Method and system for generating test case based on demand document, medium and equipment

Abstract

The invention discloses a method, a system, a medium and equipment for generating a test case based on a demand document, and the method comprises the following steps: a document preprocessing step: preprocessing the demand document to obtain an initial data set; a test data set generation step: traversing the initial data set, and performing division processing on the initial data set to obtain a test data set; a test data set filtering step: filtering stop words in the test data set to obtain a new test data set; a demand relation tree construction step: traversing the new test data set, and generating a demand relation tree according to the logic relation; and a test case generation step: splitting the demand relation tree into a plurality of sub-trees only containing secondary nodes, and generating corresponding test cases by each sub-tree according to the test case data model.According to the method, natural language processing and large model analysis processing methods are utilized, the corresponding test cases are directly and automatically generated according to the demand document, and the test efficiency is improved. And the writing time of the test case is shortened.

Description

Technical Field

[0001] The present invention relates to the technical field of computer software testing, and in particular to a method, system, medium and device for generating test cases based on requirement documents. Background Art

[0002] In the current common software development process, after the software development is completed, the software needs to be tested to ensure that the product quality meets the expected requirements. The testing work of the software is usually carried out in the order of the written test cases. Therefore, the corresponding test cases should be written according to the demand prototype before testing the software. This requires the tester to accurately write each test case word by word according to the requirements given by the demand prototype. Although some testing tools or testing platforms can record the operations performed by the tester in sequence through operation recording, and then automatically generate the corresponding test case text, it also requires the tester to frequently perform the corresponding functional operations to assist in the generation of the case.

[0003] Therefore, the current method has the following defects:

[0004] 1. It takes a long time for testers to manually write test cases, which prolongs the overall test schedule.

[0005] 2. When the requirements change during the test case writing process and the corresponding test case execution conditions and expected results need to be modified, the process of finding and modifying the corresponding test case and its associated cases is cumbersome, which increases the workload of testers. Summary of the invention

[0006] The technical problem solved by the present invention is to provide a method for generating test cases based on requirement documents, which can directly generate test case documents according to requirements.

[0007] The technical solution adopted by the present invention to solve the technical problem is: a method for generating test cases based on a requirement document, the steps of which are:

[0008] Document preprocessing step: preprocess the requirement document to obtain the initial data set;

[0009] Test data set generation steps: traverse the initial data set, divide the initial data set, and obtain the test data set;

[0010] Test data set filtering step: filter the stop words in the test data set to obtain a new test data set;

[0011] Requirements relationship tree construction steps: traverse the new test data set and generate a requirements relationship tree based on logical relationships;

[0012] Test case generation step: split the requirement relationship tree into multiple subtrees containing only secondary nodes, and generate corresponding test cases for each subtree according to the test case data model;

[0013] Test case document generation steps: After integrating and summarizing the generated test cases, write them into an Excel format file in a loop to obtain the test case document.

[0014] Furthermore, in the document preprocessing step, specifically: text parsing and word segmentation processing are performed on the content of the imported demand document, and the preprocessed data is extracted and stored in the initial data set InitialData = [data1, data2, data3, ..., datan], where n is the length of InitialData, and datan is the last data after preprocessing.

[0015] Furthermore, in the test data set generation step, specifically:

[0016] Build a word list for storing special characters at the end of common sentences;

[0017] Traverse the data in the initial data set and compare it with the segmentation word list, and store the data in the test data set TestData_N again, and let N=1. When the data in the initial data set is the same as any data value in the segmentation word list, the data is not counted in the test data set TestData_N, and N=N+1;

[0018] Continue to traverse the data in the initial data set and store it in the next test data set TestData_N;

[0019] The current step is executed repeatedly until all data in the initial data set are traversed; the obtained test data sets are TestData_1, TestData_2, ..., TestData_N, where N is a natural number.

[0020] Furthermore, the test data set filtering step is specifically as follows:

[0021] Construct a stop word list to store invalid character content;

[0022] Each test data set is traversed in turn, and the data therein is compared with the content in the stop word list. The data in each test data set that is the same as the stop word list is removed to obtain new test data sets NewTestData_1, NewTestData_2, ..., NewTestData_N, where N is a natural number.

[0023] Furthermore, the step of constructing the demand relationship tree is specifically as follows:

[0024] Construct a relational vocabulary for storing characters expressing logical relations;

[0025] Traverse each new test data set in turn, and compare it with the data content in the relationship vocabulary, and use the data items in the data set as parent nodes to build the corresponding demand relationship tree; when the same data content is compared, mark the current data as a branch, and then determine whether the previous data content of the current data is the same as the data content of all existing nodes in the same level branch. If they are the same, merge the two and regard them as the same node, and then use the data after the current data in order as the child nodes of the relationship tree corresponding to the previous data of the current data;

[0026] This operation is performed cyclically until all new test data sets are completely traversed to obtain the corresponding requirement relationship tree.

[0027] Furthermore, the test case generation step is specifically as follows:

[0028] The demand relationship tree is split into subtrees containing only secondary nodes in order from bottom to top, and each subtree is numbered in the order obtained by splitting from bottom to top and from left to right;

[0029] The split subtree data is input into the test case data model, and the test case data model generates corresponding test cases according to the relationship words and node data in the subtree.

[0030] Furthermore, if the parent node data in the requirement relationship tree does not have corresponding child node data, a corresponding test case is generated according to a default test case template preset in the test case data model.

[0031] The present invention also discloses a system for generating test cases based on demand documents, including a document preprocessing module, a test data set generation module, a test data set filtering module, a demand relationship tree construction module, a test case generation module and a test case document generation module;

[0032] The document preprocessing module is used to preprocess the demand document to obtain an initial data set;

[0033] The test data set generation module is used to traverse the initial data set, divide the initial data set, and obtain the test data set;

[0034] The test data set filtering module is used to filter the stop words in the test data set to obtain a new test data set;

[0035] The requirement relationship tree construction module is used to traverse the new test data set and generate a requirement relationship tree according to the logical relationship;

[0036] The test case generation module is used to split the requirement relationship tree into multiple subtrees containing only secondary nodes, and each subtree generates a corresponding test case according to the test case data model;

[0037] The test case document generation module is used to integrate and summarize the generated test cases, and then write them into an Excel format file in a loop to obtain a test case document.

[0038] The present invention also discloses a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method for generating test cases based on a requirement document are implemented.

[0039] The present invention also discloses a computer device, comprising a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus; wherein:

[0040] The memory is used to store computer programs;

[0041] The processor is used to execute the steps of the method for generating test cases based on requirement documents by running the program stored in the memory.

[0042] The beneficial effect of the present invention is that the method utilizes natural language processing and large model analysis and processing methods to automatically generate corresponding test cases directly according to the requirement document, thereby reducing the time for writing test cases. At the same time, when the subsequent related functional requirements change, the test case update process is more convenient and quick, and the modification of the corresponding test case and its related cases can be realized without browsing the entire test case document for search. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 A flowchart of a method for generating test cases based on a requirement document according to an embodiment of the present application.

[0044] Figure 2 A schematic diagram of the framework of a system for generating test cases based on a requirement document according to an embodiment of the present application. DETAILED DESCRIPTION

[0045] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0046] like Figure 1 As shown, an embodiment of the present application discloses a method for generating test cases based on a requirement document, comprising the following steps:

[0047] Document preprocessing step: preprocess the requirement document to obtain the initial data set;

[0048] Test data set generation steps: traverse the initial data set, divide the initial data set, and obtain the test data set;

[0049] Test data set filtering step: filter the stop words in the test data set to obtain a new test data set;

[0050] Requirements relationship tree construction steps: traverse the new test data set and generate a requirements relationship tree based on logical relationships;

[0051] Test case generation step: split the requirement relationship tree into multiple subtrees containing only secondary nodes, and generate corresponding test cases for each subtree according to the test case data model;

[0052] Test case document generation steps: After integrating and summarizing the generated test cases, write them into an Excel format file in a loop to obtain the test case document.

[0053] This method utilizes natural language processing and large model analysis and processing methods to automatically generate corresponding test cases directly based on the requirement document, reducing the time for test case writing. At the same time, when subsequent related functional requirements change, the test case update process is more convenient and quick, and the corresponding test case and its related cases can be modified without browsing the entire test case document.

[0054] In this embodiment, the document preprocessing step is specifically as follows: text parsing and word segmentation processing are performed on the content of the imported demand document, and the preprocessed data is extracted and stored in the initial data set InitialData = [data1, data2, data3, ..., datan], where n is the length of InitialData, and datan is the last data after preprocessing.

[0055] Specifically, the above n can be 1, 2, 3, 4...; the above text analysis adopts a natural language processing method, which is an existing processing method, word segmentation and adopting a word segmentation library, such as jieba word segmentation.

[0056] In this step, the documents are preprocessed to facilitate the subsequent construction of the test data set.

[0057] In this embodiment, the test data set generation step specifically includes:

[0058] Build a word list for storing special characters at the end of common sentences;

[0059] Traverse the data in the initial dataset and compare it with the segmentation word list. Store the data back in the test dataset TestData_N, where N = 1. When the data in the initial dataset is the same as any data value in the segmentation word list, this data is not included in the test dataset TestData_N, and at the same time N = N + 1;

[0060] Continue to traverse the data in the initial dataset backward and store it in the next test dataset TestData_N;

[0061] Loop and execute the current step until all the data in the initial dataset is traversed; the obtained test datasets are TestData_1, TestData_2, …, TestData_N, where N is a natural number.

[0062] Specifically, the above segmentation word list is manually constructed. The segmentation word is a marker at the end of a sentence similar to a full stop. In the above steps, according to the segmentation word, the data in the initial dataset is cut into multiple pieces of data, and each piece of data is sequentially stored in TestData_1, TestData_2, …, TestData_N. Among them, the segmentation word is not stored, that is, N functional requirements are stored in this test dataset.

[0063] The above steps realize the separated storage of the functional requirements in the initial dataset, which is convenient for the subsequent establishment of the requirement relationship tree.

[0064] In this embodiment, the test dataset filtering step is specifically as follows:

[0065] Construct a stop word list for storing invalid character content;

[0066] Traverse each test dataset TestData_1, TestData_2, …, TestData_N in turn, and compare the data in them with the content in the stop word list respectively. Remove the data in each test dataset that is the same as the stop word list to obtain new test datasets NewTestData_1, NewTestData_2, …, NewTestData_N, where N is a natural number.

[0067] Specifically, the above stop word list is manually constructed. Stop words refer to certain words or characters that will be automatically filtered out before or after processing natural language data (or text) in information retrieval to save storage space and improve search efficiency, such as common words like "de" (of), "zai" (in), etc.

[0068] Through the above steps, the useless fields in the data can be removed, thereby improving the speed of subsequent data traversal and obtaining the test case document faster.

[0069] In this embodiment, the step of constructing the demand relationship tree is specifically as follows:

[0070] Construct a relational vocabulary for storing characters expressing logical relations;

[0071] Traverse each new test data set in turn, and compare it with the data content in the relationship vocabulary, and use the data items in the data set as parent nodes to build the corresponding demand relationship tree; when the same data content is compared, mark the current data as a branch, and then determine whether the previous data content of the current data is the same as the data content of all existing nodes in the same level branch. If they are the same, merge the two and regard them as the same node, and then use the data after the current data in order as the child nodes of the relationship tree corresponding to the previous data of the current data;

[0072] This operation is performed cyclically until all new test data sets are completely traversed to obtain the corresponding requirement relationship tree.

[0073] In the specific operation of the above method, the test data set NewTestData_1 is first traversed to obtain the corresponding requirement relationship tree. After the test data set NewTestData_1 is completely traversed, the test data sets NewTestData_2, NewTestData_3, ..., NewTestData_N are processed according to the same operation to obtain the corresponding requirement relationship tree.

[0074] In this embodiment, the test case generation step is specifically as follows:

[0075] The demand relationship tree is split into subtrees containing only secondary nodes in order from bottom to top, and each subtree is numbered in the order obtained by splitting from bottom to top and from left to right;

[0076] The split subtree data is input into the test case data model, and the test case data model generates corresponding test cases according to the relationship words and node data in the subtree.

[0077] Specifically, the above-mentioned test case data model is trained and optimized using a specific test case set to match the corresponding reference data according to the key field information, and fit the key field information and the reference data to generate new test case data. After the split subtree data is input into the test case data model, the test case data model retrieves the corresponding test case template according to the relationship words marked in the requirement relationship tree, and then uses the parent node as the title source data, and the child nodes constitute the steps and expected result source data in sequence. The test case data model automatically generates the corresponding test case according to the corresponding test case template and source data.

[0078] This method can realize the automatic generation of test cases, thereby reducing the time of test case writing. At the same time, the above method splits the requirement relationship tree into subtrees of secondary nodes, so that each generated test case and the judgment content it contains are a one-to-many relationship, so that the generated test case results can be clear, avoiding ambiguity, and also making the results more detailed.

[0079] In this embodiment, if there is no corresponding child node data for the parent node data in the requirement relationship tree, a corresponding test case is generated according to a default test case template preset in the test case data model.

[0080] That is, in this method, even if there is only one node data in the requirement relationship tree, the corresponding test case can be generated according to the preset default test case template, that is, there will be no empty test case.

[0081] The present invention also discloses a system for generating test cases based on demand documents, including a document preprocessing module, a test data set generation module, a test data set filtering module, a demand relationship tree construction module, a test case generation module and a test case document generation module;

[0082] The document preprocessing module is used to preprocess the demand document to obtain an initial data set;

[0083] The test data set generation module is used to traverse the initial data set, divide the initial data set, and obtain the test data set;

[0084] The test data set filtering module is used to filter the stop words in the test data set to obtain a new test data set;

[0085] The requirement relationship tree construction module is used to traverse the new test data set and generate a requirement relationship tree according to the logical relationship;

[0086] The test case generation module is used to split the requirement relationship tree into multiple subtrees containing only secondary nodes, and each subtree generates a corresponding test case according to the test case data model;

[0087] The test case document generation module is used to integrate and summarize the generated test cases, and then write them into an Excel format file in a loop to obtain a test case document.

[0088] Specifically, the document preprocessing module is used to perform text parsing and word segmentation on the content of the imported demand document, extract the preprocessed data and store it in the initial data set InitialData = [data1, data2, data3, ..., datan], where n is the length of InitialData and datan is the last data after preprocessing.

[0089] Specifically, the test data set generation module is used to construct a segmentation word list for storing special characters at the end of common sentences;

[0090] Then, the data in the initial data set is traversed and compared with the segmentation word list, and the data is stored in the test data set TestData_N again, and N=1. When the data in the initial data set is the same as any data value in the segmentation word list, the data is not counted in the test data set TestData_N, and N=N+1;

[0091] Continue to traverse the data in the initial data set and store it in the next test data set TestData_N;

[0092] The current step is executed repeatedly until all data in the initial data set are traversed; the obtained test data sets are TestData_1, TestData_2, ..., TestData_N, where N is a natural number.

[0093] Specifically, the test data set filtering module is used to construct a stop word list for storing invalid character content;

[0094] Then, each test data set is traversed in turn, and the data therein is compared with the content in the stop word list, and the data in each test data set that is the same as the stop word list is removed to obtain new test data sets NewTestData_1, NewTestData_2, ..., NewTestData_N, where N is a natural number.

[0095] Specifically, the demand relationship tree construction module is used to construct a relationship word list for storing characters expressing logical relationships;

[0096] Then, each new test data set is traversed in turn, and the data content is compared with the data content in the relational vocabulary, and the data items in the data set are used as parent nodes to build the corresponding demand relationship tree; when the same data content is compared, the current data is marked as a branch, and then it is determined whether the previous data content of the current data is the same as the data content of all existing nodes in the same level branch. If they are the same, the two are merged and regarded as the same node, and then the data after the current data is used in sequence as the child nodes of the relationship tree corresponding to the previous data of the current data;

[0097] This operation is performed cyclically until all new test data sets are completely traversed to obtain the corresponding requirement relationship tree.

[0098] Specifically, the test case generation module is used to split the requirement relationship tree into subtrees containing only secondary nodes in order from bottom to top, and add numbers to each subtree in the order of splitting from bottom to top and from left to right;

[0099] Then the split subtree data is input into the test case data model, and the test case data model generates corresponding test cases according to the relationship words and node data in the subtree.

[0100] The present invention also discloses a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method for generating test cases based on a requirement document are implemented.

[0101] In addition, the computer-readable storage medium of this embodiment may adopt any combination of one or more computer-readable storage media, wherein the computer-readable storage medium includes electrical, optical, electromagnetic, infrared or semiconductor systems, devices or components, or any combination thereof.

[0102] The present invention also discloses a computer device, comprising a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus; wherein:

[0103] The memory is used to store computer programs;

[0104] The processor is used to execute the steps of the method for generating test cases based on requirement documents by running the program stored in the memory.

[0105] As an embodiment of the present invention, the communication bus mentioned in the above terminal can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The communication bus can be divided into an address bus, a data bus, a control bus, etc.

[0106] As an implementation mode of the present invention, the communication interface is used for communication between the above-mentioned terminal and other devices.

[0107] As an embodiment of the present invention, the memory may include a random access memory (RAM) or a non-volatile memory, such as at least one disk memory. Optionally, the memory may also be at least one storage device located away from the aforementioned processor.

[0108] As an embodiment of the present invention, the above-mentioned processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components.

[0109] Different from the existing technology, this method uses natural language processing and large model analysis and processing methods to automatically generate corresponding test cases directly according to the requirement document, reducing the time of test case writing. At the same time, when the relevant functional requirements change in the future, the test case update process is more convenient and quick. There is no need to browse the entire test case document to search for the corresponding test case and its related cases.

[0110] The specific embodiments described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for generating test cases based on a requirement document, characterized in that: Document preprocessing step: preprocess the requirement document to obtain the initial data set; Test data set generation steps: traverse the initial data set, divide the initial data set, and obtain the test data set; Test data set filtering step: filter the stop words in the test data set to obtain a new test data set; Requirements relationship tree construction steps: traverse the new test data set and generate a requirements relationship tree based on logical relationships; Test case generation step: split the requirement relationship tree into multiple subtrees containing only secondary nodes, and generate corresponding test cases for each subtree according to the test case data model; Test case document generation steps: After integrating and summarizing the generated test cases, write them into an Excel format file in a loop to obtain the test case document.

2. The method for generating test cases based on a requirement document as claimed in claim 1, characterized in that: The document preprocessing step specifically includes: performing text parsing and word segmentation on the content of the imported demand document, extracting the preprocessed data and storing it in the initial data set InitialData = [data1, data2, data3, ..., datan], where n is the length of InitialData, and datan is the last data after preprocessing.

3. The method for generating test cases based on a requirement document as claimed in claim 1, characterized in that: The test data set generation step is specifically as follows: Build a word list for storing special characters at the end of common sentences; Traverse the data in the initial data set and compare it with the segmentation word list, and store the data in the test data set TestData_N again, and let N=1. When the data in the initial data set is the same as any data value in the segmentation word list, the data is not counted in the test data set TestData_N, and N=N+1; Continue to traverse the data in the initial data set and store it in the next test data set TestData_N; The current step is executed repeatedly until all data in the initial data set are traversed; the obtained test data sets are TestData_1, TestData_2, ..., TestData_N, where N is a natural number.

4. The method for generating test cases based on a requirement document according to claim 1, characterized in that: The test data set filtering step is specifically as follows: Construct a stop word list to store invalid character content; Each test data set is traversed in turn, and the data therein is compared with the content in the stop word list. The data in each test data set that is the same as the stop word list is removed to obtain new test data sets NewTestData_1, NewTestData_2, ..., NewTestData_N, where N is a natural number.

5. The method for generating test cases based on a requirement document according to claim 1, characterized in that: The steps of constructing the demand relationship tree are specifically as follows: Construct a relational vocabulary for storing characters expressing logical relations; Traverse each new test data set in turn, and compare it with the data content in the relationship vocabulary, and use the data items in the data set as parent nodes to build the corresponding demand relationship tree; when the same data content is compared, mark the current data as a branch, and then determine whether the previous data content of the current data is the same as the data content of all existing nodes in the same level branch. If they are the same, merge the two and regard them as the same node, and then use the data after the current data in order as the child nodes of the relationship tree corresponding to the previous data of the current data; This operation is performed cyclically until all new test data sets are completely traversed to obtain the corresponding requirement relationship tree.

6. The method for generating test cases based on a requirement document according to claim 1, characterized in that: The test case generation steps are specifically as follows: The demand relationship tree is split into subtrees containing only secondary nodes in order from bottom to top, and each subtree is numbered in the order obtained by splitting from bottom to top and from left to right; The split subtree data is input into the test case data model, and the test case data model generates corresponding test cases according to the relationship words and node data in the subtree.

7. The method for generating test cases based on requirement documents according to claim 6, characterized in that: If there is no corresponding child node data for the parent node data in the requirement relationship tree, the corresponding test case is generated according to the default test case template preset in the test case data model.

8. A system for generating test cases based on requirement documents, characterized in that: It includes a document preprocessing module, a test data set generation module, a test data set filtering module, a requirement relationship tree construction module, a test case generation module and a test case document generation module; The document preprocessing module is used to preprocess the demand document to obtain an initial data set; The test data set generation module is used to traverse the initial data set, divide the initial data set, and obtain the test data set; The test data set filtering module is used to filter the stop words in the test data set to obtain a new test data set; The requirement relationship tree construction module is used to traverse the new test data set and generate a requirement relationship tree according to the logical relationship; The test case generation module is used to split the requirement relationship tree into multiple subtrees containing only secondary nodes, and each subtree generates a corresponding test case according to the test case data model; The test case document generation module is used to integrate and summarize the generated test cases, and then write them into an Excel format file in a loop to obtain a test case document.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method for generating test cases based on a requirement document according to any one of claims 1 to 7 are implemented.

10. A computer device, characterized in that: It includes a processor, a communication interface, a memory and a communication bus, wherein: The processor, the communication interface, and the memory communicate with each other via the communication bus; wherein: The memory is used to store computer programs; The processor is used to execute the steps of the method for generating test cases based on a requirement document according to any one of claims 1 to 7 by running the program stored in the memory.

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