corresponding indicator baseline data may be determined, so that a differential score corresponding to the difference may be determined according to the difference, the corresponding indicator baseline data, and the corresponding baseline threshold, and the differential score may be within a threshold range. In one example, the delta score can be as in equation (5) or equation (6)

I.e. the difference can be mapped to within a threshold range. Further, according to the index attribute information, when the index attribute information of the target test index corresponding to the index result information is a forward attribute, the sum of the baseline threshold value and the differential score can be used as index quality information; when the target result information corresponds to the target test index, the index quality information may be obtained by comparing the baseline threshold value with the differential score.

In step S509, when the index result information is less than or equal to the index lower limit data and the index attribute information of the target test index corresponding to the index result information is a positive attribute, or when the index result information is greater than or equal to the index upper limit data and the index attribute information of the target test index corresponding to the index result information is a negative attribute, it is determined that the index quality information corresponding to the target test index of the analysis object corresponding to the index result information is the lower limit threshold.

In the embodiment of the present specification, when the target test index is a forward attribute, the index quality information F corresponding to the target test index of each analysis object may be determined according to the following formula (5).

In this embodiment of the present specification, when the target test indicator is a negative attribute, the indicator quality information F corresponding to the target test indicator of each analysis object may be determined according to the following formula (6).

Wherein, x can be index result information corresponding to a target test index of any analysis object; a can be index baseline data corresponding to a target test index corresponding to x; y is_{Base line}May be a baseline threshold; y is_{Upper limit of}May be an upper threshold; y is_{Lower limit of}May be a lower threshold.

FIG. 6 is a flowchart illustrating a method for determining a weight value corresponding to a target test indicator according to an example embodiment. In one possible implementation, as shown in fig. 6, the step may include:

in step S601, the priority of the target test index and the index number of the target test index are acquired.

In this embodiment of the present disclosure, the priority of the target test indicator may be preset, for example, the priority of each target test indicator is set according to the actual requirement of data analysis, and this disclosure does not limit this.

In the embodiment of the present specification, the priority of the target test index may be obtained, and the number of indexes of the target test index may be obtained.

In step S603, the number of weights is determined according to the index number of the target test index.

In this embodiment, the number of weights may be determined according to the number of indexes of the target test index. For example, the number of indexes may be used as the number of weights, such as the number of indexes of the target test index is 3, that is, 3 target test indexes, and correspondingly, the number of weights may be 3.

In one example, the number of weights may be determined using the following equation (7):

wherein M can be an index number; n may be the number of weights.

In step S605, a weight set is obtained, and the weight set may include a plurality of weights;

in step S607, a weight of the number of weights is extracted from the plurality of weights of the weight set as a target weight;

in step S609, a weight value corresponding to the target test indicator is determined according to the priority of the target test indicator and the target weight.

In one example, the plurality of weights of the set of weights may be constant weights, e.g., the plurality of constant weights may include 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9. The present disclosure is not limited thereto. If the number of weights is 2, 2 weights can be extracted from the plurality of weights as target weights, such as 0.3 and 0.7; therefore, the target test indexes can be assigned with corresponding target weights according to the priorities of the target test indexes, for example, the target weights corresponding to the target test indexes with higher priorities can be set to be higher, so that the weight values corresponding to the target test indexes can be determined.

FIG. 7 is a flowchart illustrating a method for determining a weight value corresponding to a target test indicator according to a priority of the target test indicator and a target weight according to an exemplary embodiment. In one possible implementation, the plurality of weights may be a plurality of variable weights, and the variable weights may include variable parameters. As shown in fig. 7, the step S609 may include:

in step S701, a corresponding target weight is assigned to the target test indicator according to the priority of the target test indicator.

In this embodiment, a corresponding target weight may be assigned to a target test indicator according to a priority of the target test indicator. Wherein the target weight is a weight extracted from a plurality of variable weights. For example, the weight of the above-mentioned weight number may be extracted from a plurality of variable weights as a target weight, and the target test index may be assigned a corresponding target weight according to its priority. For example, a higher target weight may be assigned to a target test metric having a higher priority.

In step S703, determining a weight sum of target weights corresponding to the target test indexes;

in step S705, the value of the variable parameter is determined based on the weight threshold and the weight sum.

In the embodiment of the present specification, the weight threshold may be 1, which is not limited in the present disclosure. When the weight threshold is 1, the sum of weights may be equal to 1, and thus the value of the variable parameter is calculated.

In one example, the plurality of variable weights may include b, 2b, 3b, …, nb. The present disclosure is not limited thereto. Variable weights of the weight number can be obtained from the plurality of variable weights as target weights; the weight sum of the target weight corresponding to the target test index can be determined; the value of the variable parameter in the variable weight, for example the value of the variable parameter b, is determined based on the weight threshold and the weight sum. For example, the target weight includes b and 3b, the weight threshold is 1, and b +3b may be 1, which results in b being 0.25. Thus, the weight values of the target weights are 0.25 and 0.75; further, 0.25 or 0.75 may be assigned to the corresponding target test indicator according to the priority of the target test indicator, as a weight value corresponding to the target test indicator.

In another example, the plurality of variable weights may be a plurality of variable weights distributed at intervals, and the variable weights may further include a step parameter; the step S705 may include: and determining the value of the variable parameter according to the weight threshold, the step parameter and the weight sum. The step length parameter k may be a preset constant, and the value range of k may be [0.1, 0.5 ]]The present disclosure is not limited thereto. The plurality of spaced variable weights may include a, a + k, a +2k, a + (N-1) k, N may be an integer greater than or equal to 1. For example, when the index number M is 2, the weight number

One variable weight can be extracted from a plurality of variable weights distributed at intervals as a target weight, for example, the extracted target weight is a; a may be assigned to 2 target test indicators; then the sum of the weights of the target weights corresponding to the target test indexes a + a is 2 a; when 2a is equal to 1, a is equal to 0.5, that is, the target weights corresponding to the 2 target test indexes are 0.5.

Or, when the index number M is 3, the weight number

2 variable weights can be extracted from a plurality of variable weights distributed at intervals as target weights, such as the extracted target weights are a and a + k; a and a + k may be assigned to 3 target test indices; if the result of the assignment is a, a, (a + k); then 3 target test indexesThe sum of the weights of the corresponding target weights, a + a + (a + k), is 1, so that a can be (1-k)/3. When k is 0.4, a may be obtained as (1-0.4)/3 as 0.2, so that the weight values corresponding to the target test index may be determined as 0.2, 0.2, 0.6. If the result of the assignment is a, (a + k), (a + k); then the sum of the weights of the target weights corresponding to the 3 target test indexes, a + (a + k) + (a + k), is 1, so that a can be obtained as (1-2 k)/3. When k is 0.4, a ═ (1-2 × 0.4)/3 ═ 0.2/3 can be obtained, so that the weight values corresponding to the target test index can be determined to be 0.2/3, 0.2/3+0.4, 0.2/3+ 0.4.

In step S707, a value of the target weight corresponding to the target test index is determined based on the value of the variable parameter.

In step S709, the value of the target weight corresponding to the target test index is set as the weight value corresponding to the target test index.

In this embodiment of the present specification, a value of a target weight corresponding to a target test indicator is determined according to a value of a variable parameter, and the value of the target weight corresponding to the target test indicator may be used as a weight value corresponding to the target test indicator.

The weight set comprises a plurality of variable weights, so that the weight value corresponding to the target test index can be dynamically determined according to the number of the indexes, and the setting of the weight value corresponding to the target test index is more flexible and efficient.

FIG. 8 is a block diagram illustrating a data analysis device according to an exemplary embodiment. Referring to fig. 8, the data analysis apparatus may include:

a test result obtaining module 801 configured to perform obtaining of test results of at least two analysis objects, where the test results include index result information corresponding to target test indexes of the at least two analysis objects;

an index baseline information determining module 803 configured to determine index baseline information corresponding to the target test index according to the index result information corresponding to the target test index;

an index attribute information acquisition module 805 configured to perform acquisition of index attribute information of a target test index;

an index quality information determination module 807 configured to perform determining index quality information corresponding to the target test index of each analysis object according to the index result information corresponding to the target test index of each analysis object, the index baseline information corresponding to the target test index, and the index attribute information of the target test index;

the quality analysis result determination module 809 is configured to perform determination of a quality analysis result of each analysis object according to the index quality information corresponding to the target test index of each analysis object.

In one possible implementation, the index baseline information includes index baseline data and a baseline threshold corresponding to the index baseline data; the index baseline information determination module comprises:

the index result statistical information determining unit is configured to determine index result statistical information corresponding to the target test index according to the index result information corresponding to the target test index;

the index baseline data determining unit is configured to determine index baseline data corresponding to the target test index according to the index result statistical information;

and the baseline threshold setting unit is configured to execute setting of a baseline threshold corresponding to the index baseline data.

an index upper limit data and index lower limit data determining unit configured to determine index upper limit data and index lower limit data corresponding to a target test index according to index baseline data corresponding to the target test index, a baseline threshold value corresponding to the target test index, an upper limit threshold value and a lower limit threshold value;

a first index quality information determination unit configured to perform, when the index result information is greater than or equal to the index upper limit data and the index attribute information of the target test index corresponding to the index result information is a positive attribute, or when the index result information is less than or equal to the index lower limit data and the index attribute information of the target test index corresponding to the index result information is a negative attribute, determining that the index quality information corresponding to the target test index of the analysis object corresponding to the index result information is an upper limit threshold value;

a second index quality information determination unit configured to perform, when the index result information is greater than the index lower limit data and less than the index upper limit data, determining index quality information corresponding to the target test index of each analysis object according to the index result information, the index baseline data corresponding to the index result information, the corresponding baseline threshold value, and the index attribute information of the corresponding target test index;

In one possible implementation, the data analysis apparatus further includes:

the analysis result determination module includes: and the analysis result determining unit is configured to execute quality analysis result determination of each analysis object according to the index quality information corresponding to the target test index of each analysis object and the weight value corresponding to the target test index.

a priority and index number acquisition unit configured to perform acquisition of a priority of a target test index and an index number of the target test index;

a weight number determination unit configured to perform determining a weight number according to an index number of the target test index;

a target weight obtaining unit configured to perform, as a target weight, a weight of which the number of weights is extracted from a plurality of weights of the weight set;

and the weight value acquisition unit is configured to execute acquisition of a weight value corresponding to the target test index according to the priority of the target test index and the target weight.

In one possible implementation, the plurality of weights are a plurality of variable weights, and the variable weights include variable parameters; the weight value acquisition unit includes:

a weight sum determination subunit configured to perform determining a weight sum of target weights corresponding to the target test indexes;

a first variable parameter value determination subunit configured to perform determining a value of a variable parameter according to a weight threshold and a weight sum;

a value determination subunit of the target weight, configured to perform determining a value of the target weight corresponding to the target test indicator according to the value of the variable parameter;

a weight value determination subunit configured to perform setting of a value of a target weight corresponding to the target test index as a weight value corresponding to the target test index.

In a possible implementation manner, the plurality of variable weights are a plurality of variable weights distributed at intervals, and the variable weights further include a step parameter; the first variable parameter value determination subunit includes:

a second variable parameter value determination subunit configured to perform determining a value of the variable parameter according to the weight threshold, the step size parameter, and the weight sum.

With regard to the apparatus in the above-described embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment related to the method, and will not be elaborated here.

Fig. 9 is a block diagram illustrating an electronic device for data analysis, which may be a server, according to an example embodiment, and an internal structure thereof may be as shown in fig. 9. The electronic device includes a processor, a memory, and a network interface connected by a system bus. Wherein the processor of the electronic device is configured to provide computing and control capabilities. The memory of the electronic equipment comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The network interface of the electronic device is used for connecting and communicating with an external terminal through a network. The computer program is executed by a processor to implement a method of data analysis.

Those skilled in the art will appreciate that the architecture shown in fig. 9 is merely a block diagram of some of the structures associated with the disclosed aspects and does not constitute a limitation on the electronic devices to which the disclosed aspects apply, as a particular electronic device may include more or less components than those shown, or combine certain components, or have a different arrangement of components.

In an exemplary embodiment, there is also provided an electronic device including: a processor; a memory for storing the processor-executable instructions; wherein the processor is configured to execute the instructions to implement the data analysis method as in the embodiments of the present disclosure.

In an exemplary embodiment, there is also provided a storage medium having instructions that, when executed by a processor of an electronic device, enable the electronic device to perform a data analysis method in an embodiment of the present disclosure.

In an exemplary embodiment, a computer program product containing instructions that, when executed on a computer, cause the computer to perform the data analysis method in the embodiments of the present disclosure is also provided.

It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in embodiments provided by the present disclosure may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This disclosure is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.

It will be understood that the present disclosure is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A method of data analysis, comprising:

acquiring index attribute information of the target test index;

2. The data analysis method of claim 1, wherein the indicator baseline information includes indicator baseline data and a baseline threshold corresponding to the indicator baseline data; the step of determining the index baseline information corresponding to the target test index according to the index result information corresponding to the target test index comprises:

and setting the baseline threshold corresponding to the index baseline data.

3. The data analysis method according to claim 2, wherein the step of determining the index quality information corresponding to the target test index of each analysis object according to the index result information corresponding to the target test index of each analysis object, the index baseline information corresponding to the target test index, and the index attribute information of the target test index comprises:

4. The data analysis method according to claim 1, wherein before the step of determining the quality analysis result of each analysis object based on the index quality information corresponding to the target test index of each analysis object, the data analysis method further comprises:

acquiring a weight value corresponding to the target test index;

5. The data analysis method of claim 4, wherein the step of obtaining the weight value corresponding to the target test indicator comprises:

6. The data analysis method of claim 5, wherein the plurality of weights are a plurality of variable weights, the variable weights including variable parameters; the step of obtaining the weight value corresponding to the target test index according to the priority of the target test index and the target weight comprises:

7. The data analysis method of claim 6, wherein the plurality of variable weights are a plurality of variable weights distributed at intervals, the variable weights further comprising a step size parameter; the step of determining the value of the variable parameter based on the weight threshold and the weight sum comprises:

8. A data analysis apparatus, comprising:

9. An electronic device, comprising:

a processor;

a memory for storing the processor-executable instructions;

wherein the processor is configured to execute the instructions to implement the data analysis method of any one of claims 1 to 7.

10. A storage medium in which instructions, when executed by a processor of an electronic device, enable the electronic device to perform a data analysis method as claimed in any one of claims 1 to 7.