CN114090421A - Test set generation method and device, storage medium and electronic equipment - Google Patents
Test set generation method and device, storage medium and electronic equipment Download PDFInfo
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Abstract
The application provides a test set generation method, a test set generation device, a storage medium and electronic equipment, wherein the test set generation method comprises the following steps: acquiring target keywords in a target field; collecting candidate test questions based on the target keywords; and generating a test set by using the candidate test problems meeting the preset conditions. According to the method and the device, the target keywords are used for collecting the candidate test problems in the target field to which the target keywords belong, whether the candidate test problems meet the preset conditions or not is determined, the test set is generated based on the candidate test problems meeting the preset conditions, a large number of candidate test problems can be obtained without manual sampling investigation, and the generation efficiency of the test set is greatly improved; moreover, candidate test problems are derived from search records existing in the network, so that the coverage rate of the generated test set is high.
Description
Technical Field
The present application relates to the field of data processing technologies, and in particular, to a test set generation method and apparatus, a storage medium, and an electronic device.
Background
With the development of the internet, the information on the internet is more and more abundant, the more and more related information is obtained through search engine retrieval, and the problems caused by information explosion are well solved through automatic question answering. Therefore, designing a good question-answering test set is very important for the evaluation and promotion of the question-answering system.
In the prior art, a question and answer obtained by manual sampling survey is generally used for generating a test set, so that the generation efficiency is low and the coverage rate is low.
Disclosure of Invention
In view of this, embodiments of the present application provide a method and an apparatus for generating a test set, a storage medium, and an electronic device, so as to solve the problems of low test set generation efficiency and low coverage rate in the prior art.
In a first aspect, an embodiment of the present application provides a test set generation method, where the method includes:
acquiring target keywords in a target field;
collecting candidate test questions based on the target keywords;
and generating a test set by using the candidate test problems meeting the preset conditions.
In one possible embodiment, the collecting candidate test questions based on the target keywords includes:
splicing the target keywords with the question words to obtain seed problems;
searching for the candidate test problem within the target domain based on the seed problem.
In a possible embodiment, the generating a test set by using the candidate test questions meeting a preset condition includes:
performing semantic analysis on the candidate test problem to determine whether the candidate test problem meets a preset condition; the preset condition is that the semantic definition is greater than a threshold value;
and generating a test set by using the candidate test problems meeting the preset conditions.
In a possible embodiment, the generating a test set by using the candidate test questions meeting a preset condition includes:
if the candidate test problem does not meet the preset condition, deleting the candidate test problem;
if the candidate test problem meets the preset condition, taking the candidate test problem as a target test problem;
all of the target test questions are combined to form the test set.
In a second aspect, an embodiment of the present application further provides a test set generating apparatus, where the test set generating apparatus includes:
an acquisition module that configurably acquires a target keyword within a target field;
a collection module configured to collect candidate test questions based on the target keywords;
a generating module configured to generate a test set using the candidate test problem satisfying a preset condition.
In a possible implementation, the collection module is specifically configured to:
splicing the target keywords with the question words to obtain seed problems;
searching for the candidate test problem within the target domain based on the seed problem.
In a possible implementation, the generating module is specifically configured to:
performing semantic analysis on the candidate test problem to determine whether the candidate test problem meets a preset condition; the preset condition is that the semantic definition is greater than a threshold value;
and generating a test set by using the candidate test problems meeting the preset conditions.
In a possible implementation, the generating module is further specifically configured to:
if the candidate test problem does not meet the preset condition, deleting the candidate test problem;
if the candidate test problem meets the preset condition, taking the candidate test problem as a target test problem;
all of the target test questions are combined to form the test set.
In a third aspect, an embodiment of the present application further provides a storage medium, where the computer readable storage medium stores a computer program, and the computer program is executed by a processor to perform the following steps:
acquiring target keywords in a target field;
collecting candidate test questions based on the target keywords;
and generating a test set by using the candidate test problems meeting the preset conditions.
In a fourth aspect, an embodiment of the present application further provides an electronic device, where the electronic device includes: a processor and a memory, the memory storing machine-readable instructions executable by the processor, the processor and the memory communicating over a bus when an electronic device is operating, the machine-readable instructions when executed by the processor performing the steps of:
acquiring target keywords in a target field;
collecting candidate test questions based on the target keywords;
and generating a test set by using the candidate test problems meeting the preset conditions.
According to the method and the device, the target keywords are used for collecting the candidate test problems in the target field to which the target keywords belong, whether the candidate test problems meet the preset conditions or not is determined, the test set is generated based on the candidate test problems meeting the preset conditions, a large number of candidate test problems can be obtained without manual sampling investigation, and the generation efficiency of the test set is greatly improved; moreover, candidate test problems are derived from search records existing in the network, so that the coverage rate of the generated test set is high.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, it is obvious that the drawings in the following description are only some embodiments described in the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without any creative effort.
FIG. 1 is a flow chart illustrating a test set generation method provided by the present application;
FIG. 2 is a flow chart illustrating candidate test question collection based on target keywords in a test set generation method provided by the present application;
FIG. 3 is a flowchart illustrating a test set generation method according to the present application, wherein the test set is generated by using candidate test questions meeting preset conditions;
FIG. 4 is a schematic diagram illustrating a test set generation apparatus provided in the present application;
fig. 5 shows a schematic structural diagram of an electronic device provided in the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the drawings of the embodiments of the present application. It should be apparent that the described embodiments are only some of the embodiments of the present application, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the described embodiments of the application without any inventive step, are within the scope of protection of the application.
Unless defined otherwise, technical or scientific terms used herein shall have the ordinary meaning as understood by one of ordinary skill in the art to which this application belongs. As used in this application, the terms "first," "second," and the like do not denote any order, quantity, or importance, but rather are used to distinguish one element from another. The word "comprising" or "comprises", and the like, means that the element or item listed before the word covers the element or item listed after the word and its equivalents, but does not exclude other elements or items. The terms "connected" or "coupled" and the like are not restricted to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "upper", "lower", "left", "right", and the like are used merely to indicate relative positional relationships, and when the absolute position of the object being described is changed, the relative positional relationships may also be changed accordingly.
Detailed descriptions of known functions and known components are omitted in the present application in order to keep the following description of the embodiments of the present application clear and concise.
As shown in fig. 1, which is a flowchart of a test set generation method provided in the first aspect of the present application, the specific steps include S101 to S103.
S101, acquiring a target keyword in a target field.
In the specific implementation, the target field is a specific field, such as makeup, food, mobile phone, etc., and the smaller the target field is defined, the more accurate the question-answering system tested by the test set generated by the method of the present application will be.
Further, the target keyword in the target field may be a name of each product, a name of each accessory, a name of each food, etc., and may also be determined according to frequency and number of occurrences in other search engines, etc.
S102, candidate test questions are collected based on the target keywords.
After obtaining the target keywords in the target domain, candidate test questions are collected based on the target keywords, which are some of the questions already existing in a large number of other search engines.
Specifically, the candidate test questions may be collected based on the target keywords with reference to a method flowchart shown in fig. 2, wherein specific steps include S201 and S202.
S201, splicing the target keywords and the query words to obtain a seed problem.
S202, searching candidate test problems in the target field based on the seed problem.
In specific implementation, the target keywords are first spliced with the query words to obtain the seed question, for example, the cosmetics a and what are spliced, that is, the seed question is "what is the cosmetics a", of course, the query words also include "how", and "how much", and each target keyword can be spliced with all the query words to improve the accuracy of the candidate test question.
After the seed question is obtained, candidate test questions in the target field are searched based on the seed question, that is, the seed question is input into other search engines, and questions recommended and displayed by other search engines are used as the candidate test questions. The candidate test problems are all the problems searched by the user on the network, and the truth degree is higher.
For example, after inputting "what cosmetic a", other search engines recommend and display the questions of "what grade product cosmetic a is", "what product good for cosmetic a", "what country for cosmetic a", "what series of cosmetic a is suitable for people of what age", "brand of what country for cosmetic a", "what series good for cosmetic a", "when cosmetic a is established", "when cosmetic a is on the market", "what good for cosmetic a", "when cosmetic a is on the market", etc., all of which are candidate test questions.
And S103, generating a test set by using the candidate test problems meeting the preset conditions.
In the specific implementation, not all candidate test problems can be used as samples in the test set, so after obtaining the candidate test problems, it is further required to determine whether the candidate test problems satisfy the preset conditions, and generate the test set by using the candidate test problems satisfying the preset conditions.
Specifically, fig. 3 shows a flowchart of a method for generating a test set by using candidate test questions satisfying preset conditions, wherein specific steps include S301-S304.
S301, performing semantic analysis on the candidate test problems, and determining whether the candidate test problems meet preset conditions; the preset condition is that the semantic definition is greater than a threshold value.
And S302, if the candidate test problem does not meet the preset condition, deleting the candidate test problem.
And S303, if the candidate test problem meets the preset condition, taking the candidate test problem as a target test problem.
And S304, combining all the target test problems to form a test set.
In specific implementation, the pre-trained semantic model may be used to perform semantic analysis on the candidate test problem to determine whether the candidate test problem meets the preset condition, and of course, other analysis methods may also be used to perform semantic analysis on the candidate test problem as long as the semantic of the candidate test problem can be analyzed, which is not specifically limited in the embodiment of the present application.
The preset condition is that the semantic definition is larger than a threshold value, and the semantic definition represents whether the sentence can form a complete problem, whether ambiguity exists or not and the like. Performing semantic analysis on the candidate test problems to determine whether the candidate test problems meet preset conditions, namely performing semantic analysis on the candidate test problems to obtain semantic definition of the candidate test problems, comparing the semantic definition with a threshold, and determining that the candidate test problems meet the preset conditions if the semantic definition is greater than the threshold; and if the semantic definition is less than or equal to the threshold value, determining that the candidate test problem does not meet the preset condition.
If the candidate test problem does not meet the preset condition, deleting, for example, if the semantic definition of 'what cosmetic A is good at' is smaller than a threshold value, representing that the candidate test problem cannot form a complete problem, directly deleting; and if the candidate test problem meets the preset condition, taking the candidate test problem as a target test problem, and combining all the target test problems to form a test set. The question-answering system is trained by utilizing the test set, the problem does not need to be set manually, and the generation efficiency of the test set and the test efficiency of the question-answering system are greatly improved.
According to the method and the device, the target keywords are used for collecting the candidate test problems in the target field to which the target keywords belong, whether the candidate test problems meet the preset conditions or not is determined, the test set is generated based on the candidate test problems meeting the preset conditions, a large number of candidate test problems can be obtained without manual sampling investigation, and the generation efficiency of the test set is greatly improved; moreover, candidate test problems are derived from search records existing in the network, so that the coverage rate of the generated test set is high.
Based on the same inventive concept, the second aspect of the present application further provides a test set generation apparatus corresponding to the test set generation method, and since the principle of solving the problem of the test set generation apparatus in the present application is similar to that of the test set generation method in the present application, the implementation of the test set generation apparatus may refer to the implementation of the method, and repeated details are not repeated.
Fig. 4 shows a schematic diagram of a test set generating apparatus provided in an embodiment of the present application, which specifically includes:
an acquisition module 401 configured to acquire a target keyword in a target domain;
a collection module 402 configured to collect candidate test questions based on the target keywords;
a generating module 403 configured to generate a test set using the candidate test questions meeting a preset condition.
In yet another embodiment, the collection module 402 is specifically configured to:
splicing the target keywords with the question words to obtain seed problems;
searching for the candidate test problem within the target domain based on the seed problem.
In another embodiment, the generating module 403 is specifically configured to:
performing semantic analysis on the candidate test problem to determine whether the candidate test problem meets a preset condition; the preset condition is that the semantic definition is greater than a threshold value;
and generating a test set by using the candidate test problems meeting the preset conditions.
In another embodiment, the generating module 403 is further specifically configured to:
if the candidate test problem does not meet the preset condition, deleting the candidate test problem;
if the candidate test problem meets the preset condition, taking the candidate test problem as a target test problem;
all of the target test questions are combined to form the test set.
According to the method and the device, the target keywords are used for collecting the candidate test problems in the target field to which the target keywords belong, whether the candidate test problems meet the preset conditions or not is determined, the test set is generated based on the candidate test problems meeting the preset conditions, a large number of candidate test problems can be obtained without manual sampling investigation, and the generation efficiency of the test set is greatly improved; moreover, candidate test problems are derived from search records existing in the network, so that the coverage rate of the generated test set is high.
The storage medium is a computer-readable medium, and stores a computer program, and when the computer program is executed by a processor, the method provided in any embodiment of the present application is implemented, including the following steps S11 to S13:
s11, acquiring target keywords in the target field;
s12, collecting candidate test questions based on the target keywords;
and S13, generating a test set by using the candidate test problems meeting the preset conditions.
When the computer program is executed by the processor to collect the candidate test questions based on the target keywords, the following steps are specifically executed by the processor: splicing the target keywords with the question words to obtain seed problems; searching for the candidate test problem within the target domain based on the seed problem.
When the computer program is executed by the processor to generate a test set by using the candidate test problems meeting the preset conditions, the processor further executes the following steps: performing semantic analysis on the candidate test problem to determine whether the candidate test problem meets a preset condition; the preset condition is that the semantic definition is greater than a threshold value; and generating a test set by using the candidate test problems meeting the preset conditions.
When the computer program is executed by the processor to generate a test set by using the candidate test problems meeting the preset conditions, the processor further executes the following steps: if the candidate test problem does not meet the preset condition, deleting the candidate test problem; if the candidate test problem meets the preset condition, taking the candidate test problem as a target test problem; all of the target test questions are combined to form the test set.
According to the method and the device, the target keywords are used for collecting the candidate test problems in the target field to which the target keywords belong, whether the candidate test problems meet the preset conditions or not is determined, the test set is generated based on the candidate test problems meeting the preset conditions, a large number of candidate test problems can be obtained without manual sampling investigation, and the generation efficiency of the test set is greatly improved; moreover, candidate test problems are derived from search records existing in the network, so that the coverage rate of the generated test set is high.
An electronic device is provided in an embodiment of the present application, and a schematic structural diagram of the electronic device may be as shown in fig. 5, where the electronic device at least includes a memory 501 and a processor 502, a computer program is stored on the memory 501, and the processor 502 implements the method provided in any embodiment of the present application when executing the computer program on the memory 501. Illustratively, the electronic device computer program steps are as follows S21-S23:
s21, acquiring a target keyword in the target field;
s22, collecting candidate test questions based on the target keywords;
and S23, generating a test set by using the candidate test problems meeting the preset conditions.
The processor, in executing the candidate test question collected based on the target keyword stored on the memory, further executes a computer program that: splicing the target keywords with the question words to obtain seed problems; searching for the candidate test problem within the target domain based on the seed problem.
The processor, when executing the test set generated by the candidate test problem satisfying the preset condition stored in the memory, further executes the following computer program: performing semantic analysis on the candidate test problem to determine whether the candidate test problem meets a preset condition; the preset condition is that the semantic definition is greater than a threshold value; and generating a test set by using the candidate test problems meeting the preset conditions.
The processor, when executing the test set generated by the candidate test problem satisfying the preset condition stored in the memory, further executes the following computer program: if the candidate test problem does not meet the preset condition, deleting the candidate test problem; if the candidate test problem meets the preset condition, taking the candidate test problem as a target test problem; all of the target test questions are combined to form the test set.
According to the method and the device, the target keywords are used for collecting the candidate test problems in the target field to which the target keywords belong, whether the candidate test problems meet the preset conditions or not is determined, the test set is generated based on the candidate test problems meeting the preset conditions, a large number of candidate test problems can be obtained without manual sampling investigation, and the generation efficiency of the test set is greatly improved; moreover, candidate test problems are derived from search records existing in the network, so that the coverage rate of the generated test set is high.
Optionally, in this embodiment, the storage medium may include, but is not limited to: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes. Optionally, in this embodiment, the processor executes the method steps described in the above embodiments according to the program code stored in the storage medium. Optionally, the specific examples in this embodiment may refer to the examples described in the above embodiments and optional implementation manners, and this embodiment is not described herein again. It will be apparent to those skilled in the art that the modules or steps of the present application described above may be implemented by a general purpose computing device, they may be centralized on a single computing device or distributed across a network of multiple computing devices, and alternatively, they may be implemented by program code executable by a computing device, such that they may be stored in a storage device and executed by a computing device, and in some cases, the steps shown or described may be performed in an order different than that described herein, or they may be separately fabricated into individual integrated circuit modules, or multiple ones of them may be fabricated into a single integrated circuit module. Thus, the present application is not limited to any specific combination of hardware and software.
Moreover, although exemplary embodiments have been described herein, the scope thereof includes any and all embodiments based on the present application with equivalent elements, modifications, omissions, combinations (e.g., of various embodiments across), adaptations or alterations. The elements of the claims are to be interpreted broadly based on the language employed in the claims and not limited to examples described in the present specification or during the prosecution of the application, which examples are to be construed as non-exclusive. It is intended, therefore, that the specification and examples be considered as exemplary only, with a true scope and spirit being indicated by the following claims and their full scope of equivalents.
The above description is intended to be illustrative and not restrictive. For example, the above-described examples (or one or more versions thereof) may be used in combination with each other. For example, other embodiments may be used by those of ordinary skill in the art upon reading the above description. In addition, in the above detailed description, various features may be grouped together to streamline the application. This should not be interpreted as an intention that a disclosed feature not claimed is essential to any claim. Rather, subject matter of the present application can lie in less than all features of a particular disclosed embodiment. Thus, the following claims are hereby incorporated into the detailed description as examples or embodiments, with each claim standing on its own as a separate embodiment, and it is contemplated that these embodiments may be combined with each other in various combinations or permutations. The scope of the application should be determined with reference to the appended claims, along with the full scope of equivalents to which such claims are entitled.
The embodiments of the present application have been described in detail, but the present application is not limited to these specific embodiments, and those skilled in the art can make various modifications and modified embodiments based on the concept of the present application, and these modifications and modified embodiments should fall within the scope of the present application.
Claims (10)
1. A test set generation method, comprising:
acquiring target keywords in a target field;
collecting candidate test questions based on the target keywords;
and generating a test set by using the candidate test problems meeting the preset conditions.
2. The test set generation method of claim 1, wherein the collecting candidate test questions based on the target keywords comprises:
splicing the target keywords with the question words to obtain seed problems;
searching for the candidate test problem within the target domain based on the seed problem.
3. The method according to claim 1, wherein the generating a test set using the candidate test questions satisfying a preset condition comprises:
performing semantic analysis on the candidate test problem to determine whether the candidate test problem meets a preset condition; the preset condition is that the semantic definition is greater than a threshold value;
and generating a test set by using the candidate test problems meeting the preset conditions.
4. The method according to claim 1 or 3, wherein the generating a test set by using the candidate test questions satisfying a preset condition comprises:
if the candidate test problem does not meet the preset condition, deleting the candidate test problem;
if the candidate test problem meets the preset condition, taking the candidate test problem as a target test problem;
all of the target test questions are combined to form the test set.
5. A test set generation apparatus, comprising:
an acquisition module that configurably acquires a target keyword within a target field;
a collection module configured to collect candidate test questions based on the target keywords;
a generating module configured to generate a test set using the candidate test problem satisfying a preset condition.
6. The test set generation apparatus of claim 5, wherein the collection module is specifically configured to:
splicing the target keywords with the question words to obtain seed problems;
searching for the candidate test problem within the target domain based on the seed problem.
7. The test set generation apparatus of claim 5, wherein the generation module is specifically configured to:
performing semantic analysis on the candidate test problem to determine whether the candidate test problem meets a preset condition; the preset condition is that the semantic definition is greater than a threshold value;
and generating a test set by using the candidate test problems meeting the preset conditions.
8. The test set generation apparatus of claim 5 or 7, wherein the generation module is further specifically configured to:
if the candidate test problem does not meet the preset condition, deleting the candidate test problem;
if the candidate test problem meets the preset condition, taking the candidate test problem as a target test problem;
all of the target test questions are combined to form the test set.
9. A storage medium, having a computer program stored thereon, the computer program when executed by a processor performing the steps of:
acquiring target keywords in a target field;
collecting candidate test questions based on the target keywords;
and generating a test set by using the candidate test problems meeting the preset conditions.
10. An electronic device, comprising: a processor and a memory, the memory storing machine-readable instructions executable by the processor, the processor and the memory communicating over a bus when an electronic device is operating, the machine-readable instructions when executed by the processor performing the steps of:
acquiring target keywords in a target field;
collecting candidate test questions based on the target keywords;
and generating a test set by using the candidate test problems meeting the preset conditions.
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108647322A (en) * | 2018-05-11 | 2018-10-12 | 四川师范大学 | The method that word-based net identifies a large amount of Web text messages similarities |
| CN112035614A (en) * | 2020-08-31 | 2020-12-04 | 康键信息技术（深圳）有限公司 | Test set generation method and device, computer equipment and storage medium |
| WO2021126757A1 (en) * | 2019-12-20 | 2021-06-24 | UiPath, Inc. | System and computer-implemented method for analyzing test automation workflow of robotic process automation (rpa) |
-
2021
- 2021-09-17 CN CN202111094233.9A patent/CN114090421A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108647322A (en) * | 2018-05-11 | 2018-10-12 | 四川师范大学 | The method that word-based net identifies a large amount of Web text messages similarities |
| WO2021126757A1 (en) * | 2019-12-20 | 2021-06-24 | UiPath, Inc. | System and computer-implemented method for analyzing test automation workflow of robotic process automation (rpa) |
| CN112035614A (en) * | 2020-08-31 | 2020-12-04 | 康键信息技术（深圳）有限公司 | Test set generation method and device, computer equipment and storage medium |
Non-Patent Citations (1)
| Title |
|---|
| 化云龙: "Web应用测试用例自动生成技术研究", 《CNKI优秀硕士学位论文全文库 信息科技辑》, no. 12, 15 December 2018 (2018-12-15), pages 139 - 103 * |
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