CN112463584A - Accurate test analysis method and device based on defect analysis - Google Patents
Accurate test analysis method and device based on defect analysis Download PDFInfo
- Publication number
- CN112463584A CN112463584A CN202011174762.5A CN202011174762A CN112463584A CN 112463584 A CN112463584 A CN 112463584A CN 202011174762 A CN202011174762 A CN 202011174762A CN 112463584 A CN112463584 A CN 112463584A
- Authority
- CN
- China
- Prior art keywords
- defect
- risk value
- function
- function point
- calculating
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 230000007547 defect Effects 0.000 title claims abstract description 320
- 238000012360 testing method Methods 0.000 title claims abstract description 63
- 238000004458 analytical method Methods 0.000 title claims abstract description 56
- 238000000034 method Methods 0.000 claims abstract description 17
- 238000007726 management method Methods 0.000 claims description 44
- 238000007781 pre-processing Methods 0.000 claims description 24
- 230000009471 action Effects 0.000 claims description 14
- 230000002950 deficient Effects 0.000 claims description 11
- 238000004422 calculation algorithm Methods 0.000 claims description 9
- 238000011156 evaluation Methods 0.000 claims description 9
- 238000004364 calculation method Methods 0.000 claims description 8
- 230000011218 segmentation Effects 0.000 claims description 8
- 230000001939 inductive effect Effects 0.000 claims description 6
- 238000012545 processing Methods 0.000 claims description 6
- 230000006698 induction Effects 0.000 claims description 3
- 238000012795 verification Methods 0.000 abstract description 4
- 230000000875 corresponding effect Effects 0.000 description 33
- 230000002596 correlated effect Effects 0.000 description 4
- 238000012986 modification Methods 0.000 description 4
- 230000004048 modification Effects 0.000 description 4
- 238000006467 substitution reaction Methods 0.000 description 3
- 230000002457 bidirectional effect Effects 0.000 description 2
- 238000007621 cluster analysis Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000001914 filtration Methods 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000013524 data verification Methods 0.000 description 1
- 238000012217 deletion Methods 0.000 description 1
- 230000037430 deletion Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000012805 post-processing Methods 0.000 description 1
- 238000012502 risk assessment Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/36—Preventing errors by testing or debugging software
- G06F11/3668—Software testing
- G06F11/3672—Test management
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/36—Preventing errors by testing or debugging software
- G06F11/3668—Software testing
- G06F11/3672—Test management
- G06F11/3684—Test management for test design, e.g. generating new test cases
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/30—Computing systems specially adapted for manufacturing
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Computer Hardware Design (AREA)
- Quality & Reliability (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention provides a method and a device for accurate test analysis based on defect analysis, wherein the method comprises the following steps: performing defect pretreatment on the defect sample, and analyzing a defect function point; calculating the remaining defect number of the defect functional points by using a defect analysis method; determining a basic risk value according to the number of the left-over defects, the defect discovery rate meeting the functional export condition and the test time of the project plan; analyzing the incidence relation and incidence probability of the defect function points, and calculating the incidence risk value of the defect function points; calculating the risk value of the defect function point according to the basic risk value and the associated risk value of the defect function point; and selecting a test case according to the risk value of the defect function point. The method can effectively improve the test hit rate, improve the version test quality and efficiency, effectively ensure the inherent service function of the product, and provide support for determining the regression verification range.
Description
Technical Field
The invention relates to the technical field of testing, in particular to a method and a device for accurate testing and analysis based on defect analysis.
Background
In the current software project product, the tested system has more and more functions along with the superposition of versions, and the product with the periodically released and upgraded version is adopted, so that the test guarantee problem of the inherent function is often faced during the final system test. Under the condition that the manpower and the time are limited, how to accurately put the test result into the risk module and the risk function, especially how to guarantee the inherent function of the product, the automatic test of the function can meet the requirement, and the manual key verification is needed, which is often deduced through the experience and the time of a test manager, and the effective evaluation support is lacked.
The existing accurate test mainly focuses on establishing an incidence relation between codes and cases, and bidirectional backtracking, intelligent regression test case selection, coverage analysis, defect positioning, test case cluster analysis and test case automatic generation are realized by utilizing the relation. The risk points cannot be located due to the lack of analysis and prediction of defects.
Disclosure of Invention
Aiming at the existing accurate test, the method mainly focuses on establishing the incidence relation between codes and cases, and utilizes the relation to realize bidirectional backtracking, intelligent regression test case selection, coverage analysis, defect positioning, test case cluster analysis and test case automatic generation. The invention provides a defect analysis-based accurate test analysis method and device, and solves the problems that analysis and prediction of defects are lacked and risk points cannot be located.
The technical scheme of the invention is as follows:
on one hand, the technical scheme of the invention provides a precise test analysis method based on defect analysis, which comprises the following steps:
performing defect pretreatment on the defect sample, and analyzing a defect function point;
calculating the remaining defect number of the defect functional points by using a defect analysis method;
determining a basic risk value according to the number of the left-over defects, the defect discovery rate meeting the functional export condition and the test time of the project plan;
analyzing the incidence relation and incidence probability of the defect function points, and calculating the incidence risk value of the defect function points;
calculating the risk value of the defect function point according to the basic risk value and the associated risk value of the defect function point;
and selecting a test case according to the risk value of the defect function point.
Further, the step of preprocessing the defect sample and analyzing the defect function point comprises the following steps:
and acquiring defect sample data and constructing a dictionary.
Further, the step of preprocessing the defect sample to analyze the defect function point includes:
inducing a management object corresponding to the defective sample data and a management action supported by the management object;
using a word segmentation algorithm based on a dictionary, matching a management object and a management action corresponding to each defect in a forward direction to the maximum extent to form a service function table corresponding to the defect; wherein the service function table includes a defect function point, a defect creation time, and a defect level.
Further, the step of calculating the number of remaining defects of the defective functional point using a defect analysis method includes:
generating defect statistical data according to the service function table, wherein the defect statistical data comprise defect function points and accumulated DI values corresponding to the defect function points;
analyzing a trend curve of each defect functional point and a corresponding accumulated DI value curve by using a Gompertz defect analysis method;
and estimating the residual DI value of the defect function point, wherein the DI value is the weighted value of the defect number.
Further, in the step of determining the basic risk value according to the number of defects satisfying the functional export condition and the test time of the project plan, the basic risk value fo _ r is Y (T/TR);
wherein Y is the number of defects satisfying the defect exit condition;
t: the time required by the original manpower input of the project;
TR: project planning time.
Further, the step of analyzing the association relationship and the association probability and calculating the association risk value of the defect function point includes:
traversing the preprocessed defect function points and then processing to establish an incidence relation and a corresponding incidence probability of each defect function point;
calculating the associated risk value of the defect function point; and the associated risk value of the defect function point is equal to the sum of the basic risk value of each defect function point which has the associated relation with the defect function point and the corresponding associated probability.
Further, the step of calculating the risk value of the defect function point according to the basic risk value and the associated risk value of the defect function point includes:
and adding the basic risk value and the associated risk value of the defect function point to obtain the risk value of the defect function point.
On the other hand, the technical scheme of the invention also provides an accurate test analysis device based on defect analysis, which comprises a defect preprocessing module, a residual defect risk value estimation module, a basic risk value determination module, an associated business evaluation module, a comprehensive calculation risk module and a test case selection module;
the defect preprocessing module is used for preprocessing the defects of the defect sample and analyzing the defect function points;
the residual defect risk value estimation module is used for calculating the residual defect number of the defect functional points by using a defect analysis method;
the basic risk value determining module is used for determining a basic risk value according to the defect number meeting the functional export condition and the test time of the project plan;
the correlation service evaluation module is used for analyzing the correlation relation and the correlation probability of the defect function points and calculating the correlation risk value of the defect function points;
the comprehensive risk calculation module is used for calculating the risk value of the defect function point according to the basic risk value and the associated risk value of the defect function point;
and the test case selection module is used for selecting the test cases according to the risk values of the defect function points.
Further, the device also comprises a preprocessing module;
and the preprocessing module is used for acquiring defect sample data and constructing a dictionary.
Further, the defect preprocessing module comprises a generalization unit and a matching unit;
the induction unit is used for inducing the management object corresponding to the defective sample data and the management action supported by the management object;
the matching unit is used for matching the management object and the management action corresponding to each defect to the maximum extent in the forward direction by using a word segmentation algorithm based on a dictionary to form a service function table corresponding to the defect; wherein the service function table includes a defect function point, a defect creation time, and a defect level.
Further, the left-behind defect risk value estimation module comprises a defect statistical data generation unit, an analysis unit and an estimation unit;
the defect statistical data generating unit is used for generating defect statistical data according to the service function table, wherein the defect statistical data comprise defect function points and accumulated DI values corresponding to the defect function points;
the analysis unit is used for analyzing a trend curve of each defect function point and a corresponding accumulated DI value curve by using a Gompertz defect analysis method;
and the estimation unit is used for estimating the residual DI value of the defective function point.
Further, the base risk value fo _ r ═ Y (T/TR);
wherein Y is the number of defects satisfying the defect exit condition;
t: the time required by the original manpower input of the project;
TR: project planning time.
Further, the correlation service evaluation module comprises a correlation relationship establishing unit and a correlation risk value calculating unit;
the incidence relation establishing unit is used for processing the preprocessed defect function points after traversing to establish the incidence relation and the corresponding incidence probability of each defect function point;
the correlated risk value calculating unit is used for calculating the correlated risk value of the defect function point; and the associated risk value of the defect function point is equal to the sum of the basic risk value of each defect function point which has the associated relation with the defect function point and the corresponding associated probability.
And further, the comprehensive risk calculation module is specifically used for adding the basic risk value and the associated risk value of the defect function point to obtain a risk value of the defect function point.
And analyzing the defect sample to obtain the minimum function point corresponding to the defect in a dictionary matching mode. And calculating the remaining defects of each service function point by using a defect analysis method, determining a basic risk value according to the defect export condition and the test time of project planning, analyzing the associated service and the associated probability according to the probability of the simultaneous occurrence of the problems, and constructing an associated service library and the associated probability by manual filtering. And adding the basic risk value and the associated risk value obtained by multiplying the associated service by the associated probability to obtain the risk score value of the function point, and selecting the test case according to the risk score.
According to the technical scheme, the invention has the following advantages: through risk analysis to historical defects, the risk score of the product defect function can be obtained, according to the risk distribution condition of the product function, the risk module can be effectively and intensively input with manpower, the basic service function is guaranteed by combining with an automatic case, the test hit rate can be effectively improved, the version test quality and efficiency can be improved, the inherent service function of the product can be effectively guaranteed, and support is provided for the regression verification range determination.
In addition, the invention has reliable design principle, simple structure and very wide application prospect.
Therefore, compared with the prior art, the invention has prominent substantive features and remarkable progress, and the beneficial effects of the implementation are also obvious.
Drawings
In order to more clearly illustrate the embodiments or technical solutions in the prior art of the present invention, the drawings used in the description of the embodiments or prior art will be briefly described below, and it is obvious for those skilled in the art that other drawings can be obtained based on these drawings without creative efforts.
FIG. 1 is a schematic flow diagram of a method of one embodiment of the invention.
Fig. 2 is a schematic block diagram of an apparatus of one embodiment of the present invention.
Detailed Description
In order to make those skilled in the art better understand the technical solution of the present invention, the technical solution in the embodiment of the present invention will be clearly and completely described below with reference to the drawings in the embodiment of the present invention, and it is obvious that the described embodiment is only a part of the embodiment of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, an embodiment of the present invention provides a method for precision test analysis based on defect analysis, including the following steps:
s1: performing defect pretreatment on the defect sample, and analyzing a defect function point;
s2: calculating the remaining defect number of the defect functional points by using a defect analysis method;
s3: determining a basic risk value according to the number of the left-over defects, the defect discovery rate meeting the functional export condition and the test time of the project plan;
s4: analyzing the incidence relation and incidence probability of the defect function points, and calculating the incidence risk value of the defect function points;
s5: calculating the risk value of the defect function point according to the basic risk value and the associated risk value of the defect function point;
s6: and selecting a test case according to the risk value of the defect function point.
In some embodiments, in step S1, the defect sample is subjected to defect pre-processing, and the step of analyzing the defect function point includes:
and acquiring defect sample data and constructing a dictionary. In the step, historical defect detail data are obtained, and dictionaries (mo1, mo2, …; ac1 and ac2 …) are constructed;
in some embodiments, in step S1, the defect sample is subjected to defect preprocessing, and the step of analyzing the defect function point includes:
s11: inducing a management object corresponding to the defective sample data and a management action supported by the management object;
s12: using a word segmentation algorithm based on a dictionary, matching a management object and a management action corresponding to each defect in a forward direction to the maximum extent to form a service function table corresponding to the defect; wherein the service function table includes a defect function point, a defect creation time, and a defect level. The service function table is shown in table 1;
TABLE 1
| Defective function point | Defect creation time | Defect level |
| fo1＝mo1+ac1 | 2020/4/15 | major |
| fo2＝mo2+ac2 | 2020/4/16 | normal |
| fo1＝mo1+ac1 | 2020/4/17 | suggestion |
| fo3＝mo1+ac3 | 2020/4/18 | normal |
| …… | …… | …… |
It should be noted that if the service is more complicated and more complex, the chinese word segmentation algorithm may be used for processing, but the result may be less than ideal. It is proposed to generalize the management objects mo of the software (e.g. cloud hosts, cloud hard disks, clusters, cloud plans), the management actions ac supported by the management objects (typically query, view, create, delete, modify, and also specifically mount, uninstall, etc.), and then to match the defect content. Because part of the test scenarios are the results of the process data verification, for example, the management object is created first, and then the modification and deletion operations can be performed on the management object; also, management object 1 is created, management object 2 can be created, etc., and thus, it is necessary to prioritize the management objects and operations. Operation on the management object in the defect description, if the management object is created or modified, the defect function point is classified as the modification of the management object; meanwhile, if the management objects 1 and 2 exist, the business operation of the management object 2 is generalized.
And then, using a word segmentation algorithm based on a dictionary to maximally match the mo and the ac corresponding to each defect in the forward direction to form a business function corresponding to the defect.
If the service functions supported by the project are more and the detailed function points are not required to be divided, the module functions can be directly divided according to the management objects; if the defects are divided into modules, the defect data of the modules can be directly selected for analysis according to module analysis and the requirements can be met according to the characteristic definition of the product per se.
Generating a defect statistical data table as shown in table 2 according to the service function table;
TABLE 2
| Defective function point | Time | Accumulated DI value |
| fo1 | 2020/4/15 | 3 |
| fo1 | 2020/4/16 | 5 |
| fo1 | 2020/4/17 | 9 |
| fo1 | 2020/4/18 | 11 |
| … | … | … |
| fo2 | 2020/4/15 | 5 |
| fo2 | 2020/4/16 | 9 |
| … | … | … |
| fon | 2020/4/15 | 8 |
| … | … | … |
In some embodiments, the step of calculating the number of remaining defects of the defect functional point using the defect analysis method in step S2 includes:
s21: generating defect statistical data according to the service function table, wherein the defect statistical data comprise defect function points and accumulated DI values corresponding to the defect function points;
s22: analyzing a trend curve of each defect functional point and a corresponding accumulated DI value curve by using a Gompertz defect analysis method;
s23: and estimating the residual DI value of the defect function point, wherein the DI value is the weighted value of the defect number.
Using Gompertz defect analysis, each defect function point trend curve is analyzed, and then using a model of Y ═ a ^ b ^ (c ^ T) to calculate possible remaining defects. Where Y represents the number of software defects found over time T, and a is the total number of software defects that can be found when T → ∞ i, i.e., the total number of defects contained in the software. a b is the number of software defects found when T → 0, and c represents the growth rate of the found defects. And (3) estimating the defect discovery rate d required to be achieved by the test outlets of the a, b and c according to the project by adopting a nonlinear regression least square method, namely, when Y is a.
Other curve data trend prediction analysis methods can be introduced to predict the residual DI value, and the residual DI value can be properly selected according to the structural characteristics of the product.
In some embodiments, in step S3, in the step of determining the basic risk value according to the number of defects satisfying the function exit condition and the test time of the project plan, the basic risk value fo _ r is Y (T/TR);
wherein Y is the number of defects satisfying the defect exit condition;
t: the time required by the original manpower input of the project;
TR: project planning time.
In combination with the time TR for project group planning, based on the T value selected in the above steps, we define the basic risk value of a certain function fo as fo _ r ═ Y (T/TR), that is, the number of defects satisfying the function export condition is multiplied by the ratio of the time required by the original human input of the project to the project planning time.
In step S3, the function export condition is set to 90%, that is, it can be defined that 90% of defects can analyze that the function corresponding to the defect is that the object library and the action library are complete.
In some embodiments, in step S4, the step of analyzing the association relationship and the association probability and calculating the associated risk value of the defect function point includes:
s41: traversing the preprocessed defect function points and then processing to establish an incidence relation and a corresponding incidence probability of each defect function point;
s42: calculating the associated risk value of the defect function point; and the associated risk value of the defect function point is equal to the sum of the basic risk value of each defect function point which has the associated relation with the defect function point and the corresponding associated probability.
The preprocessed defects can be subjected to traversal post-processing to construct the association risk, the number of days when the defects appear in fo1 is n, the number of days when the fo2 appears in the days is m, p (1-2) ═ m/n, when p is greater than 80%, the association relationship is left to be stored in a warehouse, and the association relationship is abandoned when the p is smaller than the m, at the moment, the association relationship is one-way, namely, the probability that the fo2 is the problem of the fo1, and the probability that the fo1 is the problem of the fo2 are calculated in the same way and cannot be reused. In this way, all the association relations are repeatedly acquired through traversal.
The method is a relatively simple association algorithm, and other root cause analysis methods can be introduced to obtain the association, so that the obtained association is a mechanical one, the association needs to be manually checked, analyzed and confirmed due to the complexity of the business, and really related business is left, and the association analyzed and summarized in the test process can be manually increased so as to avoid omission.
The associated risk value of the business is the risk value of other functional points with association relation multiplied by the association probability p, for example, the associated risk value of fo 2:
fo2_relation＝fo1_r*p(1-2)+fo3_r*p(3-2)+fo4_r*p(3-2)+……
in some embodiments, in step S5, the step of calculating the risk value of the defect function point according to the basic risk value and the associated risk value of the defect function point includes:
and adding the basic risk value and the associated risk value of the defect function point to obtain the risk value of the defect function point.
According to the sequence of the risk values, proper risk cases can be selected for verification by combining the labor input of the project and the case scale, the first round of system test mainly guides to select the baseline cases, and the subsequent rounds can also be used for selecting regression cases with newly increased requirements. If the use case has the function point mark, the automatic selection of the test use case can be realized.
As shown in fig. 2, an embodiment of the present invention further provides an accurate test analysis device based on defect analysis, which includes a defect preprocessing module, a left defect risk value estimation module, a basic risk value determination module, an associated service evaluation module, a comprehensive risk calculation module, and a test case selection module;
the defect preprocessing module is used for preprocessing the defects of the defect sample and analyzing the defect function points;
the residual defect risk value estimation module is used for calculating the residual defect number of the defect functional points by using a defect analysis method;
the basic risk value determining module is used for determining a basic risk value according to the defect number meeting the functional export condition and the test time of the project plan;
base risk value fo _ r ═ Y ═ T/TR;
wherein Y is the number of defects satisfying the defect exit condition;
t: the time required by the original manpower input of the project;
TR: project planning time.
The correlation service evaluation module is used for analyzing the correlation relation and the correlation probability of the defect function points and calculating the correlation risk value of the defect function points;
the comprehensive risk calculation module is used for calculating the risk value of the defect function point according to the basic risk value and the associated risk value of the defect function point;
and the test case selection module is used for selecting the test cases according to the risk values of the defect function points.
In some embodiments, the apparatus further comprises a pre-processing module;
and the preprocessing module is used for acquiring defect sample data and constructing a dictionary.
In some embodiments, the defect pre-processing module comprises a generalization unit and a matching unit;
the induction unit is used for inducing the management object corresponding to the defective sample data and the management action supported by the management object;
the matching unit is used for matching the management object and the management action corresponding to each defect to the maximum extent in the forward direction by using a word segmentation algorithm based on a dictionary to form a service function table corresponding to the defect; wherein the service function table includes a defect function point, a defect creation time, and a defect level.
In some embodiments, the left-behind defect risk value estimation module includes a defect statistic generation unit, an analysis unit, and an estimation unit;
the defect statistical data generating unit is used for generating defect statistical data according to the service function table, wherein the defect statistical data comprise defect function points and accumulated DI values corresponding to the defect function points;
the analysis unit is used for analyzing a trend curve of each defect function point and a corresponding accumulated DI value curve by using a Gompertz defect analysis method;
and the estimation unit is used for estimating the residual DI value of the defective function point.
In some embodiments, the association business evaluation module includes an association relationship establishing unit and an association risk value calculating unit;
the incidence relation establishing unit is used for processing the preprocessed defect function points after traversing to establish the incidence relation and the corresponding incidence probability of each defect function point;
the correlated risk value calculating unit is used for calculating the correlated risk value of the defect function point; and the associated risk value of the defect function point is equal to the sum of the basic risk value of each defect function point which has the associated relation with the defect function point and the corresponding associated probability.
In some embodiments, the comprehensive calculation risk module is specifically configured to add the basic risk value and the associated risk value of the defect function point to obtain a risk value of the defect function point.
And analyzing the defect sample to obtain the minimum function point corresponding to the defect in a dictionary matching mode. And calculating the remaining defects of each service function point by using a defect analysis method, determining a basic risk value according to the defect export condition and the test time of project planning, analyzing the associated service and the associated probability according to the probability of the simultaneous occurrence of the problems, and constructing an associated service library and the associated probability by manual filtering. And adding the basic risk value and the associated risk value obtained by multiplying the associated service by the associated probability to obtain the risk score value of the function point, and selecting the test case according to the risk score.
Although the present invention has been described in detail by referring to the drawings in connection with the preferred embodiments, the present invention is not limited thereto. Various equivalent modifications or substitutions can be made on the embodiments of the present invention by those skilled in the art without departing from the spirit and scope of the present invention, and these modifications or substitutions are within the scope of the present invention/any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
Claims (10)
1. A precise test analysis method based on defect analysis is characterized by comprising the following steps:
performing defect pretreatment on the defect sample, and analyzing a defect function point;
calculating the remaining defect number of the defect functional points by using a defect analysis method;
determining a basic risk value according to the number of the left-over defects, the defect discovery rate meeting the functional export condition and the test time of the project plan;
analyzing the incidence relation and incidence probability of the defect function points, and calculating the incidence risk value of the defect function points;
calculating the risk value of the defect function point according to the basic risk value and the associated risk value of the defect function point;
and selecting a test case according to the risk value of the defect function point.
2. The method as claimed in claim 1, wherein the step of preprocessing the defect sample to obtain the functional point of the defect comprises:
and acquiring defect sample data and constructing a dictionary.
3. The method as claimed in claim 2, wherein the step of preprocessing the defect sample to analyze the functional point of the defect comprises:
inducing a management object corresponding to the defective sample data and a management action supported by the management object;
using a word segmentation algorithm based on a dictionary, matching a management object and a management action corresponding to each defect in a forward direction to the maximum extent to form a service function table corresponding to the defect; wherein the service function table includes a defect function point, a defect creation time, and a defect level.
4. The method of claim 3, wherein the step of calculating the number of remaining defects of the functional point of defect using the defect analysis method comprises:
generating defect statistical data according to the service function table, wherein the defect statistical data comprise defect function points and accumulated DI values corresponding to the defect function points;
analyzing a trend curve of each defect functional point and a corresponding accumulated DI value curve by using a Gompertz defect analysis method;
and estimating the residual DI value of the defect function point, wherein the DI value is the weighted value of the defect number.
5. The method of claim 4, wherein in the step of determining the basic risk value, the basic risk value fo _ r is Y (T/TR) according to the legacy DI value, the defect discovery rate satisfying the functional export condition, and the test time of the project plan;
wherein Y is the number of defects satisfying the defect exit condition;
t: the time required by the original manpower input of the project;
TR: project planning time.
6. The method as claimed in claim 5, wherein the step of analyzing the correlation relationship and the correlation probability and calculating the correlation risk value of the defect function point comprises:
traversing the preprocessed defect function points and then processing to establish an incidence relation and a corresponding incidence probability of each defect function point;
calculating the associated risk value of the defect function point; and the associated risk value of the defect function point is equal to the sum of the basic risk value of each defect function point which has the associated relation with the defect function point and the corresponding associated probability.
7. The method as claimed in claim 6, wherein the step of calculating the risk value of the defect function point according to the basic risk value and the associated risk value of the defect function point comprises:
and adding the basic risk value and the associated risk value of the defect function point to obtain the risk value of the defect function point.
8. An accurate test analysis device based on defect analysis is characterized by comprising a defect preprocessing module, a left-behind defect risk value estimation module, a basic risk value determination module, an associated service evaluation module, a comprehensive calculation risk module and a test case selection module;
the defect preprocessing module is used for preprocessing the defects of the defect sample and analyzing the defect function points;
the residual defect risk value estimation module is used for calculating the residual defect number of the defect functional points by using a defect analysis method;
the basic risk value determining module is used for determining a basic risk value according to the number of the left-over defects, the defect discovery rate meeting the function export condition and the test time of the project plan;
the correlation service evaluation module is used for analyzing the correlation relation and the correlation probability of the defect function points and calculating the correlation risk value of the defect function points;
the comprehensive risk calculation module is used for calculating the risk value of the defect function point according to the basic risk value and the associated risk value of the defect function point;
and the test case selection module is used for selecting the test cases according to the risk values of the defect function points.
9. The apparatus for precision test analysis based on defect analysis of claim 8, further comprising a preprocessing module;
and the preprocessing module is used for acquiring defect sample data and constructing a dictionary.
10. The apparatus for precision test analysis based on defect analysis of claim 9, wherein the defect preprocessing module comprises a generalization unit and a matching unit;
the induction unit is used for inducing the management object corresponding to the defective sample data and the management action supported by the management object;
the matching unit is used for matching the management object and the management action corresponding to each defect to the maximum extent in the forward direction by using a word segmentation algorithm based on a dictionary to form a service function table corresponding to the defect; wherein the service function table includes a defect function point, a defect creation time, and a defect level.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011174762.5A CN112463584B (en) | 2020-10-28 | 2020-10-28 | Accurate test analysis method and device based on defect analysis |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011174762.5A CN112463584B (en) | 2020-10-28 | 2020-10-28 | Accurate test analysis method and device based on defect analysis |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112463584A true CN112463584A (en) | 2021-03-09 |
| CN112463584B CN112463584B (en) | 2022-07-08 |
Family
ID=74835681
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011174762.5A Active CN112463584B (en) | 2020-10-28 | 2020-10-28 | Accurate test analysis method and device based on defect analysis |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112463584B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113342651A (en) * | 2021-06-01 | 2021-09-03 | 南京大学 | Recovery method for testing case defect and case fuzzy association relation |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110633222A (en) * | 2019-11-01 | 2019-12-31 | 中国银行股份有限公司 | Method and device for determining regression test case |
| CN111625454A (en) * | 2020-05-22 | 2020-09-04 | 平安普惠企业管理有限公司 | Data processing method based on test case and related equipment |
-
2020
- 2020-10-28 CN CN202011174762.5A patent/CN112463584B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110633222A (en) * | 2019-11-01 | 2019-12-31 | 中国银行股份有限公司 | Method and device for determining regression test case |
| CN111625454A (en) * | 2020-05-22 | 2020-09-04 | 平安普惠企业管理有限公司 | Data processing method based on test case and related equipment |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113342651A (en) * | 2021-06-01 | 2021-09-03 | 南京大学 | Recovery method for testing case defect and case fuzzy association relation |
| CN113342651B (en) * | 2021-06-01 | 2023-11-03 | 南京大学 | Recovery method for testing fuzzy association relation between case defects and cases |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112463584B (en) | 2022-07-08 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109587713B (en) | Network index prediction method and device based on ARIMA model and storage medium | |
| US7865389B2 (en) | Analyzing time series data that exhibits seasonal effects | |
| CN110730099B (en) | Flow prediction method based on historical flow data time sequence | |
| US8954362B2 (en) | System and method for predictive analytics in an electrical grid network | |
| CN106803799B (en) | Performance test method and device | |
| CN110263827A (en) | Abnormal transaction detection method and device based on transaction rule identification | |
| CN102522709B (en) | Decision-making method and decision-making system for state overhaul of transformers | |
| CN109800995A (en) | A kind of grid equipment fault recognition method and system | |
| CN108830417B (en) | ARMA (autoregressive moving average) and regression analysis based life energy consumption prediction method and system | |
| CN111060652A (en) | Method for predicting concentration of dissolved gas in transformer oil based on long-term and short-term memory network | |
| CN109254912A (en) | A kind of method and device of automatic test | |
| CN114048436A (en) | Construction method and construction device for forecasting enterprise financial data model | |
| CN111651444B (en) | Self-adaptive time series data prediction method | |
| CN112463584B (en) | Accurate test analysis method and device based on defect analysis | |
| CN113641959A (en) | High-voltage cable joint temperature trend prediction method | |
| CN112486137A (en) | Method and system for constructing fault feature library of active power distribution network and fault diagnosis method | |
| CN114490404A (en) | Test case determination method and device, electronic equipment and storage medium | |
| CN111738504A (en) | Enterprise financial index fund amount prediction method and device, equipment and storage medium | |
| CN113835947B (en) | Method and system for determining abnormality cause based on abnormality recognition result | |
| CN110910241B (en) | Cash flow evaluation method, apparatus, server device and storage medium | |
| CN116737681A (en) | Real-time abnormal log detection method and device, computer equipment and storage medium | |
| CN115328866A (en) | Log sampling based method and system for predicting event under process instance | |
| CN110096335A (en) | One kind being directed to the different types of service concurrence amount prediction technique of virtual machine | |
| CN116561569A (en) | Industrial power load identification method based on EO feature selection and AdaBoost algorithm | |
| CN114765574B (en) | Network anomaly delimitation positioning method and device |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |