Test scheme review method and device, equipment and storage medium
By extracting keywords and identifying named entities in the existing test scheme of bank software, generating a set of historical test points and comparing them with the current test scheme, the problem of high cost and easy omission of manual screening and comparison of test points is solved, and the effect of reducing costs and improving testing efficiency is achieved.
Patent Information
- Application Number
- CN202411841996.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-13
- Publication Date
- 2025-03-25
AI Technical Summary
After bank software development, testers need to test a large amount of software, which leads to the cost of manual screening and comparison of test points and is prone to missed test points.
By extracting keywords and identifying named entities in the stock test scheme, a set of historical test points is generated, and comparing it with the current test scheme, the test points that may be missed can be supplemented and generated.
It reduces the cost of manual screening and comparison of test points, avoids the problem of missing test points, and improves test efficiency and accuracy.
Smart Images
Figure CN119690839A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the field of financial technology, and in particular to a test program review method, device, equipment and storage medium. Background Art
[0002] After the banking software is developed, testers need to test the software. During the testing process, they need to determine whether any test points are missed.
[0003] At present, in order to prevent the problem of missing test points during the test process, the test points that need to be regularly summarized as pointed out in the test guidance documents are often screened and compared manually to confirm whether there are any missing test points.
[0004] However, when there are a large number of software to be tested and / or a large number of test points in the software, not only is the cost of manually screening and comparing the test points high, but there is also the problem of missing test points that is inevitable. Summary of the invention
[0005] In order to reduce the cost of manual screening and comparison of test points and prevent the problem of missing test points, the present application provides a test plan review method, device, equipment and storage medium.
[0006] In a first aspect, the present application provides a test plan review method, comprising:
[0007] Extract keywords from the existing test solutions to obtain a keyword set;
[0008] Identify the existing test schemes to obtain a named entity set;
[0009] Obtaining a historical test key point set based on the keyword set and the named entity set;
[0010] A target test plan is determined based on the historical test key point set and the current test plan.
[0011] Through the above implementation, a historical test point set can be determined by extracting the existing test plans. The historical test point set is used to reflect the test points of the historical test plans for software similar to the current test software. Then, by comparing the historical test point set with the current test plan, the test points that may be missed in the current test plan can be determined. This makes it easy to supplement the test points missed in the current test plan and obtain the target test plan. The process of obtaining the target test plan does not require large-scale manual intervention, which makes it easy to reduce the cost of manual screening and comparison of test points and prevent the problem of missing test points.
[0012] Preferably, extracting keywords from the existing test solutions to obtain a keyword set includes:
[0013] Performing word segmentation processing on the stock test scheme to obtain a target word list;
[0014] A keyword set is obtained based on the target word list and a preset word frequency threshold.
[0015] Through the above implementation, by performing word segmentation processing on the existing test solutions, it is convenient to subsequently extract keywords corresponding to the existing test solutions from the word segmentation.
[0016] Preferably, obtaining a keyword set based on the target word list and a preset word frequency threshold comprises:
[0017] Constructing a word frequency matrix based on the target word list;
[0018] Calculating the word frequency value of each word in the word frequency matrix to obtain a word frequency value set;
[0019] A keyword set is obtained based on the word frequency value set and a preset word frequency threshold.
[0020] Through the above implementation, by comparing the word frequency of the segmented words with the word frequency threshold, it is convenient to use the segmented words with higher word frequency as keywords to improve the accuracy of keyword confirmation.
[0021] Preferably, obtaining a historical test key point set based on the keyword set and the named entity set includes:
[0022] Determine the keyword meaning of each keyword in the keyword set, and also determine the named entity meaning of each named entity in the named entity set;
[0023] The keywords and the named entities whose meanings are similar to each other are merged to obtain a historical test key point set.
[0024] Through the above implementation, by comparing the meanings of keywords and the meanings of named entities, it is convenient to merge the keywords and named entities with similar meanings.
[0025] Preferably, determining the target test scheme based on the historical test key point set and the current test scheme includes:
[0026] Extracting test points from the current test plan to obtain the current test point set;
[0027] Calculating the similarity between the current test key point in the current test key point set and each historical test key point in the historical test key point set to obtain a similarity set;
[0028] A target test plan is determined based on the similarity set, a preset similarity threshold, and the historical test key point set.
[0029] Through the above implementation, the similarity between the current test points and the historical test points is calculated, which can provide reference data for the subsequent determination of the target test plan.
[0030] Preferably, determining the target test scheme based on the similarity set, the preset similarity threshold and the historical test key point set includes:
[0031] In response to the similarity set not having the similarity less than the similarity threshold, taking the current test scheme as a target test scheme;
[0032] In response to the similarity set having the similarity less than the similarity threshold, displaying the test points to be added to the user; wherein the test points to be added are historical test points corresponding to the similarity less than the similarity threshold;
[0033] In response to obtaining an adding instruction issued by a user, determining the to-be-added test point corresponding to the adding instruction as a newly added test point, and adding the newly added test point to the current test plan to obtain a new current test plan;
[0034] The similarity between the current test points in the new current test point set and each of the historical test points in the historical test point set is calculated to obtain a new similarity set, until the new similarity set does not have the similarity less than the similarity threshold, and the new current test plan is used as the target test plan.
[0035] Through the above implementation, when there is a similarity less than the similarity threshold in the similarity set, the target test plan can be finally determined by cyclically calculating the similarity between the new current test points and the historical test points.
[0036] In a second aspect, the present application provides a test plan review device, comprising:
[0037] A keyword extraction module is used to extract keywords from existing test solutions to obtain a keyword set;
[0038] An entity recognition module, used to identify the existing test scheme to obtain a named entity set;
[0039] A key point acquisition module, used for obtaining a historical test key point set based on the keyword set and the named entity set;
[0040] A scheme determination module is used to determine a target test scheme based on the historical test key point set and the current test scheme.
[0041] Through the above implementation, a historical test point set can be determined by extracting the existing test plans. The historical test point set is used to reflect the test points of the historical test plans for software similar to the current test software. Then, by comparing the historical test point set with the current test plan, the test points that may be missed in the current test plan can be determined. This makes it easy to supplement the test points missed in the current test plan and obtain the target test plan. The process of obtaining the target test plan does not require large-scale manual intervention, which makes it easy to reduce the cost of manual screening and comparison of test points and prevent the problem of missing test points.
[0042] In a third aspect, the present application provides a computer device, the computer device comprising a memory and a processor, the memory storing a computer program, and the processor implementing the steps in the above method when executing the computer program.
[0043] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which implements the steps in the above method when executed by a processor.
[0044] In a fifth aspect, the present application further provides a computer program product, wherein the computer program product comprises a computer program, and when the computer program is executed by a processor, the steps in any of the above method embodiments are implemented.
[0045] The above-mentioned test plan review method, device, equipment and storage medium obtain a keyword set by extracting keywords from existing test plans; obtain a named entity set by identifying the existing test plans; obtain a historical test point set based on the keyword set and the named entity set; and determine a target test plan based on the historical test point set and the current test plan. Through the above implementation, a historical test point set can be determined by extracting existing test plans. The historical test point set is used to reflect the test points of the test plans of software similar to the current test software in history. Then, by comparing the historical test point set with the current test plan, the test points that may be missed in the current test plan can be determined, which makes it easy to supplement the test points missed by the current test plan, thereby obtaining the target test plan; the process of obtaining the target test plan does not require large-scale human intervention, which makes it easy to reduce the cost of manual screening and comparison of test points and prevent the problem of missing test points.
[0046] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS
[0047] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.
[0048] Figure 1 A flow chart of a test scheme review method provided in an embodiment of the present application;
[0049] Figure 2 A schematic diagram of the structure of a test scheme review device provided in an embodiment of the present application;
[0050] Figure 3 A schematic diagram of the structure of a computer device provided in an embodiment of the present application;
[0051] Figure 4 This is a diagram of the internal structure of a computer-readable storage medium provided in an embodiment of the present application. DETAILED DESCRIPTION
[0052] In order to make the purpose, technical solution and advantages of the present disclosure more clear, the present disclosure is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present disclosure and are not used to limit the present disclosure.
[0053] It should be noted that the terms "first", "second", etc. in the specification and claims of this article and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of this article described here can be implemented in an order other than those illustrated or described here. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, device, product or equipment that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or equipment.
[0054] In this article, the term "and / or" is only a description of the association relationship between related objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the related objects before and after are in an "or" relationship.
[0055] Embodiment 1
[0056] Figure 1A flow chart of a test plan review method provided in Example 1 of this application, refer to Figure 1 The method may be performed by a device for performing the method, and the device may be implemented by software and / or hardware. The method includes:
[0057] S110, extract keywords from the existing test solutions to obtain a keyword set.
[0058] Among them, the existing test plan is a historically existing test plan, and the test plan is a plan for testing the initially developed banking software; keyword extraction is the process of processing the existing test plan according to a preset keyword extraction algorithm; the keyword set is a set of keywords extracted from the existing test plan by the keyword extraction algorithm.
[0059] Specifically, the stock test scheme is processed according to a preset keyword extraction algorithm to obtain a keyword set.
[0060] S120: Identify the existing test schemes to obtain a named entity set.
[0061] Among them, recognition means using corresponding recognition algorithms to process the existing test schemes, so as to obtain named entities in the existing test schemes. Exemplarily, the recognition algorithm specifically adopts an open source word segmentation library combined with a part-of-speech tagging method, wherein the open source word segmentation specifically adopts the jieba library in this embodiment, and is not specifically limited in other embodiments; the named entity set is a set of identified named entities.
[0062] Specifically, the existing test scheme is processed by a preset recognition algorithm to obtain a named entity set.
[0063] S130: Obtain a historical test key point set based on the keyword set and the named entity set.
[0064] Among them, the historical test key points set is a collection of historical test key points; the historical test key points are test key points corresponding to the existing test solutions; the historical test key points set can be obtained by processing the keyword set and the named entity set.
[0065] Specifically, the obtained keyword set and named entity set are processed to obtain a historical test key point set.
[0066] S140: Determine a target test plan based on the historical test key point set and the current test plan.
[0067] Among them, by comparing the historical test point set with the current test plan, the test points that may be missed in the current test plan can be determined, and the missed test points are subsequently added to the current test plan to prevent the current test plan from missing test points; if there are missed test points, the target test plan is a new test plan obtained by adding the missed test points to the current test plan; if there are no missed test points, the target test plan is also the current test plan.
[0068] Specifically, by performing corresponding comparison processing on the historical test point set and the current test plan, it is determined whether there are any missing test points in the current test plan. If so, the missing test points are added to the current test plan to obtain the target test plan. Otherwise, the current test plan is used as the target test plan.
[0069] It should be noted that, in this embodiment, a keyword set is obtained by extracting keywords from existing test schemes; a named entity set is obtained by identifying the existing test schemes; a historical test point set is obtained based on the keyword set and the named entity set; and a target test scheme is determined based on the historical test point set and the current test scheme. Through the above implementation, a historical test point set can be determined by extracting existing test schemes, and the historical test point set is used to reflect the test points of the test schemes of software similar to the current test software in history. Then, by comparing the historical test point set with the current test scheme, the test points that may be missed in the current test scheme can be determined, which makes it easy to supplement the test points missed in the current test scheme, thereby obtaining the target test scheme; the process of obtaining the target test scheme does not require large-scale human intervention, which makes it easy to reduce the cost of manual screening and comparison of test points and prevent the problem of missing test points.
[0070] In addition, this method does not require testers to conduct detailed design plan analysis and historical test point comparison. The test points can be automatically extracted after only selecting the product direction, which greatly saves the working time of testers in writing test plans. Moreover, it has an evolutionary function, which can continuously improve the accuracy of test point extraction and recommendation of the test plan review device according to the feedback of testers during actual use.
[0071] Embodiment 2
[0072] A test plan review method provided in the second embodiment of the present application optimizes the "extracting keywords from the existing test plans to obtain a keyword set" in the first embodiment; it should be noted that for the parts not described in detail in this embodiment, reference can be made to the descriptions of other embodiments, and the method includes:
[0073] S211, performing word segmentation processing on the existing test scheme to obtain a target word list.
[0074] Among them, the word segmentation process is to first perform word segmentation on the existing test scheme, and then remove stop words. In this implementation, the open-source word segmentation library Jieba library is specifically used to perform word segmentation on the existing test scheme, so as to obtain a primary word list. The primary word list is a set of multiple words obtained by performing word segmentation on the existing test scheme. It should be noted that there are many stop words in the primary word list that contribute less to understanding the specific meaning of the existing test scheme, such as "de", "le", "shi", "zai", etc. To improve the accuracy of word segmentation, it is necessary to remove the stop words in the primary word list. The target word list is the new word list obtained after removing the stop words from the primary word list.
[0075] Specifically, use a preset word segmentation library to perform word segmentation on the existing test scheme to obtain a primary word list; further, remove the stop words in the primary word list to obtain the target word list.
[0076] S212. Obtain a keyword set based on the target word list and a preset word frequency threshold.
[0077] Among them, the keywords of the existing test scheme exist in the target word list; the word frequency threshold is a preset value used to judge and process the target word list to determine the keywords of the existing test scheme; the keyword set is a set of keywords determined from the target word list.
[0078] Specifically, judge the words in the target word list through a preset word frequency threshold to determine the corresponding keywords, and obtain the keyword set by aggregating the determined keywords.
[0079] S220. Identify the existing test scheme to obtain a named entity set.
[0080] S230. Obtain a historical test key point set based on the keyword set and the named entity set.
[0081] S240. Determine the target test scheme based on the historical test key point set and the current test scheme.
[0082] Embodiment III
[0083] A test scheme review method provided in Embodiment III of this application optimizes the "obtaining a keyword set based on the target word list and a preset word frequency threshold" in Embodiment II. It should be noted that for parts not detailed in this embodiment, reference can be made to the descriptions of other embodiments. The method includes:
[0084] S311. Perform word segmentation processing on the existing test scheme to obtain a target word list.
[0085] S3121. Construct a word frequency matrix based on the target word list.
[0086] Among them, some words in the target word list may exist only once, while some words may be repeated in multiple words; in order to intuitively reflect the words and the number of each word in the target word list, a word frequency matrix corresponding to the target word list can be constructed, and the word frequency matrix includes multiple non-repeated words and the number of each non-repeated word.
[0087] Specifically, a word frequency matrix is constructed according to each non-repeated word and the number of non-repeated words in the target word list.
[0088] S3122. Calculate the word frequency value of each word in the word frequency matrix to obtain a word frequency value set.
[0089] Among them, the word frequency value is the ratio of the number of corresponding words to the total number of word segments in the word frequency matrix. For example, the number of words "test time" is 3, and the total number of word segments in the word frequency matrix is 100, then the word frequency value corresponding to the word "test time" is 0.03; the word frequency value set is the set of word frequency values of each word in the word frequency matrix.
[0090] Specifically, according to the number of each word in the word frequency matrix and the total number of word segments in the word frequency matrix, the word frequency value of each word in the word frequency matrix is calculated respectively, and the word frequency values are aggregated to obtain a word frequency value set.
[0091] S3123. Obtain a keyword set based on the word frequency value set and a preset word frequency threshold.
[0092] Among them, the preset word frequency threshold is used to compare with each word frequency value in the word frequency value set. If the word frequency value is greater than the word frequency threshold, it means that the word corresponding to the word frequency value appears more frequently in the stock test scheme. At this time, the word corresponding to the word frequency value can be used as the keyword of the stock test scheme; the keyword set is a collection of the judged keywords.
[0093] Specifically, it is determined whether the word frequency value in the word frequency value set is greater than a preset word frequency threshold. If so, the word corresponding to the word frequency value is used as a keyword, and the confirmed keywords are collected to obtain a keyword set.
[0094] S320: Identify the existing test schemes to obtain a named entity set.
[0095] S330: Obtain a historical test key point set based on the keyword set and the named entity set.
[0096] S340: Determine a target test plan based on the historical test key point set and the current test plan.
[0097] Embodiment 4
[0098] A test plan review method provided in the fourth embodiment of the present application optimizes the "obtaining a historical test key point set based on the keyword set and the named entity set" in the first embodiment; it should be noted that for the parts not described in detail in this embodiment, reference can be made to the descriptions of other embodiments, and the method includes:
[0099] S410: Extract keywords from the existing test solutions to obtain a keyword set.
[0100] S420: Identify the existing test schemes to obtain a named entity set.
[0101] S431: Determine the keyword meaning of each keyword in the keyword set, and also determine the named entity meaning of each named entity in the named entity set.
[0102] Among them, by processing the keywords in the keyword set through the preset semantic parsing model, the meaning of the corresponding keywords can be determined, and the determined meaning of the keywords is recorded as the keyword meaning; the preset semantic parsing model can also process the named entities in the named entity set to determine the meaning of the named entities, and the meaning of the named entities is recorded as the named entity meaning.
[0103] Specifically, each keyword in the keyword set is input into a preset semantic parsing model for processing, thereby obtaining the keyword meaning of each keyword in turn; each named entity in the named entity set is input into a preset semantic parsing model for processing, thereby obtaining the named entity meaning of each named entity in turn.
[0104] S432: Merge the keywords and the named entities whose meanings are similar to each other to obtain a historical test key point set.
[0105] Among them, if the meaning of a keyword is similar to the meaning of a named entity, it means that the keyword corresponding to the keyword meaning and the named entity corresponding to the named entity meaning have a corresponding relationship. At this time, the keyword and the named entity meaning with the corresponding relationship need to be merged into a corresponding historical test point; the historical test point set is the set of each historical test point obtained.
[0106] Specifically, taking one of the keyword meanings as an example, determine whether there is a named entity meaning that is similar in meaning to the keyword meaning. If so, merge the keyword corresponding to the keyword meaning and the named entity corresponding to the named entity meaning to obtain a historical test point; judge the meaning of each keyword according to the above steps to obtain at least one historical test point, and combine the historical test points to obtain a historical test point set.
[0107] S440: Determine a target test plan based on the historical test key point set and the current test plan.
[0108] Embodiment 5
[0109] A test plan review method provided in Embodiment 5 of the present application optimizes the “determining a target test plan based on the historical test key point set and the current test plan” in Embodiment 1; it should be noted that for the parts not described in detail in this embodiment, reference may be made to the descriptions of other embodiments, and the method includes:
[0110] S510: Extract keywords from the existing test solutions to obtain a keyword set.
[0111] S520: Identify the existing test schemes to obtain a named entity set.
[0112] S530: Obtain a historical test key point set based on the keyword set and the named entity set.
[0113] S541. Extract test key points from the current test plan to obtain a current test key point set.
[0114] The current test plan is the test plan corresponding to the bank software currently to be tested, from which several current test points can be proposed, and the current test point set is a collection of several current test points.
[0115] Specifically, the current test points in the current test plan are extracted, and the current test points are collected to obtain a current test point set.
[0116] S542: Calculate the similarity between the current test key point in the current test key point set and each historical test key point in the historical test key point set to obtain a similarity set.
[0117] Among them, the feature vector of the current test point in the current test point set can be calculated through the preset feature vector calculation algorithm, and the feature vector of the current test point is recorded as the current feature vector; the feature vector of each historical test point in the historical test point set can also be calculated through the preset feature vector calculation algorithm, and the feature vector of the historical test point is recorded as the historical feature vector; further, the similarity between the current feature vector and the historical feature vector can be calculated through the preset cosine similarity algorithm, and the similarity is used as the similarity between the corresponding current test point and the historical test point; the similarity set is a set of similarities between the current test point in the current test point set and each historical test point in the historical test point set.
[0118] Specifically, taking a current test point in the current test point set as an example, the current test point in the current test point set is input into a preset feature vector calculation algorithm for processing, so as to obtain a current feature vector corresponding to the current test point; each historical test point in the historical test point set is also input into the feature vector calculation algorithm for processing, so as to obtain historical feature vectors corresponding to each historical test point; then, the similarity between the current feature vector and each historical feature vector is calculated through a preset cosine similarity algorithm, and each similarity is combined to obtain a similarity set.
[0119] S543: Determine a target test solution based on the similarity set, a preset similarity threshold, and the historical test key point set.
[0120] The preset similarity threshold is used to process the similarity set, so as to determine whether to add the corresponding historical test points in the historical test point set to the current test plan, so as to determine the target test plan.
[0121] Specifically, a preset similarity threshold is used to process the similarity set to determine whether to add the corresponding historical test points in the historical test point set to the current test plan. If not, the current test plan is used as the target test plan; otherwise, the corresponding historical test points in the historical test point set are added to the current test plan to obtain the target test plan.
[0122] Embodiment 6
[0123] A test plan review method provided in Example 6 of the present application optimizes the "determining a target test plan based on the similarity set, the preset similarity threshold and the historical test key point set" in Example 5; it should be noted that for the parts not described in detail in this embodiment, reference may be made to the descriptions of other embodiments, and the method includes:
[0124] S610: Extract keywords from the existing test solutions to obtain a keyword set.
[0125] S620: Identify the existing test schemes to obtain a named entity set.
[0126] S630: Obtain a historical test key point set based on the keyword set and the named entity set.
[0127] S641. Extract test key points from the current test plan to obtain a current test key point set.
[0128] S642: Calculate the similarity between the current test key point in the current test key point set and each historical test key point in the historical test key point set to obtain a similarity set.
[0129] S6431. In response to the similarity set not having the similarity less than the similarity threshold, taking the current test scheme as the target test scheme.
[0130] Among them, the similarity threshold is used to compare with each similarity in the similarity set. If there is no similarity less than the similarity threshold in the similarity set, it means that there are no missing test points in the current test plan compared with the existing test plan. At this time, the current test plan can be directly used as the final target test plan.
[0131] Specifically, it is determined whether there is a similarity less than a similarity threshold in the similarity set, and if not, the current test solution is used as the target test solution.
[0132] S6432. In response to the similarity set having a similarity smaller than a similarity threshold, display the test points to be added to the user; wherein the test points to be added are historical test points corresponding to the similarity smaller than the similarity threshold.
[0133] Among them, if there is a similarity less than the similarity threshold in the similarity set, it means that there are missing test points in the current test plan compared with the existing test plan, and the missing test points are the historical test points corresponding to the similarity less than the similarity threshold; at this time, the historical test points corresponding to the similarity less than the similarity threshold, that is, the test points to be added, need to be displayed to the user in the form of interface interaction or command line interaction, so as to facilitate the selection of the test points to be added to the current test plan, so that all the test points to be added can be selected at one time for addition, or the test points to be added can be selected one by one for addition.
[0134] Specifically, if there is a similarity less than the similarity threshold in the similarity set, the historical test points corresponding to the similarity less than the similarity threshold, that is, the test points to be added, are displayed to the user.
[0135] S6433: In response to obtaining an adding instruction issued by a user, determining the test point to be added corresponding to the adding instruction as a newly added test point, and adding the newly added test point to the current test plan to obtain a new current test plan.
[0136] Among them, after the user receives the test points to be added, when selecting the test points to be added, a corresponding adding instruction will also be generated. The adding instruction is used to control the adding of the test points to be added selected by the user to the current test plan; the newly added test points are the test points to be added corresponding to the adding instruction.
[0137] Specifically, in response to obtaining an adding instruction issued by the user, the to-be-added test point corresponding to the adding instruction is determined as a newly added test point, and the newly added test point is further added to the current test plan, thereby obtaining a new current test plan.
[0138] S6434. Calculate the similarity between the current test points in the new current test point set and each of the historical test points in the historical test point set to obtain a new similarity set, until the new similarity set does not have a similarity less than a similarity threshold, and use the new current test plan as the target test plan.
[0139] After obtaining the new current test point set, return to execute S642, and execute S642-S6434 in a loop until it is determined that the new similarity set does not have the similarity less than the similarity threshold. At this time, a new current test plan, that is, the target test plan, is obtained.
[0140] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.
[0141] Embodiment 7
[0142] Based on the same inventive concept, this embodiment also provides a test plan review device for implementing the test plan review method involved above. The implementation solution provided by the device to solve the problem is similar to the implementation solution recorded in the above method, so the specific limitations in one or more test plan review device embodiments provided below can refer to the limitations of the test plan review method above, and will not be repeated here.
[0143] In this embodiment, Figure 2 As shown, a test plan review device is provided, comprising:
[0144] A keyword extraction module is used to extract keywords from existing test solutions to obtain a keyword set;
[0145] An entity recognition module, used to identify the existing test scheme to obtain a named entity set;
[0146] A key point acquisition module, used for obtaining a historical test key point set based on the keyword set and the named entity set;
[0147] A scheme determination module is used to determine a target test scheme based on the historical test key point set and the current test scheme.
[0148] Each module in the above-mentioned test scheme review device can be implemented in whole or in part by software, hardware and their combination. Each of the above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the corresponding operations of each of the above modules.
[0149] It should be noted that, in this embodiment, a keyword set is obtained by extracting keywords from existing test schemes; a named entity set is obtained by identifying the existing test schemes; a historical test point set is obtained based on the keyword set and the named entity set; and a target test scheme is determined based on the historical test point set and the current test scheme. Through the above implementation, a historical test point set can be determined by extracting existing test schemes, and the historical test point set is used to reflect the test points of the test schemes of software similar to the current test software in history. Then, by comparing the historical test point set with the current test scheme, the test points that may be missed in the current test scheme can be determined, which makes it easy to supplement the test points missed in the current test scheme, thereby obtaining the target test scheme; the process of obtaining the target test scheme does not require large-scale human intervention, which makes it easy to reduce the cost of manual screening and comparison of test points and prevent the problem of missing test points.
[0150] In one embodiment, in extracting keywords from existing test solutions to obtain a keyword set, the keyword extraction module is specifically used to:
[0151] Performing word segmentation processing on the stock test scheme to obtain a target word list;
[0152] A keyword set is obtained based on the target word list and a preset word frequency threshold.
[0153] In one embodiment, in terms of obtaining a keyword set based on the target word list and a preset word frequency threshold, the keyword extraction module is specifically used to:
[0154] Constructing a word frequency matrix based on the target word list;
[0155] Calculating the word frequency value of each word in the word frequency matrix to obtain a word frequency value set;
[0156] A keyword set is obtained based on the word frequency value set and a preset word frequency threshold.
[0157] In one embodiment, in obtaining a historical test key point set based on the keyword set and the named entity set, the key point acquisition module is specifically used to:
[0158] Determine the keyword meaning of each keyword in the keyword set, and also determine the named entity meaning of each named entity in the named entity set;
[0159] The keywords and the named entities whose meanings are similar to each other are merged to obtain a historical test key point set.
[0160] In one embodiment, in determining the target test solution based on the similarity set, the preset similarity threshold and the historical test key point set, the solution determination module is specifically used to:
[0161] Extracting test points from the current test plan to obtain the current test point set;
[0162] Calculating the similarity between the current test key point in the current test key point set and each historical test key point in the historical test key point set to obtain a similarity set;
[0163] A target test plan is determined based on the similarity set, a preset similarity threshold, and the historical test key point set.
[0164] In one embodiment, in determining the target test solution based on the similarity set, the preset similarity threshold and the historical test key point set, the solution determination module is specifically used to:
[0165] In response to the similarity set not having the similarity less than the similarity threshold, taking the current test scheme as a target test scheme;
[0166] In response to the similarity set having the similarity less than the similarity threshold, displaying the test points to be added to the user; wherein the test points to be added are historical test points corresponding to the similarity less than the similarity threshold;
[0167] In response to obtaining an adding instruction issued by a user, determining the to-be-added test point corresponding to the adding instruction as a newly added test point, and adding the newly added test point to the current test plan to obtain a new current test plan;
[0168] The similarity between the current test points in the new current test point set and each of the historical test points in the historical test point set is calculated to obtain a new similarity set, until the new similarity set does not have the similarity less than the similarity threshold, and the new current test plan is used as the target test plan.
[0169] Embodiment 8
[0170] In this embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 3 As shown. The computer device includes a processor, a memory and a network interface connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a test scheme review method is implemented.
[0171] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the scheme of the present disclosure, and does not constitute a limitation on the computer device to which the scheme of the present disclosure is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.
[0172] Embodiment 9
[0173] In this embodiment, a computer readable storage medium is provided. Figure 4 As shown, a computer program is stored thereon, and when the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.
[0174] Embodiment 10
[0175] In this embodiment, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the steps in the above method embodiments are implemented.
[0176] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this disclosure are all information and data authorized by the user or fully authorized by all parties.
[0177] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided by the present disclosure can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided by the present disclosure may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited thereto. The processor involved in each embodiment provided by the present disclosure may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited thereto.
[0178] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.
[0179] The above-described embodiments only express several implementation methods of the present disclosure, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present invention. It should be pointed out that, for a person of ordinary skill in the art, several modifications and improvements can be made without departing from the concept of the present disclosure, and these all belong to the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure shall be subject to the attached claims.
Claims
1. A test plan review method, characterized in that: include: Extract keywords from the existing test solutions to obtain a keyword set; Identify the existing test schemes to obtain a named entity set; Obtaining a historical test key point set based on the keyword set and the named entity set; A target test plan is determined based on the historical test key point set and the current test plan.
2. The method according to claim 1, characterized in that The keyword set is obtained by extracting keywords from the existing test scheme, including: Performing word segmentation processing on the stock test scheme to obtain a target word list; A keyword set is obtained based on the target word list and a preset word frequency threshold.
3. The method according to claim 2, characterized in that The step of obtaining a keyword set based on the target word list and a preset word frequency threshold comprises: Constructing a word frequency matrix based on the target word list; Calculating the word frequency value of each word in the word frequency matrix to obtain a word frequency value set; A keyword set is obtained based on the word frequency value set and a preset word frequency threshold.
4. The method according to claim 1, characterized in that The obtaining of a historical test key point set based on the keyword set and the named entity set includes: Determine the keyword meaning of each keyword in the keyword set, and also determine the named entity meaning of each named entity in the named entity set; The keywords and the named entities whose meanings are similar to each other are merged to obtain a historical test key point set.
5. The method according to claim 1, characterized in that The determining of the target test scheme based on the historical test key point set and the current test scheme includes: Extracting test points from the current test plan to obtain the current test point set; Calculating the similarity between the current test key point in the current test key point set and each historical test key point in the historical test key point set to obtain a similarity set; A target test plan is determined based on the similarity set, a preset similarity threshold, and the historical test key point set.
6. The method according to claim 5, characterized in that The determining of the target test scheme based on the similarity set, the preset similarity threshold and the historical test key point set includes: In response to the similarity set not having the similarity less than the similarity threshold, taking the current test scheme as a target test scheme; In response to the similarity set having the similarity less than the similarity threshold, displaying the test points to be added to the user; wherein the test points to be added are historical test points corresponding to the similarity less than the similarity threshold; In response to obtaining an adding instruction issued by a user, determining the to-be-added test point corresponding to the adding instruction as a newly added test point, and adding the newly added test point to the current test plan to obtain a new current test plan; The similarity between the current test points in the new current test point set and each of the historical test points in the historical test point set is calculated to obtain a new similarity set, until the new similarity set does not have the similarity less than the similarity threshold, and the new current test plan is used as the target test plan.
7. A test plan review device, characterized in that: The device comprises: A keyword extraction module is used to extract keywords from existing test solutions to obtain a keyword set; An entity recognition module, used to identify the existing test scheme to obtain a named entity set; A key point acquisition module, used for obtaining a historical test key point set based on the keyword set and the named entity set; A scheme determination module is used to determine a target test scheme based on the historical test key point set and the current test scheme.
8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.
9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.
10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.