CN113626328A - Test case similarity checking method and device - Google Patents
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Abstract
The invention discloses a method and a device for checking similarity of test cases, which relate to the technical field of big data, wherein the method comprises the following steps: dividing each test case in the plurality of test cases into a plurality of characteristic items; for each test case, calculating the weight of each characteristic item in the test case; establishing a feature vector space for each test case, wherein each feature vector in the feature vector space is the weight of each feature item in the test case; calculating cosine similarity of feature vector spaces of any two test cases in the plurality of test cases; and determining whether the two corresponding test cases are similar according to the calculated cosine similarity, so that the similarity of the test cases can be checked, the test efficiency is improved, and the user experience is improved.
Description
Technical Field
The invention relates to the technical field of big data, in particular to a method and a device for checking similarity of test cases.
Background
This section is intended to provide a background or context to the embodiments of the invention that are recited in the claims. The description herein is not admitted to be prior art by inclusion in this section.
At present, when test cases are written, the test cases are generally written manually according to experience, so that part of the test cases are similar, and the problem that a plurality of test cases relate to the same repetition point exists, so that the test efficiency is low, and the user experience is influenced.
In view of the above problems, no effective solution has been proposed.
Disclosure of Invention
The embodiment of the invention provides a method for checking similarity of test cases, which is used for checking the similarity of the test cases, improving the test efficiency and improving the user experience and comprises the following steps:
dividing each test case in the plurality of test cases into a plurality of characteristic items;
for each test case, calculating the weight of each characteristic item in the test case;
establishing a feature vector space for each test case, wherein each feature vector in the feature vector space is the weight of each feature item in the test case;
calculating cosine similarity of feature vector spaces of any two test cases in the plurality of test cases;
and determining whether the two corresponding test cases are similar or not according to the calculated cosine similarity.
The embodiment of the invention also provides a device for checking the similarity of test cases, which is used for combining the test cases with high similarity, improving the test efficiency and improving the user experience, and the device comprises:
the dividing module is used for dividing each test case in the plurality of test cases into a plurality of characteristic items;
the weight calculation module is used for calculating the weight of each characteristic item in each test case;
the characteristic vector space establishing module is used for establishing a characteristic vector space for each test case, and each characteristic vector in the characteristic vector space is the weight of each characteristic item in the test case;
the cosine similarity calculation module is used for calculating the cosine similarity of the feature vector space of any two test cases in the plurality of test cases;
and the determining module is used for determining whether the two corresponding test cases are similar according to the calculated cosine similarity.
The embodiment of the invention also provides computer equipment which comprises a memory, a processor and a computer program which is stored on the memory and can run on the processor, wherein the processor executes the computer program to realize the test case similarity checking method.
The embodiment of the invention also provides a computer readable storage medium, which stores a computer program for executing the test case similarity checking method.
In the embodiment of the invention, each test case in a plurality of test cases is divided into a plurality of characteristic items; for each test case, calculating the weight of each characteristic item in the test case; establishing a feature vector space for each test case, wherein each feature vector in the feature vector space is the weight of each feature item in the test case; calculating cosine similarity of feature vector spaces of any two test cases in the plurality of test cases; and determining whether the two corresponding test cases are similar according to the calculated cosine similarity, so that the similarity of the test cases can be checked, the test efficiency is improved, and the user experience is improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts. In the drawings:
FIG. 1 is a flowchart illustrating a method for checking similarity of test cases according to an embodiment of the present invention;
FIG. 2 is a flowchart illustrating an embodiment of performing test case similarity checking using cosine similarity according to the present invention;
FIG. 3 is a schematic structural diagram of a test case similarity checking apparatus according to an embodiment of the present invention;
FIG. 4 is a schematic diagram illustrating an embodiment of a test case similarity apparatus according to the present invention;
fig. 5 is a schematic structural diagram of a computer device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the embodiments of the present invention are further described in detail below with reference to the accompanying drawings. The exemplary embodiments and descriptions of the present invention are provided to explain the present invention, but not to limit the present invention.
Fig. 1 is a processing flow chart of a test case similarity checking method according to an embodiment of the present invention. As shown in fig. 1, the method for checking similarity of test cases in the embodiment of the present invention may include:
102, calculating the weight of each characteristic item in each test case;
103, establishing a feature vector space for each test case, wherein each feature vector in the feature vector space is the weight of each feature item in the test case;
104, calculating cosine similarity of feature vector spaces of any two test cases in the plurality of test cases;
and 105, determining whether the two corresponding test cases are similar or not according to the calculated cosine similarity.
As can be known from the process shown in fig. 1, in the method for checking similarity of test cases according to the embodiment of the present invention, for each test case, the weight of each feature item in the test case may be calculated, a feature vector space is established according to the weight of each feature item in the test case, cosine similarity of the feature vector spaces of any two test cases in the plurality of test cases is calculated, and whether two corresponding test cases are similar is determined according to the calculated cosine similarity, so that the similarity of the test cases may be checked, the test efficiency is improved, and the user experience is improved.
In specific implementation, each of the plurality of test cases may be divided into a plurality of feature items according to characteristics of the test case, each of the plurality of test cases is represented by the plurality of feature items, and the weight of each of the feature items in the test case is calculated.
In one embodiment, the weight of each feature term in the test case can be calculated as follows:
wherein W (f, d) is the weight of the characteristic item f in the test case d; TF (f, d) is the frequency of occurrence of the feature term f in the test case d; f epsilon d represents that the characteristic item f is in the range of the test case d;
and the frequency of the characteristic item f appearing in the set of the plurality of test cases is shown, N is the total number of the plurality of test cases, and nf is the number of the test cases with the characteristic item f in the plurality of test cases.
In specific implementation, the weight of each feature item in the test case can be calculated by adopting a TF-IDF (Term Frequency-Inverse Document Frequency) statistical method, wherein the TF-IDF is actually: TF x IDF, where TF is Term Frequency (Term Frequency) and IDF is Inverse Document Frequency (Inverse Document Frequency), in the embodiment of the present invention, the importance degree of a feature item to a test case in a test case set can be evaluated, and it can be found through a formula that the importance of a feature item increases in proportion to the number of times it appears in a test case, but decreases in Inverse proportion to the Frequency of it appearing in the test case set.
After the weight of each feature item in the test case is calculated, a feature vector space can be established for each test case, each feature vector in the feature vector space is the weight of each feature item in the test case, each feature item is converted into a feature vector corresponding to each feature item, the cosine similarity of the feature vector spaces of any two test cases in the plurality of test cases is calculated, and the similarity degree between different test cases in the test case set can be obtained step by step.
In one embodiment, the cosine similarity of the feature vector space of any two of the plurality of test cases can be calculated according to the following formula:
wherein, TiThe feature vector space of the ith test case; t isjThe feature vector space of the jth test case; t isitIs the T-th feature vector, T, in the feature vector space of the i-th test casejtIs the t-th feature vector in the feature vector space of the j-th test case.
In specific implementation, when similarity elimination is performed on test cases, the elimination standard of the similar cases can be preset, for example, when cosine similarity of feature vector spaces of any two test cases in a plurality of test cases is calculated, if the cosine similarity of the feature vector spaces of the two test cases is lower than 20%, the similarity of the two test cases is low, and the two test cases can be independently used for testing; if the cosine similarity of the feature vector spaces of the two test cases is between 20% and 50%, the similarity of the two test cases is high, the similar parts of the two test cases can be modified, the cosine similarity of the feature vector spaces of the two test cases is recalculated after the modification, and the two test cases can be independently used for testing until the similarity is lower than 20%; if the cosine similarity of the feature vector space of the two test cases is higher than 50%, the similarity of the two test cases is too high, and the two test cases can be combined into one test case for testing.
In one embodiment, the method may further include: and merging the two similar test cases by adopting a hierarchical clustering algorithm.
In one embodiment, merging two similar test cases by using a hierarchical clustering algorithm may include: and (4) aggregating the similar parts in the two similar test cases by adopting a hierarchical clustering algorithm until the similar parts are combined into one test case.
In specific implementation, still taking the above cosine similarity determination method as an example, when the cosine similarity of the feature vector space of two test cases is higher than 50%, the two similar test cases may be merged by using a hierarchical clustering algorithm, that is, the similar parts in the two similar test cases are aggregated until the two similar test cases are merged into one test case.
FIG. 2 is a flowchart illustrating an embodiment of performing test case similarity checking using cosine similarity according to the present invention. As shown in fig. 2, the process of performing similarity checking on test cases by using cosine similarity in the embodiment of the present invention may include:
and step 207, independently using the two test cases for testing, and ending the process.
And 208, modifying the similar parts of the two test cases, recalculating the cosine similarity of the feature vector spaces of the two test cases after modification until the similarity is lower than 20%, independently using the two test cases for testing, and ending the process.
And 209, combining the two test cases by adopting a hierarchical clustering algorithm, and ending the process.
The embodiment of the invention also provides a device for checking the similarity of the test cases, which is described in the following embodiment. Because the principle of solving the problems of the device is similar to the test case similarity checking method, the implementation of the device can be referred to the implementation of the test case similarity checking method, and repeated parts are not described again.
Fig. 3 is a schematic structural diagram of a test case similarity checking apparatus according to an embodiment of the present invention. As shown in fig. 3, the device for checking similarity of test cases in the embodiment of the present invention may specifically include:
a dividing module 301, configured to divide each of the plurality of test cases into a plurality of feature items;
the weight calculation module 302 is used for calculating the weight of each feature item in each test case;
a feature vector space establishing module 303, configured to establish a feature vector space for each test case, where each feature vector in the feature vector space is a weight of each feature item in the test case;
a cosine similarity calculation module 304, configured to calculate cosine similarities of feature vector spaces of any two test cases in the multiple test cases;
a determining module 305, configured to determine whether the two corresponding test cases are similar according to the calculated cosine similarity.
In one embodiment, the weight calculation module 302 is specifically configured to:
the weight of each feature term in the test case is calculated according to the following formula:
wherein W (f, d) is the weight of the characteristic item f in the test case d; TF (f, d) is taken as a characteristic item f in a test case dThe frequency of the present; f epsilon d represents that the characteristic item f is in the range of the test case d;
and the frequency of the characteristic item f appearing in the set of the plurality of test cases is shown, N is the total number of the plurality of test cases, and nf is the number of the test cases with the characteristic item f in the plurality of test cases.
In one embodiment, the cosine similarity calculation module 304 is specifically configured to:
calculating the cosine similarity of the feature vector spaces of any two test cases in the plurality of test cases according to the following formula:
wherein, TiThe feature vector space of the ith test case; t isjThe feature vector space of the jth test case; t isitIs the T-th feature vector, T, in the feature vector space of the i-th test casejtIs the t-th feature vector in the feature vector space of the j-th test case.
FIG. 4 is a schematic structural diagram of an embodiment of a test case similarity apparatus according to an embodiment of the present invention. As shown in fig. 4, in an embodiment, the test case similarity apparatus shown in fig. 3 further includes:
and a merging module 401, configured to merge the two similar test cases by using a hierarchical clustering algorithm.
In one embodiment, the merging module 401 is specifically configured to:
and (4) aggregating the similar parts in the two similar test cases by adopting a hierarchical clustering algorithm until the similar parts are combined into one test case.
Based on the aforementioned inventive concept, as shown in fig. 5, the present invention further provides a computer apparatus 500, which includes a memory 510, a processor 520, and a computer program 530 stored in the memory 510 and executable on the processor 520, wherein the processor 520 executes the computer program 530 to implement the aforementioned test case similarity checking method.
Based on the foregoing inventive concept, an embodiment of the present invention further provides a computer-readable storage medium, in which a computer program for executing the test case similarity checking method is stored.
In summary, each of the plurality of test cases is divided into a plurality of feature items; for each test case, calculating the weight of each characteristic item in the test case; establishing a feature vector space for each test case, wherein each feature vector in the feature vector space is the weight of each feature item in the test case; calculating cosine similarity of feature vector spaces of any two test cases in the plurality of test cases; and determining whether the two corresponding test cases are similar according to the calculated cosine similarity, so that the similarity of the test cases can be checked, the test efficiency is improved, and the user experience is improved.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are only exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (12)
1. A method for checking similarity of test cases is characterized by comprising the following steps:
dividing each test case in the plurality of test cases into a plurality of characteristic items;
for each test case, calculating the weight of each characteristic item in the test case;
establishing a feature vector space for each test case, wherein each feature vector in the feature vector space is the weight of each feature item in the test case;
calculating cosine similarity of feature vector spaces of any two test cases in the plurality of test cases;
and determining whether the two corresponding test cases are similar or not according to the calculated cosine similarity.
2. The method of claim 1, wherein the weight of each feature term in a test case is calculated as follows:
wherein W (f, d) is the weight of the characteristic item f in the test case d; TF (f, d) is the frequency of occurrence of the feature term f in the test case d; f epsilon d represents that the characteristic item f is in the range of the test case d;
and the frequency of the characteristic item f appearing in the set of the plurality of test cases is shown, N is the total number of the plurality of test cases, and nf is the number of the test cases with the characteristic item f in the plurality of test cases.
3. The method of claim 1, wherein the cosine similarity of the feature vector space of any two of the plurality of test cases is calculated as follows:
wherein, TiThe feature vector space of the ith test case; t isjThe feature vector space of the jth test case; t isitIs the T-th feature vector, T, in the feature vector space of the i-th test casejtIs the t-th feature vector in the feature vector space of the j-th test case.
4. The method of claim 1, further comprising:
and merging the two similar test cases by adopting a hierarchical clustering algorithm.
5. The method of claim 4, wherein merging two similar test cases using a hierarchical clustering algorithm comprises:
and (4) aggregating the similar parts in the two similar test cases by adopting a hierarchical clustering algorithm until the similar parts are combined into one test case.
6. A test case similarity screening apparatus, comprising:
the dividing module is used for dividing each test case in the plurality of test cases into a plurality of characteristic items;
the weight calculation module is used for calculating the weight of each characteristic item in each test case;
the characteristic vector space establishing module is used for establishing a characteristic vector space for each test case, and each characteristic vector in the characteristic vector space is the weight of each characteristic item in the test case;
the cosine similarity calculation module is used for calculating the cosine similarity of the feature vector space of any two test cases in the plurality of test cases;
and the determining module is used for determining whether the two corresponding test cases are similar according to the calculated cosine similarity.
7. The apparatus of claim 6, wherein the weight calculation module is specifically configured to:
the weight of each feature term in the test case is calculated according to the following formula:
wherein W (f, d) is the weight of the characteristic item f in the test case d; TF (f, d) is the frequency of occurrence of the feature term f in the test case d; f epsilon d represents that the characteristic item f is in the range of the test case d;
and the frequency of the characteristic item f appearing in the set of the plurality of test cases is shown, N is the total number of the plurality of test cases, and nf is the number of the test cases with the characteristic item f in the plurality of test cases.
8. The apparatus of claim 6, wherein the cosine similarity calculation module is specifically configured to:
calculating the cosine similarity of the feature vector spaces of any two test cases in the plurality of test cases according to the following formula:
wherein, TiThe feature vector space of the ith test case; t isjThe feature vector space of the jth test case; t isitIs the T-th feature vector, T, in the feature vector space of the i-th test casejtIs the t-th feature vector in the feature vector space of the j-th test case.
9. The apparatus of claim 6, further comprising a merge module to:
and merging the two similar test cases by adopting a hierarchical clustering algorithm.
10. The apparatus of claim 9, wherein the merging module is specifically configured to:
and (4) aggregating the similar parts in the two similar test cases by adopting a hierarchical clustering algorithm until the similar parts are combined into one test case.
11. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of any one of claims 1 to 5 when executing the computer program.
12. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program for executing the method of any one of claims 1 to 5.
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Patent Citations (4)
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| WO2009083844A1 (en) * | 2007-12-20 | 2009-07-09 | Koninklijke Philips Electronics N.V. | Method and device for case-based decision support |
| CN105335496A (en) * | 2015-10-22 | 2016-02-17 | 国网山东省电力公司电力科学研究院 | Customer service repeated call treatment method based on cosine similarity text mining algorithm |
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