CN114218114A - Full-automatic test data generation method based on interface flow arrangement - Google Patents
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Abstract
The invention discloses a full-automatic test data generation method based on interface flow arrangement, which comprises the following steps: recording initial interface calling information and synchronizing to a process engine; arranging a process engine; generating interface parameter data by a rule engine; generating test data; the invention realizes easy and efficient generation of test data, reduces the test data generation cost and enables the test data generation to be more intelligent and automatic.
Description
Technical Field
The invention relates to the technical field of testing, in particular to a full-automatic test data generation method based on interface flow arrangement.
Background
With the rise of internet computers, enterprise digital transfer, cloud service and the like, more and more businesses are transferred to the online high-efficiency processing. Different service systems deal with different service categories, and various systems with related service resolving capability are produced at the same time.
In order to meet the infinite business requirements, various data processing requirements are generated, and a large amount of research and development and test workload is generated by continuously iterating the system. Testing, whether performance testing, automated testing, or manual testing, requires the preparation of a variety of test data in advance, including the values of the various fields that are required to be used during testing.
In the prior art, there are ways of maintaining a data structure, selecting business data to be generated to generate data, and ways of generating test data corresponding to a business system based on a data generation request and a data rule set, but these ways all require full matching of the corresponding business system and a field rule to generate data corresponding to the business system, and it is difficult to ensure that a field is not deleted and changed in a later period after a field is maintained in an earlier period, and a large amount of maintenance work of rule name change caused by changes of the business system and the field name is performed in the later period, so that not only is the efficiency low, but also the success rate of data generation is reduced.
Disclosure of Invention
In order to solve the problems in the prior art, the invention aims to provide a full-automatic test data generation system based on interface flow arrangement by utilizing a flow engine, a rule engine and an NLP text matching technology contained in the rule engine, so that test data can be easily and efficiently generated, the test data generation cost is reduced, and the test data generation is more intelligent and automatic.
In order to achieve the purpose, the invention adopts the technical scheme that: a full-automatic test data generation method based on interface flow arrangement comprises the following steps:
step 1, recording initial interface calling information and synchronizing to a process engine: recording interface calling sequence and input data when a tester operates the service system by using a recording tool;
step 2, process engine arrangement: sequencing according to the interface calling sequence synchronized in the step 1 to form an initial interface execution chain, so that a tester can randomly arrange the interface calling sequence and logic to realize different operation scenes;
step 3, generating interface parameter data by the rule engine: regularly maintaining parameter data generation rules in the rule engine and adding comments, after the interface calling sequence is compiled in the step 2, triggering the rule engine when the interfaces are sequentially executed, automatically searching corresponding interface information in an interface document according to an interface name, matching the interface information with the data generation rules maintained in the rule engine by using NLP text matching, and calling the data generation rules to generate interface field parameters;
step 4, generating test data: positioning the interface flow arranged in the corresponding flow engine according to the name of the service system, the service category and the detailed service scene, transmitting the parameter data generated in the step 3 into the interface, and circularly executing to generate test data according to the generated number.
As a preferred embodiment, step 1 is specifically as follows:
when a tester starts a recording function, recording an interface calling step, a field transmitted into an interface and a corresponding field value when operating a service system; when the tester operates the service system according to the detailed service scene to finish recording, the recording tool synchronizes the recorded interface calling sequence and the recorded data of the operation system to the process engine; and when the tester operates the service system according to the detailed service scene to cancel recording, the recording tool calls the interface and deletes the recorded data when operating the service system.
As another preferred embodiment, in step 3, the data generation rule includes a data generation function, and the condition parameter is input, that is, a random parameter under the corresponding rule can be generated.
As another preferred embodiment, in step 3, matching the interface information with the data generation rule maintained in the rule engine by using NLP text matching specifically includes the following steps:
step one, the description of all parameter data generating functions in a rule engine is participled;
step two, generating corresponding text vectors for each group of participles in the step one respectively, and storing the vectors;
step three, making the descriptive information sentence of the interface in the interface document into participles to generate a text vector of the descriptive information in the same representation mode as that in the step two;
step four, utilizing cosine similarity to respectively calculate included angles between the text vectors in the step three and the text vectors stored in the step two, wherein the smaller the included angle is, the more similar the included angle is, and the vector stored in the step two is correspondingly the most similar match of the text vectors;
and subsequently, repeating the third step and the fourth step, namely matching all the interface parameters to the corresponding parameter generation rules.
The invention has the beneficial effects that:
in the invention, a tester only needs to record the service operation once and go to a process engine to arrange a data generation scene as required, and the data volume to be generated can be selected at a data generation tool, so that the test data can be generated in a one-key full-automatic manner.
Drawings
Fig. 1 is a flowchart of a test data generation method according to embodiment 2 of the present invention;
FIG. 2 is a diagram of test data generation architecture in example 2 of the present invention;
FIG. 3 is a visual interface diagram of a tester data generation tool in embodiment 2 of the present invention;
fig. 4 is a schematic diagram of an effect of a tester clicking a newly added button of a visual interface of a data generation tool in embodiment 2 of the present invention;
fig. 5 is a schematic diagram of an NLP text matching detection method in embodiment 2 of the present invention.
Detailed Description
Embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
Example 1
The core purpose of this embodiment is to implement automatic generation of test data of various service systems in a more flexible and efficient manner, and the test data generation is more intelligent and automated.
In order to achieve the above object, a technical solution adopted in this embodiment is to provide a full-automatic test data generation method based on interface flow arrangement, where an interface sequence and a parameter field called in an initial operation step are recorded and synchronized to a flow engine, and the flow engine is synchronized to enable a user to arrange the interface calling sequence at will, so as to implement different operation scenarios, a rule engine is used to read parameters and comments of a corresponding interface in a service system interface document and match the interface with a text of NLP to a data generation rule in the rule engine, so as to generate random parameters under the corresponding rule, and then the parameters generated in the rule engine are transmitted to an interface in which the flow engine arranges the calling sequence, and the interface is sequentially called to generate test data of a designated scenario, and the step of generating the number of data can be designated to cycle or more, so as to generate test data of the designated number. The method specifically comprises the following functional links:
1. the initial interface calls information records and synchronizes to a process engine link, provides a recording tool, records interface calling sequence and input data when a tester operates a system, and comprises a recording starting button, a recording ending button, a recording canceling button and an input control for naming recording operation. When a tester clicks a recording start button, the recording function of the tool is started, and the interface calling step, the fields transmitted into the interface and the corresponding field values are recorded when the business system is operated. When a tester operates a service system according to a detailed service scene and clicks a recording ending button, a recording tool synchronizes a recorded interface calling sequence and recorded data of the operating system into a process engine; when a tester operates the service system according to the detailed service scene and clicks a recording cancellation button, the recording tool calls the recorded interface of the operation system and deletes the recorded data.
2. In the process engine arrangement link, the interfaces synchronized in the step 1 are ordered according to the calling sequence to form an initial interface execution chain, so that testers can freely arrange the calling sequence and logic of the interfaces to realize different operation scenes.
3. A step 2, after interface calling sequence is arranged, the rule engine is triggered to automatically search corresponding interface information in an interface document according to the interface name when the interface is executed in sequence, interface information (including parameter field names, annotation languages and the like) is matched with data generating rules (including annotations of the parameter data generating rules) maintained in the rule engine by utilizing NLP text matching, and the data generating rules are called to generate interface field parameters. The data generation rule can be a data generation function, and the random parameters under the corresponding rule can be generated by inputting the condition parameters.
4. And in the test data generation link, providing a data generation tool and a visual interface, wherein the interface comprises a pull-down selection box: the name of the service system, the major category of the service, the detailed service scene is input into a box II: the number of data generation pieces, the new button and the data generation button are arranged. Selecting the name of the service system, the major class of the service, the detailed service scene, the number of generated data and the like, clicking a data generating button, positioning the trigger to the interface flow arranged in the corresponding flow engine according to the selected name of the service system, the major class of the service and the detailed service scene, transmitting the parameter data generated in step 3 into the interface, and circularly executing and generating test data according to the number of generated data.
Example 2
As shown in fig. 1, a method for generating fully automatic test data based on interface flow layout includes:
1. the initial interface calls information records and synchronizes to a process engine link, provides a recording tool, records interface calling addresses and sequences, fields of input interfaces and corresponding field values when a tester operates a system, and comprises a recording starting button, a recording ending button, a recording canceling button and an input control for naming recording operation.
1.1, the input control for naming the recording operation comprises a submission button and a cancellation button, and a tester is required to enter the name of the service system, the class of the service and the detailed service scene and click to submit, so that the tester can operate the service system according to the detailed service scene.
1.2, if a tester inputs a service system name, a service category and a detailed service scene, prompting whether the existing service scene already exists or not, if so, covering the original data by the data generated in the subsequent recording, and if not, adding a sequential number after the detailed service scene name, for example: if the business scene 002 is renamed again, the selection is no, the sequence numbering is changed: business scenario 003, and so on.
1.3, the recording tool can be in a browser plug-in form or a desktop program, when a tester clicks a start recording button, the recording function of the tool is started, and the fields and corresponding field values of the interface calling step and the incoming interface are recorded when the business system is operated. When a tester operates a service system according to a detailed service scene and clicks a recording ending button, a recording tool synchronizes a recorded interface calling step and recorded data to a process engine; when a tester operates the service system according to the detailed service scene and clicks a recording cancellation button, the recording tool calls the recorded interface of the operation system and deletes the recorded data.
2. And in the process engine arrangement link, the synchronous interfaces in the 1.3 are ordered according to the order of the interfaces to form an initial interface execution chain, so that a tester can flexibly modify the execution order and logic of the interface execution chain. As shown in fig. 2, the interface call sequence is: interface 1 is called first and then interface 2 is called, which is denoted as interface 1- > interface 2.
2.1, synchronizing the interface and the calling sequence of the process engine, visually displaying, as shown in fig. 3, and controlling the interface calling sequence and logic by dragging the interface to adjust the interface pointing arrow and changing the arrow attribute, similar to the existing process engine;
2.2, the interface call logic is similar to existing flow engines, such as:
sequential flow, namely realizing sequential calling of interfaces according to a well-arranged sequence;
parallel bifurcation, namely generating a plurality of branches after calling a certain interface and continuously calling the branch interfaces in parallel;
and thirdly, synchronizing, namely combining a plurality of parallel sub-interface links into one interface link in the process. The node interface can execute the interface call only after all the branch interfaces are executed;
exclusive selection, after one interface is called, only one interface can be executed in the following multiple interface branches;
simple aggregation, combining one interface of more than 2 branches into one branch, and finishing one branch and finishing other branches automatically.
3. And 2, after an interface calling sequence (interface 1- > interface 2) is arranged in the step 2, the rule engine is triggered to automatically search corresponding interface information in an interface document according to an interface name when the interfaces are sequentially executed, the interface information (comprising a parameter field name, a comment language and the like) is matched with a data generation rule (comprising a comment of the parameter data generation rule) maintained in the rule engine by utilizing NLP text matching, and the data generation rule is called to generate an interface field parameter.
3.1, the rule engine can regularly maintain parameter data generation rules and add comments, and the rules can be a plurality of functions for generating category data in each category:
the func generateId () # generates an id, and the function implementation function is to randomly generate an id value of an integer. The function may be limited, for example, if the id is required to be not repeatable, the maximum id may be queried in the database, and the obtained parameter is the maximum id + 1. For example, the id field in fig. 2 can be matched with the NLP text according to the field name, and can be matched with this function to generate the id meeting the specification.
Second, generating name from the connected _ message, database _ name, user, name #, and randomly taking the value of field name from the user in database, where the connected _ message is database connection information. For example, the name field in fig. 2 may be matched with the text according to the field name, and may be matched to the function, and a value randomly extracted from the name field of the user table in the database is located as the parameter data of the field.
3.2, as shown in fig. 5, the NLP text matching detection method in 3.1 specifically operates as follows:
step one, segmenting the description of all parameter data generating functions in the rule engine
And step two, generating corresponding text vectors for each group of participles in the step one respectively, and storing the vectors. The corresponding value of the word in the vector may be 0/1, 0 represents that the word appears in the text, and 1 represents that the word does not appear in the text; may be TF values (word frequency); may be the DF value (document frequency, higher DF indicates more common words and thus lower discrimination, lower weight); can be TF-IDF value, can be N-Gram, can be Embedding (word level Embedding, document level Embedding)
And step three, taking the descriptive information statement of the name field of the interface 1 in the interface document as a participle, and generating a text vector corresponding to the descriptive information of the name field in the same representation mode as the step two.
And step four, utilizing cosine similarity to respectively calculate included angles between the text vectors in the step three and the text vectors stored in the step two, wherein the smaller the included angle is, the more similar the included angle is, and the vector stored in the step two is the most similar match of the text vectors.
And subsequently, repeating the third step and the fourth step, namely matching all the interface parameters to the corresponding parameter generation rules.
4. In the test data generation step, a data generation tool is provided, and a visual interface is provided, as shown in fig. 4, the interface includes (i) a pull-down selection box: the name of the service system, the major category of the service, the detailed service scene is input into a box II: the number of data generation pieces, the new button and the data generation button are arranged. Selecting the name of the service system, the major class of the service, the detailed service scene, the number of generated data and the like, clicking a button for generating the data, positioning the trigger to the interface flow arranged in the corresponding flow engine according to the selected name of the service system, the major class of the service and the detailed service scene, and circularly executing and generating test data according to the number of generated data.
4.1, newly generating a group of drop-down selection boxes after clicking the newly added button: input box: and generating data buttons so as to generate test data of other service systems/service large-class/detailed service scenes when the data button is clicked.
The above-mentioned embodiments only express the specific embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention.
Claims (4)
1. A full-automatic test data generation method based on interface flow arrangement is characterized by comprising the following steps:
step 1, recording initial interface calling information and synchronizing to a process engine: recording interface calling sequence and input data when a tester operates the service system by using a recording tool;
step 2, process engine arrangement: sequencing according to the interface calling sequence synchronized in the step 1 to form an initial interface execution chain, so that a tester can randomly arrange the interface calling sequence and logic to realize different operation scenes;
step 3, generating interface parameter data by the rule engine: regularly maintaining parameter data generation rules in the rule engine and adding comments, after the interface calling sequence is compiled in the step 2, triggering the rule engine when the interfaces are sequentially executed, automatically searching corresponding interface information in an interface document according to an interface name, matching the interface information with the data generation rules maintained in the rule engine by using NLP text matching, and calling the data generation rules to generate interface field parameters;
step 4, generating test data: positioning the interface flow arranged in the corresponding flow engine according to the name of the service system, the service category and the detailed service scene, transmitting the parameter data generated in the step 3 into the interface, and circularly executing to generate test data according to the generated number.
2. The method for generating fully automatic test data based on interface flow arrangement according to claim 1, wherein the step 1 is as follows:
when a tester starts a recording function, recording an interface calling step, a field transmitted into an interface and a corresponding field value when operating a service system; when the tester operates the service system according to the detailed service scene to finish recording, the recording tool synchronizes the recorded interface calling sequence and the recorded data of the operation system to the process engine; and when the tester operates the service system according to the detailed service scene to cancel recording, the recording tool calls the interface and deletes the recorded data when operating the service system.
3. The method according to claim 1, wherein in step 3, the data generation rule includes a data generation function, and the condition parameters are inputted, i.e. random parameters under the corresponding rule can be generated.
4. The method for generating full-automatic test data based on interface process layout according to claim 1, wherein in step 3, matching interface information with a data generation rule maintained in a rule engine by NLP text matching specifically comprises the following steps:
step one, the description of all parameter data generating functions in a rule engine is participled;
step two, generating corresponding text vectors for each group of participles in the step one respectively, and storing the vectors;
step three, making the descriptive information sentence of the interface in the interface document into participles to generate a text vector of the descriptive information in the same representation mode as that in the step two;
step four, utilizing cosine similarity to respectively calculate included angles between the text vectors in the step three and the text vectors stored in the step two, wherein the smaller the included angle is, the more similar the included angle is, and the vector stored in the step two is correspondingly the most similar match of the text vectors;
and subsequently, repeating the third step and the fourth step, namely matching all the interface parameters to the corresponding parameter generation rules.
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