CN113886241A - Brain graph use case set generation method and device - Google Patents
Brain graph use case set generation method and device Download PDFInfo
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- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/36—Preventing errors by testing or debugging software
- G06F11/3668—Software testing
- G06F11/3672—Test management
- G06F11/3684—Test management for test design, e.g. generating new test cases
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- G06F11/36—Preventing errors by testing or debugging software
- G06F11/3668—Software testing
- G06F11/3672—Test management
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Abstract
The invention relates to the field of computer program testing, in particular to a method and a device for generating a brain graph case set. The invention provides a brain graph use case set generation method, which comprises the following steps: step S1, creating a brain graph use case set template according to the test requirement; step S2, creating a brain graph use case set according to the brain graph use case set template; s3, selecting existing brain graph nodes, copying and pasting original case information of the selected existing brain graph nodes to new brain graph nodes, and modifying the identity codes and node names of primary nodes in the new brain graph nodes; step S4, editing the new brain graph nodes; and S5, repeating the steps S2-S4 until the brain graph use case set is edited, saving the brain graph use case set, and automatically generating the list use case. According to the invention, through the new brain graph catalog and the quick pasting and copying of the case nodes, only the first-level nodes are processed, the front-end performance is optimized, and the user experience when accessing and editing the brain graph is improved.
Description
Technical Field
The invention relates to the field of computer program testing, in particular to a method and a device for generating a brain graph case set.
Background
In a traditional test working environment, a tester usually compiles test cases through excel or other forms, and then creates different catalogs for case management aiming at the test cases of different modules.
However, with the rapid development of the internet, the traditional test case editing mode gradually shows defects, such as low editing efficiency, easy error, large consumption of human resources and the like, so that the test requirements cannot be met.
In the prior art, a brain graph tool is used for editing test cases by various companies so as to realize flexible and free test case editing effect which is more suitable for an internet agile mode of quick iteration. However, the current test case editing tools in brain graph form still have limitations.
The brain map is a visualization means capable of expressing the association between things, and the brain map is well suited for showing the relationship between test function hierarchies, and various brain map editing tools such as xmind or Baidu brain maps are provided in the prior art.
The brain graph has wide application, for example, the test case can be displayed in the form of the brain graph, and the creation is simple and efficient. There are also test cases edited using brain graph format.
Chinese invention patent CN 201911124330.0 discloses a brain graph generation method, apparatus and computer readable storage medium. Wherein, the method comprises the following steps: when a brain graph file with a preset data format is operated by a code editor, acquiring source data of the brain graph file; analyzing node information of a brain graph corresponding to the source data according to the source data; and drawing a brain graph corresponding to the source data in the new page according to the node information.
Chinese invention patent CN 202010100561.4 discloses a brain graph-based test method, apparatus, electronic device and storage medium. The method comprises the following steps: receiving a test result adding request corresponding to a target test case; the test result adding request comprises a test result, test personnel information and system time, the test result is generated by testing a system to be tested by using a test case in a brain graph by the test personnel and adding the test result to the brain graph, the brain graph is of a tree structure comprising at least one test case, and each test case object comprises a plurality of nodes; and inserting the test result, the tester information and the system time into brain picture object data corresponding to the target test case.
The invention patent generates the brain graph based on the existing data, and can not realize the operation of editing the brain graph on line.
Disclosure of Invention
The invention aims to provide a brain graph use case set generation method and device, and solves the problem that the existing brain graph use case set cannot be edited on line.
In order to achieve the above object, the present invention provides a brain map corpus generating method, including the steps of:
step S1, creating a brain graph use case set template according to the test requirement;
step S2, creating a brain graph use case set according to the brain graph use case set template;
s3, selecting existing brain graph nodes, copying and pasting original case information of the selected existing brain graph nodes to new brain graph nodes, and modifying the identity codes and node names of primary nodes in the new brain graph nodes;
step S4, editing the new brain graph nodes;
and S5, repeating the steps S2-S4 until the brain graph use case set is edited, saving the brain graph use case set, and automatically generating the list use case.
In one embodiment, the test requirements in step S1 include test case type, test preconditions, test remarks, test steps, test results, test priorities, and test review status.
In one embodiment, the brain graph nodes comprise a directory node and a test case related node;
the test case related nodes comprise a test case node, a remark node, a precondition node, a test step node and an expected result node;
the test case nodes are child nodes of the directory nodes, and the other nodes are child nodes of the test case nodes.
In an embodiment, in the step S3, the modifying the identity identifier of the primary node in the new brain graph node further includes:
and modifying the identity identification code of the primary node in the new brain graph node into a newly generated universal unique identification code.
In an embodiment, in the step S3, modifying the node name of the primary node in the new brain graph node, further includes:
and adding an identifiable identifier on the basis of the node name of the selected existing brain graph node to generate the node name of the first-level node in the new brain graph node.
In an embodiment, in the step S3, the existing brain graph nodes are single or multiple directory nodes and corresponding child nodes:
if the directory node is a single directory node, the identity mark code of the new directory node is directly modified into a newly generated universal unique identification code, and a suffix identifier is added on the basis of the node name of the selected existing directory node to generate the node name of the new directory node;
and if the directory nodes are a plurality of directory nodes in the same hierarchy, circularly traversing the primary directory nodes, respectively generating universal unique identification codes of the primary directory nodes, assigning the universal unique identification codes to the identity identification code attributes corresponding to the new primary directory nodes, respectively adding suffix identification on the basis of the node names of the selected existing directory nodes, and generating the node names corresponding to the new primary directory nodes.
In an embodiment, in the step S3, the selected existing brain graph nodes are single or multiple test case nodes and corresponding child nodes:
if the test case node is a single test case node, directly modifying the identity mark code of the new test case node into a newly generated universal unique identification code, and adding a suffix mark on the basis of the node name of the selected existing test case node to generate the node name of the new test case node;
and if the test case nodes are a plurality of test case nodes of the same level, circularly traversing the primary test case nodes, respectively generating the universal unique identification codes of the primary test case nodes, assigning the universal unique identification codes to the identity identification code attributes corresponding to the new primary test case nodes, respectively adding suffix identification on the basis of the node names of the selected existing test cases, and generating the node names corresponding to the new primary test case nodes.
In an embodiment, in the step S3, the selected existing brain graph nodes are a plurality of directory nodes and test case nodes:
and circularly traversing the primary directory node and the primary test case node, respectively generating universal unique identification codes of the primary directory node and the primary test case node, assigning the universal unique identification codes to the identity identification code attributes of the corresponding new primary directory node or the primary test case node, respectively adding suffix identification on the basis of the node name of the selected primary directory node or the primary test case node, and generating the node name of the corresponding new primary directory node or the primary test case node.
In one embodiment, the edited content of the brain map node in step S4 includes a directory name, a test case name, an individual test step, and an expected result.
In order to achieve the above object, the present invention provides an electroencephalogram corpus generating apparatus, including:
a memory for storing instructions executable by the processor;
a processor for executing the instructions to implement the method of any one of the above.
To achieve the above object, the present invention provides a computer readable medium having stored thereon computer instructions, wherein the computer instructions, when executed by a processor, perform the method as described in any one of the above.
According to the brain graph use case set generation method and device provided by the invention, the new brain graph catalog and the use case nodes are quickly pasted and copied, only the first-level nodes are processed, the performance of the front end is optimized, the user experience when the brain graph is accessed for editing is improved, and meanwhile, after the brain graph use case is quickly compiled, the catalog and list use cases of the traditional test management mode can be accurately and automatically generated.
Drawings
The above and other features, properties and advantages of the present invention will become more apparent from the following description of the embodiments with reference to the accompanying drawings in which like reference numerals denote like features throughout the several views, wherein:
FIG. 1 discloses a flowchart of a brain diagram corpus generating method according to an embodiment of the invention;
FIG. 2 discloses a workflow diagram for brain diagram set online editing according to an embodiment of the invention;
FIG. 3 discloses a workflow diagram of a copy-and-paste node according to an embodiment of the invention;
FIG. 4 discloses a schematic diagram of three replication type nodes according to an embodiment of the invention;
FIG. 5 is a diagram illustrating paste effects of three copy type nodes according to an embodiment of the invention;
FIG. 6 discloses a diagram of a list use case according to an embodiment of the invention;
fig. 7 discloses a schematic diagram of an apparatus for generating an electroencephalogram corpus according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The invention provides a brain graph case generation method and device, which support quick online creation and editing of a test case set. The method is applied to a testing device, and the testing device can be intelligent electronic equipment such as a desktop computer, a tablet computer, a notebook computer, an intelligent mobile phone and intelligent wearable equipment.
Fig. 1 discloses a flowchart of a brain diagram use case set generation method according to an embodiment of the present invention, and the brain diagram use case set generation method shown in fig. 1 includes the following steps:
step S1, creating a brain graph use case set template according to the test requirement;
step S2, creating a brain graph use case set according to the brain graph use case set template;
s3, selecting the existing brain graph nodes, copying cases, and pasting the cases to new nodes;
step S4, modifying the new pasted node;
and S5, repeating the steps S2-S4 until the brain graph use case set is edited, saving the brain graph use case set, and automatically generating the list use case.
While, for purposes of simplicity of explanation, the methodologies are shown and described as a series of acts, it is to be understood and appreciated that the methodologies are not limited by the order of acts, as some acts may, in accordance with one or more embodiments, occur in different orders and/or concurrently with other acts from that shown and described herein or not shown and described herein, as would be understood by one skilled in the art.
Fig. 2 discloses a workflow diagram of brain diagram use case set online editing according to an embodiment of the present invention, and the workflow diagram of brain diagram use case set online editing shown in fig. 2 adopts the brain diagram use case set generation method shown in fig. 1, and specifically includes:
defining a brain graph use case set template according to test requirements;
the test requirements comprise test case types, test preconditions, test remarks, test steps, test results, test priorities and test review states.
The user adds a case brain graph on a front page of the platform, and the platform automatically loads a brain graph case template and displays an example brain graph case so as to support the user to carry out quick operation on the basis of the existing template brain graph case.
The user can copy (ctrl-c) a plurality of existing brain graph nodes, such as directory nodes and test case nodes, and paste (ctrl-v) the original case information content of the related nodes to the new brain graph node.
Quickly editing and storing the pasted new brain graph nodes;
the editing content mainly includes directory node name, test case node name, individual test steps, expected results and the like.
And automatically generating the list use case when the brain graph use case set is saved.
Compared with the traditional test case creating mode for respectively creating child nodes of the brain graph in the prior art, the brain graph case set generating method provided by the invention has the advantages that the test cases are created more quickly by quickly copying multi-node paste and modifying modes, and the operation of editing the brain graph cases on line is more intuitive.
However, in the prior art, when a list case is automatically created, the back end distinguishes different cases by the ID attribute of the brain graph node, and when the brain graph directory node is copied or the test case node information is pasted again, the case information of the copied brain graph node is pasted to a new brain graph node without being changed, which may cause that background data may not distinguish two brain graph nodes, which is the original existing brain graph node and which is the pasted and copied new brain graph node.
The invention provides a brain graph case set generation method, which can accurately distinguish directory nodes and test case nodes when brain graph nodes are copied and pasted.
The brain map nodes in the brain map case set of this embodiment are classified into two types, one type is a directory node, and the other type is a test case related node.
The test case related nodes comprise test case nodes, remark nodes, precondition nodes, test step nodes and expected result nodes.
The test case nodes are child nodes of the directory nodes, and the other nodes are child nodes of the test case nodes.
The case information data of the directory node and the test case node contain ID field attributes as unique constraints for distinguishing.
In the process of copying and pasting the directory node and the test case node, a new universal unique identifier UUID value is regenerated to serve as an identity identifier ID of a new brain graph node (the directory node and the test case related node) after pasting, so that the uniqueness of the node is marked.
Each time a new brain graph node is pasted, a plurality of deeper nodes may be available, and if the ID of the use case information of each node is processed, the performance of the front end may be affected, which may result in an unfriendly experience for the user.
Fig. 3 discloses a work flow diagram of a copy-paste node according to an embodiment of the present invention, and the work flow of the copy-paste node shown in fig. 3 ensures that the brain graph nodes required by the present invention can be copied and pasted in batch.
The invention provides a brain graph use case set generation method, which marks the uniqueness of a brain graph use case through a form of combining a directory node and a test case node when copying and pasting the brain graph node, namely, the same test case can exist under different brain graph directories.
In step S3, the modifying the id code of the primary node in the new brain graph node further includes:
and modifying the identity identification code of the primary node in the new brain graph node into a newly generated universal unique identification code.
In step S3, modifying the node name of the primary node in the new brain graph node, further includes:
and adding an identifiable identifier on the basis of the node name of the selected existing brain graph node to generate the node name of the first-level node in the new brain graph node.
Wherein the recognizable identifier may be a prefix identifier or a suffix identifier.
According to the copy-paste operation brain graph node, when a new unique identifier UUID is generated, only the identity identifier ID and the node name of a first-level node in the new brain graph node are processed, so that exponential-level node data change is avoided.
The brain graph first-level node refers to a brain graph node without a father node, and if the brain graph is in a left-to-right display form, the leftmost node is the first-level node.
Therefore, in the workflow of copying and pasting nodes shown in fig. 3, after copying nodes of the brain graph, the front js operation string data is converted into json objects. JavaScript Object Notation (JSON) is a lightweight data exchange language conceived and designed by douglas-crookford that is based on easily readable text to transmit data objects consisting of attribute values or sequential values.
Then, corresponding pasting processing is performed according to 3 different copy types.
Fig. 4 is a schematic diagram illustrating three replication type nodes according to an embodiment of the present invention, and as shown in fig. 4, when replicating a brain graph node, the replication type node can be subdivided into 3 replication types.
In the first replication type node shown in fig. 4, the existing brain graph nodes are single or multiple directory nodes and corresponding child nodes.
If the directory node is a single directory node, the identity mark code of the new directory node is directly modified into a newly generated universal unique identification code, and a suffix identifier is added on the basis of the node name of the selected existing directory node to generate the node name of the new directory node;
in the embodiment shown in fig. 4, the id of the original brain graph node is directly modified by a new uuid, a new directory node after copying and pasting is generated, and the directory node name of the new directory node is added with a [ copy ] mark on the basis of the name of the original directory node;
and if the directory nodes are a plurality of directory nodes in the same hierarchy, circularly traversing the primary directory nodes, respectively generating a universal unique identifier UUID of the primary directory node, assigning the UUID to an identity identifier id attribute corresponding to a new primary directory node, respectively adding suffix identifiers on the basis of the node names of the selected existing directory nodes, and generating the node names corresponding to the new primary directory nodes.
In the embodiment shown in fig. 4, the first-level directory nodes are traversed in a circulating manner, new UUIDs are generated and assigned to the id attributes of the directory nodes to be pasted, the directory names are modified, the directory node names of the new directory nodes are marked by adding suffixes (copy) on the basis of the original directory node names, and the ids and the names of the nodes in a deeper level below the first-level directory nodes are kept unchanged.
As shown in fig. 4, for the second type of replication node, the existing brain graph nodes are single or multiple test case nodes and corresponding child nodes, and the processing method is the same as that of the directory node.
If the test case node is a single test case node, directly modifying the identity mark code of the new test case node into a newly generated universal unique identification code, and adding a suffix mark on the basis of the node name of the selected existing test case node to generate the node name of the new test case node;
and if the test case nodes are a plurality of test case nodes of the same level, circularly traversing the primary test case nodes, respectively generating the universal unique identification codes of the primary test case nodes, assigning the universal unique identification codes to the identity identification code attributes corresponding to the new primary test case nodes, respectively adding suffix identification on the basis of the node names of the selected existing test cases, and generating the node names corresponding to the new primary test case nodes.
As shown in fig. 4, for the third replication type node, the existing brain graph nodes are a plurality of directory nodes, test case nodes and corresponding child nodes, and the processing mode is the same as that of the directory nodes.
And circularly traversing the primary directory node and the primary test case node, respectively generating universal unique identification codes of the primary directory node and the primary test case node, assigning the universal unique identification codes to the identity identification code attributes of the corresponding new primary directory node or the primary test case node, respectively adding suffix identification on the basis of the node name of the selected primary directory node or the primary test case node, and generating the node name of the corresponding new primary directory node or the primary test case node.
Next, a change of use case information data before and after copy-paste will be described by taking the first copy type node shown in fig. 4 as an example.
The node data is stored in the form of a Json array, each node is a Json object, the object is formed by comma-divided members enclosed by curly brackets, the members are character string keys, and the values described above are formed by comma-divided key-value pairs.
Each node contains two attributes, data and children.
The data stores data of itself, such as directory node containing id (uniquely identifying the node), created (creation time) and text (node name), test case node containing background (background color), id (uniquely identifying the node), priority (case priority), resource (resource node) and text (test case name).
children stores data of child nodes.
The original use case information of the existing brain graph nodes before copying is as follows:
[ { "data": 98d1aa70-615e-4b4b-9eb3-241261d8b32c "," created ": 1628059538478", "text": test case catalog 1"}," child "[ {" data ": {" id ": 7d09c35e-a0ec-44f6-83d6-d69aa1170807", "created":1628063665537 "," text ": test case catalog 3" }, "child" and [ ] } ]
[ { "data": a0e4d960-65d4-4442-adf9-45f2297c723 a', "created":1628059549994, "text": test case catalog 2"}," child ": [ ] } ]
The information of the new brain graph nodes after copying and pasting is as follows:
[ { "data": 35ff0ac3-94d3-4361-8939-cb3d938a3d 52-, "created":1628059538478, "text": test case catalog 1 [ copy "}," child "[ {" data ": {" id ": 7d09c35e-a0ec-44f6-83d6-d69aa1170807 ]," created ":1628063665537," text ": test case catalog 3" }, "child": [ ] } ]
[ { "data": 50a46bf6-ce1c-4ed0-88b6-550a5f09b78b "," created ": 1628059549994", "text": test case catalog 2 [ copy ] "}," children ": [ ] } ])
In the first replication type shown in fig. 4, the brain graph nodes "test case directory 1" and "test case directory 2" are both primary nodes, the id attribute of the identity code is UUID, the new brain graph node id after pasting is a regenerated UUID, the name is a text attribute value, and a suffix [ replication ] is uniformly added.
For the test case directory 1 containing child nodes 3, the id and name of the primary node in the node non-duplicated node remain unchanged.
Fig. 5 is a schematic diagram illustrating paste effects of three copy-type nodes according to an embodiment of the present invention, and as shown in fig. 5, the brain graph node effects of the three copy-type nodes shown in fig. 4 after being copied and pasted to a target use case directory node are shown, and use case information contents of the copied brain graph node can be used as child nodes of the new brain graph node (target use case directory node). And storing the brain picture use case set, and automatically generating corresponding directory and list use cases by the platform.
Fig. 6 is a schematic diagram of list use cases according to an embodiment of the present invention, and as shown in fig. 6, after the copy and paste processes of fig. 4 and fig. 5, 4 (3 main directories, 1 sub-directory) and 4 list use cases are automatically added.
Fig. 7 discloses a schematic diagram of an apparatus for generating an electroencephalogram corpus according to an embodiment of the present invention. The brain map corpus generating device may include an internal communication bus 701, a processor (processor)702, a Read Only Memory (ROM)703, a Random Access Memory (RAM)704, a communication port 705, and a hard disk 707. The internal communication bus 701 may enable data communication between components of the brain graph set generation apparatus. The processor 702 may make the determination and issue the prompt. In some embodiments, the processor 702 may be comprised of one or more processors.
The communication port 705 can realize data transmission and communication between the electroencephalogram collection generating apparatus and an external input/output device. In some embodiments, the brain graph corpus generating device may send and receive information and data from a network through the communication port 705. In some embodiments, the brain graph corpus generating apparatus may transmit and communicate data between the external input/output devices in a wired manner through the input/output terminal 706.
The brain diagram generating apparatus may also include various forms of program storage units and data storage units, such as a hard disk 707, Read Only Memory (ROM)703 and Random Access Memory (RAM)704, capable of storing various data files used for computer processing and/or communications, and possibly program instructions executed by the processor 702. The processor 702 executes these instructions to implement the main parts of the method. The results of the processing by the processor 702 are communicated to an external output device via the communication port 705 for display on a user interface of the output device.
For example, the implementation process file of the above-mentioned electroencephalogram generation method can be a computer program, stored in the hard disk 707, and can be recorded in the processor 702 to be executed, so as to implement the method of the present application.
When the implementation process file of the electroencephalogram generation method is a computer program, the implementation process file may be stored in a computer-readable storage medium as a product. For example, computer-readable storage media can include but are not limited to magnetic storage devices (e.g., hard disk, floppy disk, magnetic strips), optical disks (e.g., Compact Disk (CD), Digital Versatile Disk (DVD)), smart cards, and flash memory devices (e.g., electrically Erasable Programmable Read Only Memory (EPROM), card, stick, key drive). In addition, various storage media described herein can represent one or more devices and/or other machine-readable media for storing information. The term "machine-readable medium" can include, without being limited to, wireless channels and various other media (and/or storage media) capable of storing, containing, and/or carrying code and/or instructions and/or data.
According to the brain graph use case set generation method and device provided by the invention, the new brain graph catalog and the use case nodes are quickly pasted and copied, only the first-level nodes are processed, the performance of the front end is optimized, the user experience when the brain graph is accessed for editing is improved, and meanwhile, after the brain graph use case is quickly compiled, the catalog and list use cases of the traditional test management mode can be accurately and automatically generated.
As used in this application and the appended claims, the terms "a," "an," "the," and/or "the" are not intended to be inclusive in the singular, but rather are intended to be inclusive in the plural unless the context clearly dictates otherwise. In general, the terms "comprises" and "comprising" merely indicate that steps and elements are included which are explicitly identified, that the steps and elements do not form an exclusive list, and that a method or apparatus may include other steps or elements.
Those of skill in the art would understand that information, signals, and data may be represented using any of a variety of different technologies and techniques. For example, data, instructions, commands, information, signals, bits (bits), symbols, and chips that may be referenced throughout the above description may be represented by voltages, currents, electromagnetic waves, magnetic fields or particles, optical fields or particles, or any combination thereof.
Those of skill would further appreciate that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The embodiments described above are provided to enable persons skilled in the art to make or use the invention and that modifications or variations can be made to the embodiments described above by persons skilled in the art without departing from the inventive concept of the present invention, so that the scope of protection of the present invention is not limited by the embodiments described above but should be accorded the widest scope consistent with the innovative features set forth in the claims.
Claims (11)
1. A brain graph use set generation method is characterized by comprising the following steps:
step S1, creating a brain graph use case set template according to the test requirement;
step S2, creating a brain graph use case set according to the brain graph use case set template;
s3, selecting existing brain graph nodes, copying and pasting original case information of the selected existing brain graph nodes to new brain graph nodes, and modifying the identity codes and node names of primary nodes in the new brain graph nodes;
step S4, editing the new brain graph nodes;
and S5, repeating the steps S2-S4 until the brain graph use case set is edited, saving the brain graph use case set, and automatically generating the list use case.
2. The brain graph set generating method according to claim 1, wherein the test requirements in the step S1 include test case type, test preconditions, test remarks, test steps, test results, test priorities, and test review status.
3. The brain graph set generation method according to claim 1, wherein the brain graph nodes include a directory node and a test case related node;
the test case related nodes comprise a test case node, a remark node, a precondition node, a test step node and an expected result node;
the test case nodes are child nodes of the directory nodes, and the other nodes are child nodes of the test case nodes.
4. The brain graph use set generating method according to claim 1, wherein in step S3, modifying the identity codes of the primary nodes in the new brain graph node further includes:
and modifying the identity identification code of the primary node in the new brain graph node into a newly generated universal unique identification code.
5. The brain graph use set generating method according to claim 1, wherein in the step S3, modifying the node name of the primary node in the new brain graph node further includes:
and adding an identifiable identifier on the basis of the node name of the selected existing brain graph node to generate the node name of the first-level node in the new brain graph node.
6. The brain graph use case set generating method according to claim 3, wherein in the step S3, the existing brain graph nodes are single or multiple directory nodes and corresponding child nodes:
if the directory node is a single directory node, the identity mark code of the new directory node is directly modified into a newly generated universal unique identification code, and a suffix identifier is added on the basis of the node name of the selected existing directory node to generate the node name of the new directory node;
and if the directory nodes are a plurality of directory nodes in the same hierarchy, circularly traversing the primary directory nodes, respectively generating universal unique identification codes of the primary directory nodes, assigning the universal unique identification codes to the identity identification code attributes corresponding to the new primary directory nodes, respectively adding suffix identification on the basis of the node names of the selected existing directory nodes, and generating the node names corresponding to the new primary directory nodes.
7. The brain graph use case set generating method according to claim 3, wherein in the step S3, the selected existing brain graph nodes are single or multiple test case nodes and corresponding child nodes:
if the test case node is a single test case node, directly modifying the identity mark code of the new test case node into a newly generated universal unique identification code, and adding a suffix mark on the basis of the node name of the selected existing test case node to generate the node name of the new test case node;
and if the test case nodes are a plurality of test case nodes of the same level, circularly traversing the primary test case nodes, respectively generating the universal unique identification codes of the primary test case nodes, assigning the universal unique identification codes to the identity identification code attributes corresponding to the new primary test case nodes, respectively adding suffix identification on the basis of the node names of the selected existing test cases, and generating the node names corresponding to the new primary test case nodes.
8. The brain graph use case set generating method according to claim 3, wherein in step S3, the selected existing brain graph nodes are a plurality of directory nodes and test case nodes:
and circularly traversing the primary directory node and the primary test case node, respectively generating universal unique identification codes of the primary directory node and the primary test case node, assigning the universal unique identification codes to the identity identification code attributes of the corresponding new primary directory node or the primary test case node, respectively adding suffix identification on the basis of the node name of the selected primary directory node or the primary test case node, and generating the node name of the corresponding new primary directory node or the primary test case node.
9. The brain graph use case set generating method according to claim 1, wherein the edited contents of the brain graph nodes in the step S4 include directory node names, test case node names, individual test steps, and expected results.
10. An electroencephalogram corpus generating apparatus, comprising:
a memory for storing instructions executable by the processor;
a processor for executing the instructions to implement the method of any one of claims 1-9.
11. A computer readable medium having computer instructions stored thereon, wherein the computer instructions, when executed by a processor, perform the method of any of claims 1-9.
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