CN117349151A - Test case priority ordering method and device based on clustering and storage medium - Google Patents
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Abstract
The invention discloses a test case priority ordering method, a device and a storage medium based on clustering, which are applied to regression testing of software, wherein the method comprises the following steps: (1) Clustering analysis is carried out on the test cases, and the test cases with similarity are grouped to obtain a plurality of unit clusters; (2) And taking each unit cluster as a unit, determining the unit priority through the test cases of the execution unit clusters, and determining the case priority of the test cases according to the unit priority. According to the method for sorting the priorities of the test cases based on the dynamic sorting of the test execution results, the machine learning algorithm is adopted to conduct clustering analysis on the cases, and the test case priorities are dynamically adjusted according to the test execution results, so that the test cases which are more likely to detect defects are preferentially executed, the defect detection speed is improved, the purpose of improving the regression test benefit is achieved, and the test efficiency is greatly improved.
Description
Technical Field
The invention belongs to the field of software testing, and particularly relates to a test case priority ordering method and device based on clustering and a storage medium.
Background
Regression testing is the process of software development or after old code is modified after release, and tests are re-conducted to confirm that the modification did not introduce new errors or cause other code to produce errors. The automatic regression test greatly reduces the cost of the system testing, maintenance and upgrading stages and the like.
Regression testing is used as a component of the software lifecycle, and takes up a great amount of work in the whole software testing process, and multiple regression tests are performed at each stage of software development. In progressive and rapid iterative development, successive releases of new versions make regression testing more frequent, whereas in the extreme programming approach, regression testing is more required to be performed several times per day. Therefore, it is significant to improve the efficiency and effectiveness of regression testing by selecting the correct regression testing strategy. The Chinese patent document with publication No. CN101178687A discloses a software regression testing method.
In the software development process, as the function pairs of the product are continuously increased and iterated, the scale of the test case set is continuously increased, so that the regression test cost is also continuously increased. Because the test resources are limited, the test cases cannot be executed completely, and in order to improve the regression test efficiency, a regression test strategy which better meets the regression test requirements needs to be formulated, namely, the test cases are ordered according to a certain set test target so as to determine the execution sequence of the test cases, so that the optimal test cases can be executed preferentially.
The Chinese patent document with publication number of CN113778855A discloses a self-adaptive test case ordering method based on greedy algorithm and cluster analysis, which comprises the following steps: step 1, reading an original test case, calculating a Manhattan distance matrix of the original test case, and dividing the test case set into K clusters by using an improved K center point clustering method for the original test case according to the distance matrix; step 2, based on the distance matrix, sequencing each cluster clustered by the K central points by using a greedy algorithm; and 3, after each cluster is arranged in sequence, selecting test cases from each cluster in sequence, and merging the test cases into a test case set for testing.
The existing test case priority ordering method adopts a clustering algorithm, but does not consider the influence of different types of clusters and feature subsets on a clustering result, has the problem that the ordering information is not comprehensive or the ordering target is single, can not comprehensively use various information to order the test cases, greatly reduces the regression testing speed and reduces the testing efficiency.
Disclosure of Invention
The invention provides a test case priority ordering method, a device and a storage medium based on clustering, wherein the test case priority ordering method based on the dynamic ordering of test execution results adopts a machine learning algorithm to conduct clustering analysis on the test cases, and the test case priority ordering is dynamically adjusted through the test execution results, so that the test cases more likely to detect defects are preferentially executed, the defect detection speed is improved, the purpose of improving regression test benefits is achieved, and the test efficiency is greatly improved.
A test case prioritization method based on clustering is applied to regression testing of software, and comprises the following steps:
(1) Clustering analysis is carried out on the test cases, and the test cases with similarity are grouped to obtain a plurality of unit clusters;
(2) And taking each unit cluster as a unit, determining the unit priority through the test cases of the execution unit clusters, and determining the case priority of the test cases according to the unit priority.
Further, the specific process of the step (2) is as follows:
(2-1) assigning the cell priority of each cell cluster to the same initial value; further, the initial value is 1;
(2-2) obtaining an execution result through the test cases of the execution unit clusters; and reassigning the unit priority according to the execution result;
(2-3) determining the case priority of the test case according to the unit priority.
The specific process of the step (2-2) is as follows:
(2-2-1) extracting test cases with representative features in each unit cluster as representative cases;
(2-2-2) grouping all the extracted representative use cases together into a single template cluster;
(2-2-3) executing representative cases in the template cluster, obtaining an execution result, and updating the unit priority according to the execution result.
The specific process of the step (2-2-1) is as follows:
(2-2-1-1) prioritizing the test cases of the unit clusters based on the historical execution frequency and associated importance corresponding to each test case;
(2-2-1-2) after the prioritization, taking the test case having the first priority therein as the test case having the representative feature as the representative case.
The specific process of the step (2-2-3) is as follows:
(2-2-3-1) executing each representative case in the template cluster to obtain an execution result corresponding to each representative case;
(2-2-3-2) deriving a priority update value corresponding to the representative use case from the execution result; wherein, the result parameters in the execution result include: code coverage, functional priority, defect generation number, and defect severity level;
(2-2-3-3) updating the unit priority of the unit cluster corresponding to the representative use case by the priority updating value;
the priority updating value is a numerical value corresponding to the initial value, and replaces the initial value set by the original unit cluster during initialization through the priority updating value.
The specific process of the step (2-3) is as follows:
(2-3-1) determining a prioritization order of the unit clusters corresponding to the unit priorities by the unit priorities according to the prioritization engine after updating the unit priorities with the priority update values;
(2-3-2) determining the case priority of each test case based on the prioritization order and according to the historical execution frequency and the associated importance corresponding to each test case in the unit cluster.
A cluster-based test case prioritization apparatus, comprising:
the clustering module is used for carrying out clustering analysis aiming at the test cases to obtain a unit cluster after the clustering analysis; each unit cluster comprises the test cases with similarity after clustering;
and the execution module is used for determining the unit priority by taking each unit cluster as a unit through executing the test cases of the unit clusters, and determining the case priority of the test cases according to the unit priority.
A test case prioritization device based on clustering comprises a memory and a processor, wherein the memory stores executable codes, and the processor is used for realizing the test case prioritization method when the executable codes are shaped.
A computer readable storage medium having stored thereon a program for test case prioritization, which when executed by a processor, implements the above-described test case prioritization method.
Compared with the prior art, the invention has the following beneficial effects:
1. the invention improves the sequencing effect of the test cases, has better software error detection capability, and the priority of the test cases is dynamically adjusted by the set priority sequencing engine and the sequencing engine according to the test result.
2. The invention can integrate the code coverage rate in the test process into the function layer according to the execution result obtained by executing the test, namely, the comprehensive sequencing is carried out by combining the priorities of the functions.
3. The invention can detect software errors covered by fewer test cases earlier.
4. Under the condition of limited time resources, the invention tests the test cases with great test contribution to priority execution of the test cases, thereby improving the detection rate of software errors and reducing the cost of regression test.
Drawings
FIG. 1 is a flow chart of a test case prioritization method based on clustering;
FIG. 2 is a flowchart of step S200 in an embodiment of the invention;
FIG. 3 is a flowchart of step S220 in an embodiment of the present invention;
FIG. 4 is a flowchart of step S221 in an embodiment of the invention
FIG. 5 is a flowchart of step S223 in an embodiment of the present invention
FIG. 6 is a flowchart of step S230 in an embodiment of the invention
FIG. 7 is a schematic diagram of a cluster-based test case prioritization apparatus.
Detailed Description
The invention will be described in further detail with reference to the drawings and examples, it being noted that the examples described below are intended to facilitate the understanding of the invention and are not intended to limit the invention in any way.
As shown in fig. 1, a test case prioritization method based on clustering includes:
step S100, carrying out cluster analysis aiming at the test cases to obtain a unit cluster after cluster analysis; and each unit cluster comprises the test cases with similarity after clustering.
It should be noted that cluster analysis, i.e., cluster analysis, refers to an analysis process of grouping a collection of physical or abstract objects into a plurality of classes composed of similar objects, and is a data processing behavior.
The goal of cluster analysis is to collect data on a similar basis to classify. Clustering is derived from many fields including mathematics, computer science, statistics, biology and economics. In different fields of application, a number of clustering techniques have been developed, which are used to describe data, measure similarities between different data sources, and classify the data sources into different clusters.
For example, in test cases, the types can be divided into two types:
1) Test cases designed to test existing functionality:
the first test case (testing existing functions) because it is already in use in other test cycles and contains test execution results (e.g., test passing rate, test defects, etc.), which can use the previous execution data to prioritize this calculation;
2) Test cases designed for testing newly added functions:
the second test case (test newly added function) has no previous test execution result, and is designed according to a test strategy to achieve a preset target.
In this embodiment, a test strategy is provided for producing test cases in a pre-test stage, and these test cases are executed to find defects related to new functions.
After clustering is performed by the clustering analysis algorithm, the test cases with similarity can be grouped.
The clustering analysis, using machine learning, groups test cases according to their diversity to generate a similarity-based test case cluster, and the clustering analysis step may include two actions:
(1) Determining which test cases are similar through a machine learning clustering technology;
(2) In the process of clustering the test cases by using a clustering algorithm, the internal test cases can be ranked by using a conventional test case priority ranking algorithm.
And finally obtaining a plurality of unit clusters through a clustering algorithm, wherein the unit clusters comprise the test cases with similarity after clustering.
And step 200, determining the unit priority by taking each unit cluster as a unit through executing the test cases of the unit clusters, and determining the case priority of the test cases according to the unit priority.
The priority of the unit cluster is determined by aiming at the test cases in the unit cluster, namely, the unit priority of the unit cluster is determined first, and then the test case which is executed most preferentially, namely, the case priority, is determined according to the unit priority, so that the determination of the execution sequence of the test cases can be realized.
According to the method for sorting the priorities of the test cases based on the dynamic sorting of the test execution results, the machine learning algorithm is adopted to conduct clustering analysis on the cases, and the test case priorities are dynamically adjusted according to the test execution results, so that the test cases which are more likely to detect defects are preferentially executed, the defect detection speed is improved, the purpose of improving the regression test benefit is achieved, and the test efficiency is greatly improved.
As shown in fig. 2, step S200 specifically includes:
in step S210, the cell priority of each cell cluster is assigned to the same initial value.
In the embodiment, a ranking engine is constructed, and the ranking engine dynamically adjusts the ranking of all the unit clusters.
And the unit priority is correspondingly arranged in each unit cluster and represents the priority level of the unit cluster in all the unit clusters.
Specifically, when sorting, sorting is required for each constructed unit cluster, in this embodiment, for further sorting dynamic adjustment, an initialization operation is performed on each unit cluster through the sorting engine, that is, a value is assigned to the unit priority of each unit cluster, where the value assignment requirement is that all current units have priority assigned to the same value, that is, an initial value.
Here, the initial value may be 1. That is, all the unit clusters have a corresponding unit priority of 1.
Step S220, obtaining an execution result through the test cases of the execution unit clusters; and reassigning the unit priority according to the execution result.
Step S230, determining the case priority of the test case according to the unit priority.
The unit priority is updated and assigned again by executing the test cases in the unit clusters to realize the dynamic adjustment on the value of the unit priority, so that the unit clusters with similarity can be ordered, and the case priority of the test cases can be determined.
In a word, the priority order of the test cases is dynamically adjusted according to the test execution results, so that the test cases more likely to detect the defects are preferentially executed, the defect detection speed is improved, the purpose of improving the regression test benefit is achieved, and the test efficiency is greatly improved.
As shown in fig. 3, step S220 specifically includes:
in step S221, a test case having a representative feature in each unit cluster is extracted as a representative case.
In this embodiment, in order to improve the efficiency of the sorting analysis of test cases, according to the similarity of test cases in the unit clusters, a representative test case is extracted from each test case for the sorting analysis.
Specifically, the representative feature is extracted as a representative feature point, and the extracted test case is the representative case, which is still the test case in the original unit cluster, and at this time, the extracted test case is further analyzed as the representative case.
In step S222, all the extracted representative cases are combined into a single template cluster.
In each unit cluster, a corresponding representative use case is extracted through the representative feature sequencing, and the representative use cases are further recombined to form a new independent template cluster.
Step S223, executing the representative use cases in the template cluster, obtaining the execution result, and updating the unit priority according to the execution result.
The template cluster includes a plurality of representative cases extracted from the original unit clusters. In order to determine the priority levels for the representative cases, by executing the representative cases, a corresponding execution result can be obtained for each representative case, the priority level of each representative case is determined according to the execution results, the priority levels both correspond to the execution condition of each representative case and correspond to the unit cluster where the representative case is located according to the corresponding property, therefore, the limited level of the original unit priority level can be determined according to the execution results of the representative cases, the unit priority level is evaluated, and the evaluation result is updated in the unit priority levels.
As shown in fig. 4, step S221 specifically includes:
step S2211, the test cases of the unit cluster are prioritized based on the historical execution frequency and the associated importance corresponding to each test case;
the representative feature of each test case is the data of the historical execution frequency and the data of the relevant importance.
The historical execution frequency is the execution frequency of the test case in different test environments or test processes, and the execution frequency can primarily represent the importance of the test case in the test; the correlation importance is the correlation with the related program in the running process, and the correlation data represents the correlation of the test cases, so that the representative characteristics of each test case can be quantified through the acquisition of two parameters of the correlation and the execution frequency.
In the above, by determining the historical execution frequency and the associated importance in the unit cluster, the test cases in the unit cluster can be prioritized in the unit cluster, so that the test case with the highest priority can be found.
In step S2212, after the priority ranking, the test case having the first priority is used as the test case having the representative feature, and the test case is used as the representative case.
After the priority ranking, the test case with the first priority is selected, and the test case with the highest priority is determined to have the representative characteristic, namely the test case with the highest priority is the representative case.
The method has the advantages that through the historical execution frequency and the associated importance in the representative features, the test cases in each unit cluster are internally sequenced, so that the representative case with the highest priority in each unit cluster is screened and extracted, further analysis is carried out, repeated execution of a large number of test cases with similarity or repeated analysis can be avoided, the test cases with similarity are screened to have more limited execution level, the test cases with more important relevance to software are more representative, further analysis is carried out after extraction, analysis efficiency can be pertinently improved, accuracy of an analysis strategy is improved, complexity of strategy formulation is greatly reduced, occupation on analysis time is greatly reduced, and analysis efficiency is improved.
As shown in fig. 5, step S223 specifically includes:
step S2231, executing each representative case in the template cluster to obtain an execution result corresponding to each representative case.
Step S2232, a priority update value corresponding to the representative use case is obtained according to the execution result.
Further, the result parameters in the execution result include: code coverage, functional priority, defect generation number, and defect severity level.
The execution result includes code coverage, function priority, defect generation number and defect severity level. The code coverage rate is the importance of the coverage function of the result, the defect generation quantity is the quantity of BUG and defects generated after execution, the defect severity level can be a preset level standard, and the defect severity level is obtained by automatic assignment after the defects are generated after execution.
In step S2233, the unit priorities of the unit clusters corresponding to the representative use cases are updated by the priority update values.
And the priority updating value is a numerical value corresponding to the initial value, and replaces the initial value set by the original unit cluster during initialization through the priority updating value.
When initializing, each unit cluster is set to be the same initial value, for example, 1, after executing the extracted representative cases and obtaining the execution result, a priority update value is obtained through the execution result, each representative case is provided with a priority update value corresponding to the representative case, then the original initial value is correspondingly covered or updated according to the priority update value, so that the unit priority of each original unit cluster is changed and is not completely the same, and thus the unit clusters have different priorities, at the moment, the difference in priority is the importance or the priority of other test cases with similarity in the unit cluster, which can be represented after the representative case is executed to obtain the execution result, so that the priority update value obtained by the execution result replaces the original initial value, the characteristic is endowed to the original unit cluster, and all the test cases in the unit cluster have the new unit priority.
For the execution result of the representative use case, four criteria are adopted for evaluation (for the judgment criteria, namely, evaluation criteria, the evaluation criteria can also be adjusted according to actual conditions): 1. code coverage rate, 2, function priority, 3, defect generation quantity, 4, defect severity level; and analyzing the use case result by adopting a comprehensive evaluation algorithm. Each index is provided with a corresponding weight and a weight value, and the final priority updating value of the representative case is obtained by calculating the sum of the weight and the weight value of the index, so that the unit priority of the unit cluster with the representative case is dynamically adjusted, the priority sorting of the test case is dynamically adjusted according to the test execution result, the test case which is more likely to detect the defect is preferentially executed, the defect detection speed is improved, the purpose of improving the regression test benefit is achieved, and the test efficiency is greatly improved.
As shown in fig. 6, step S230 specifically includes:
in step S231, after updating the unit priorities with the priority update values, the unit priorities are used to determine the priority ranking order of the unit clusters corresponding to the unit priorities according to the priority ranking engine.
Step S232, based on the priority order, and according to the historical execution frequency and the associated importance corresponding to each test case in the unit cluster, determining the case priority of each test case.
In the above, according to the prioritization engine, after dynamically adjusting the unit priority of each unit cluster, the prioritization order according to the unit priorities in all the unit clusters may be further obtained, and the unit cluster having the highest priority among them may be determined.
Further, by prioritizing the order, each test case in each unit cluster can be directly obtained according to the historical execution frequency and the associated importance of the unit clusters that were previously ranked, the unit clusters with the unit priorities that are most preferably executed, and the test cases that are most preferably executed in the unit clusters can be determined by ranking in the overall test case.
As shown in fig. 7, this embodiment further provides a test case prioritization device based on clustering, including:
the clustering module 10 is used for carrying out cluster analysis aiming at the test cases to obtain a unit cluster after cluster analysis; each unit cluster comprises test cases with similarity after clustering.
The execution module 20 is configured to determine, by using each unit of the unit clusters as a unit, a unit priority through executing the test cases of the unit clusters, and determine the case priority of the test cases according to the unit priority.
In addition, the embodiment also provides another cluster-based test case prioritization device, which comprises a memory and a processor, wherein the memory stores a test case prioritization program, and the processor runs the test case prioritization program to enable a test case prioritization system to execute the test case prioritization method.
In addition, the embodiment also provides a computer readable storage medium, and the computer readable storage medium stores a program for sequencing the test case priorities, and the program for sequencing the test case priorities realizes the method for sequencing the test case priorities when being executed by a processor.
The foregoing embodiments have described in detail the technical solution and the advantages of the present invention, it should be understood that the foregoing embodiments are merely illustrative of the present invention and are not intended to limit the invention, and any modifications, additions and equivalents made within the scope of the principles of the present invention should be included in the scope of the invention.
Claims (10)
1. A test case prioritization method based on clustering is applied to regression testing of software, and is characterized by comprising the following steps:
(1) Clustering analysis is carried out on the test cases, and the test cases with similarity are grouped to obtain a plurality of unit clusters;
(2) And taking each unit cluster as a unit, determining the unit priority through the test cases of the execution unit clusters, and determining the case priority of the test cases according to the unit priority.
2. The method for prioritizing test cases based on clusters according to claim 1, wherein the specific process of step (2) is as follows:
(2-1) assigning the cell priority of each cell cluster to the same initial value;
(2-2) obtaining an execution result through the test cases of the execution unit clusters; and reassigning the unit priority according to the execution result;
(2-3) determining the case priority of the test case according to the unit priority.
3. The method of claim 2, wherein in step (2-1), the initial value is 1.
4. The method for prioritizing test cases based on clusters according to claim 2, wherein the specific procedure of step (2-2) is as follows:
(2-2-1) extracting test cases with representative features in each unit cluster as representative cases;
(2-2-2) grouping all the extracted representative use cases together into a single template cluster;
(2-2-3) executing representative cases in the template cluster, obtaining an execution result, and updating the unit priority according to the execution result.
5. The method of claim 4, wherein the specific process of step (2-2-1) is as follows:
(2-2-1-1) prioritizing the test cases of the unit clusters based on the historical execution frequency and associated importance corresponding to each test case;
(2-2-1-2) after the prioritization, taking the test case having the first priority therein as the test case having the representative feature as the representative case.
6. The method of claim 4, wherein the specific process of step (2-2-3) is as follows:
(2-2-3-1) executing each representative case in the template cluster to obtain an execution result corresponding to each representative case;
(2-2-3-2) deriving a priority update value corresponding to the representative use case from the execution result; wherein, the result parameters in the execution result include: code coverage, functional priority, defect generation number, and defect severity level;
(2-2-3-3) updating the unit priority of the unit cluster corresponding to the representative use case by the priority updating value;
the priority updating value is a numerical value corresponding to the initial value, and replaces the initial value set by the original unit cluster during initialization through the priority updating value.
7. The method of claim 2, wherein the specific process of step (2-3) is as follows:
(2-3-1) determining a prioritization order of the unit clusters corresponding to the unit priorities by the unit priorities according to the prioritization engine after updating the unit priorities with the priority update values;
(2-3-2) determining the case priority of each test case based on the prioritization order and according to the historical execution frequency and the associated importance corresponding to each test case in the unit cluster.
8. A cluster-based test case prioritization apparatus, comprising:
the clustering module is used for carrying out clustering analysis aiming at the test cases to obtain a unit cluster after the clustering analysis; each unit cluster comprises the test cases with similarity after clustering;
and the execution module is used for determining the unit priority by taking each unit cluster as a unit through executing the test cases of the unit clusters, and determining the case priority of the test cases according to the unit priority.
9. A cluster-based test case prioritization device, comprising a memory and a processor, wherein the memory stores executable code, and the processor is configured to implement the test case prioritization method of any one of claims 1-7 when the executable code is typed.
10. A computer-readable storage medium, having stored thereon a program for test case prioritization, which when executed by a processor, implements the test case prioritization method of any of claims 1-7.
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| CN117590063A (en) * | 2024-01-18 | 2024-02-23 | 荣耀终端有限公司 | Power consumption test circuit, power consumption test method, electronic device and storage medium |
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| CN117590063A (en) * | 2024-01-18 | 2024-02-23 | 荣耀终端有限公司 | Power consumption test circuit, power consumption test method, electronic device and storage medium |
| CN117590063B (en) * | 2024-01-18 | 2024-06-04 | 荣耀终端有限公司 | Power consumption test circuit, power consumption test method, electronic device and storage medium |
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