CN114328267A

CN114328267A - Big data testing method and device based on artificial intelligence, electronic equipment and medium

Info

Publication number: CN114328267A
Application number: CN202210033883.0A
Authority: CN
Inventors: 唐伟
Original assignee: Ping An Technology Shenzhen Co Ltd
Current assignee: Ping An Technology Shenzhen Co Ltd
Priority date: 2022-01-12
Filing date: 2022-01-12
Publication date: 2022-04-12

Abstract

The invention relates to the technical field of artificial intelligence, and provides a big data testing method, a device, electronic equipment and a medium based on artificial intelligence, wherein the method comprises the following steps: analyzing the received task to be tested to obtain an extraction path of the script to be tested; when an extraction path of the script to be tested exists in a preset script correlation server, extracting the script to be tested based on the extraction path of the script to be tested; analyzing the script to be tested to obtain a target table name of the data table; mapping a corresponding processing task based on the target table name of the source table, and detecting the script to be tested according to the processing task to obtain a detection result; and testing the test script to be tested based on the detection result. The invention finds out the corresponding processing task reversely according to the target table name of the source table for detection, thereby improving the detection efficiency and accuracy.

Description

Big data testing method and device based on artificial intelligence, electronic equipment and medium

Technical Field

The invention relates to the technical field of artificial intelligence, in particular to a big data testing method and device based on artificial intelligence, electronic equipment and a medium.

Background

In the software development process, most of the adopted development processes are firstly demand analysis, then design and coding, and finally testing. At present, software development is mostly carried out in a continuous integration mode, namely, developers often integrate their works and carry out automatic testing every time of integration, so that problems in the development process are discovered as early as possible.

However, before testing, the existing testing platform needs to deploy a testing environment on a testing machine in advance according to a testing project, and meanwhile, whether the data to be tested is correct or not cannot be known, so that the testing accuracy and efficiency are low.

Disclosure of Invention

In view of the above, it is necessary to provide a method, an apparatus, an electronic device and a medium for testing big data based on artificial intelligence, which improve the detection efficiency and accuracy by finding out the corresponding processing task according to the target table name of the source table.

The invention provides a big data testing method based on artificial intelligence, which comprises the following steps:

analyzing the received task to be tested to obtain an extraction path of the script to be tested;

identifying whether an extraction path of the script to be tested exists in a preset script correlation server;

when the preset script correlation server has the extraction path of the script to be tested, extracting the script to be tested based on the extraction path of the script to be tested;

analyzing the script to be tested to obtain a target table name of a data table, wherein the data table comprises a source table and a target table;

mapping a corresponding processing task based on the target table name of the source table, and detecting the script to be tested according to the processing task to obtain a detection result;

and testing the script to be tested based on the detection result.

Optionally, the parsing the script to be tested to obtain the target table name of the data table includes:

preprocessing the script to be tested to obtain a preprocessed script to be tested;

detecting a preset key field in the preprocessed script to be tested, and extracting the table name of the data table according to the detected preset key field;

and detecting the table name, and determining the target table name of the data table according to the detection result.

Optionally, the detecting the table name, and determining the target table name of the data table according to the detection result includes:

when the table name is detected to be a variable name, inquiring a key value corresponding to the variable name in a preset dictionary, and determining the inquired key value as a target table name of the data table; or

When the length of the table name after the emptying processing is detected to meet a preset table name determining condition, determining the table name as a target table name of the data table; or

And when detecting that the table name contains brackets, determining that the table name is not the target table name.

Optionally, after extracting the script to be tested based on the extraction path of the script to be tested, the method further includes:

and detecting a plurality of preset script elements in the script to be tested.

Optionally, the detecting a plurality of preset script elements in the script to be tested includes:

detecting the scheduling time in the script to be tested; or

Detecting the task type in the script to be tested; or

And detecting a target table in the script to be tested.

Optionally, the detecting the scheduled time in the script to be tested includes:

detecting the lengths of the plurality of first tasks in the scheduling time to obtain a first detection result, and sending the first detection result to a client, where the first tasks include daily tasks, monthly tasks, weekly tasks, and yearly tasks, and the detecting the lengths of the plurality of first tasks in the scheduling time to obtain the first detection result includes:

judging whether the length of each first task in the plurality of first tasks meets the length requirement of the corresponding task;

when the length of each first task in the plurality of first tasks meets the length requirement of the corresponding task, detecting whether the numerical value of each first task in the plurality of first tasks meets the numerical value requirement of the corresponding task;

when the numerical value of each first task in the plurality of first tasks meets the numerical value requirement of the corresponding task, determining that the first detection result is that the scheduling time is correct; or

And when the numerical value of any one of the first tasks does not meet the numerical value requirement of the corresponding task, determining that the first detection result is the scheduling time abnormity.

Optionally, the detecting the script to be tested according to the processing task to obtain a detection result includes:

acquiring first document content corresponding to the processing task, and reading the script to be tested to acquire corresponding second document content;

sequentially comparing the first sub-content in each column of the first document content with the second sub-content in the corresponding column of the second document content;

when the first sub-content in each column of the first document content is consistent with the second sub-content in the corresponding column of the second document content, determining that the detection result is that the script is normal; or

And when the first sub-content in each column of the first document content is inconsistent with the second sub-content in the corresponding column of the second document content, determining that the detection result is a script abnormity.

A second aspect of the present invention provides an artificial intelligence-based big data testing apparatus, the apparatus comprising:

the first analysis module is used for analyzing the received task to be tested to obtain an extraction path of the script to be tested, wherein the task to be tested comprises a configuration file;

the identification module is used for identifying whether an extraction path of the script to be tested exists in a preset script correlation server;

the extracting module is used for extracting the script to be tested based on the extracting path of the script to be tested when the extracting path of the script to be tested exists in the preset script correlation server;

the second analysis module is used for analyzing the script to be tested to obtain a target table name of a data table, wherein the data table comprises a source table and a target table;

the first testing module is used for mapping out a corresponding processing task based on the target table name of the source table and detecting the script to be tested according to the processing task to obtain a detection result;

and the second testing module is used for testing the script to be tested based on the detection result.

A third aspect of the present invention provides an electronic device comprising a processor and a memory, wherein the processor is configured to implement the artificial intelligence based big data testing method when executing a computer program stored in the memory.

A fourth aspect of the present invention provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the artificial intelligence based big data testing method.

In summary, according to the big data testing method, device, electronic device and medium based on artificial intelligence, the extraction path of the script to be tested is obtained and identified by analyzing the received task to be tested, when the extraction path of the script to be tested exists in the preset script correlation server, the extraction path of the script to be tested is determined to be correct, and the corresponding script to be tested is extracted through the correct extraction path, so that the accuracy of the extracted script to be tested is improved. Analyzing the script to be tested extracted by extracting the path to obtain the target table name of the data table, mapping out the corresponding processing task based on the target table name of the source table, detecting the script to be tested according to the processing task, and searching out the corresponding processing task on a task management platform according to the target table name of the source table. And testing the script to be tested based on the detection result, and testing on the premise that the test result is a normal script, so that the test efficiency and accuracy are improved.

Drawings

Fig. 1 is a flowchart of a big data testing method based on artificial intelligence according to an embodiment of the present invention.

Fig. 2 is a structural diagram of a big data testing apparatus based on artificial intelligence according to a second embodiment of the present invention.

Fig. 3 is a schematic structural diagram of an electronic device according to a third embodiment of the present invention.

Detailed Description

In order that the above objects, features and advantages of the present invention can be more clearly understood, a detailed description of the present invention will be given below with reference to the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present invention and features of the embodiments may be combined with each other without conflict.

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.

Example one

In this embodiment, the method for testing big data based on artificial intelligence may be applied to an electronic device, and for an electronic device that needs to perform big data testing based on artificial intelligence, the function of big data testing based on artificial intelligence provided by the method of the present invention may be directly integrated on the electronic device, or may be run in the electronic device in the form of a Software Development Kit (SDK).

As shown in FIG. 1, the big data testing method based on artificial intelligence specifically includes the following steps, and the order of the steps in the flowchart can be changed and some steps can be omitted according to different requirements.

And S11, analyzing the received task to be tested to obtain the extraction path of the script to be tested.

In this embodiment, the task to be tested includes a configuration document, and the client uploads the configuration document of the task to be tested to the server, where the configuration document includes configuration resources such as log data, a required data table, and a table name of the data table, and specifically, in a process of uploading the configuration document of the task to be tested, the client may upload the configuration document through a corresponding test interface of the server, and upload the configuration document through the corresponding test interface, so that an upload speed and security of the configuration document may be ensured.

In other optional embodiments, the client may upload the configuration document of the task to be tested through a web page, and when uploading the configuration document through the web page.

In this embodiment, when the server receives the configuration document of the file to be tested, the server parses the configuration document, and obtains the extraction path of the script to be tested according to the parsing result.

In an optional embodiment, the analyzing the received task to be tested to obtain the extraction path of the script to be tested includes:

analyzing the configuration document in the task to be tested to obtain the path and the splicing rule filled in the configuration document;

and splicing the filled paths according to the splicing rule, and determining the spliced paths as the extraction paths of the scripts to be tested.

In this embodiment, when confirming the extraction path of the script to be tested, a path extraction rule may be preset, and a filled path may be extracted from the parsed configuration document according to the preset path extraction rule, where the filled path may be one or more.

And S12, identifying whether the preset script correlation server has the extraction path of the script to be tested.

In this embodiment, a script association server may be predetermined, and the script association server stores an extraction path of a script to be tested.

In an optional embodiment, the identifying whether the extraction path of the script to be tested exists in the preset script association server includes:

matching the extraction path of the script to be tested with the extraction path in the preset script correlation server;

when the extraction path of the script to be tested is completely matched with any one extraction path in the preset script correlation server, determining that the extraction path of the script to be tested exists in the preset script correlation server; or

And when the extraction path of the script to be tested is not completely matched with each extraction path in the preset script correlation server, determining that the extraction path of the script to be tested does not exist in the preset script correlation server.

Further, the method further comprises:

and when the preset script correlation server does not have the extraction path of the script to be tested, sending the extraction path error result of the script to be tested to the client.

In the embodiment, whether the extraction path of the script to be tested exists in the preset script correlation server is identified, when the extraction path of the script to be tested exists in the preset script correlation server, the extraction path of the script to be tested is determined to be correct, and the corresponding script to be tested is extracted through the correct extraction path, so that the accuracy rate of the extracted script to be tested is improved.

And S13, when the preset script correlation server has the extraction path of the script to be tested, extracting the script to be tested based on the extraction path of the script to be tested.

In this embodiment, the script to be tested refers to a script to be tested whether the script is an abnormal script, where the script refers to an executable file written according to a certain format by using a specific descriptive language.

In the embodiment, in the aspect of automated testing, all problems cannot be found by a large number of online big data programs through data testing alone, for example, a configuration document filled by a developer at the early stage of deployment often has low-level errors, the deployment problem occurs when a task processing test is subsequently performed by using the filled configuration document, when the deployment problem occurs, the test is stopped, and the document needs to be reconfigured for a secondary test, so that the script testing efficiency is low.

In the embodiment, before the test task is executed, the script to be tested is detected, so that the accuracy of the script to be tested is ensured, the problem of slow script test efficiency caused by incorrect configuration document and need of redeployment configuration is avoided, and the script test efficiency is improved.

In other optional embodiments, after extracting the script to be tested based on the extraction path of the script to be tested, the method further includes:

and detecting a plurality of preset script elements in the script to be tested.

Specifically, the detecting a plurality of preset script elements in the script to be tested includes:

detecting the scheduling time in the script to be tested; or

Detecting the task type in the script to be tested; or

And detecting a target table in the script to be tested.

In this embodiment, the multiple script elements may include other script elements that need to be detected, such as scheduling time, task type, and target table.

Further, the detecting the scheduled time in the script to be tested includes:

and detecting the lengths of a plurality of first tasks in the scheduling time to obtain a first detection result, and sending the first detection result to a client, wherein the first tasks comprise daily tasks, monthly tasks, weekly tasks and annual tasks.

Specifically, the detecting the lengths of the plurality of first tasks in the scheduling time to obtain a first detection result includes:

Further, the method further comprises:

and when the length of any one first task in the plurality of first tasks does not meet the length requirement of the corresponding task, determining that the first detection result is the scheduling time abnormity.

Illustratively, the length requirement of the daily task may be set to 4 bits; the length requirement of the monthly task can be set to 6 bits, and the length of the weekly task can be set to 4 bits; the annual task length may be set to 8 bits; the numerical requirement of the daily task can be set to include hours and minutes, the former two numerical values meet the numerical range of 0-24, and the latter two numerical values meet the numerical range of 0-59; the data requirement of the monthly task may be set to a value interval where the first two values satisfy 1-28.

Further, the detecting the task type in the script to be tested includes:

and detecting whether the scheduling time corresponding to the task type in the script to be tested is legal or not to obtain a second detection result.

Further, the detecting whether the scheduling time corresponding to the task type in the script to be tested is legal or not to obtain a second detection result includes:

initializing scheduling time corresponding to a task type in the script to be tested to obtain target scheduling time corresponding to the task type;

detecting whether the target scheduling time corresponding to the task type meets a preset format requirement or not;

when the target scheduling time corresponding to the task type meets the preset format requirement, the scheduling time corresponding to the task type in the script to be tested is legal, and a second detection result is determined to be that the task type in the script to be tested is normal; or

And when the target scheduling time corresponding to the task type does not meet the preset format requirement, determining that the scheduling time corresponding to the task type in the script to be tested is illegal, and determining that a second detection result is that the task type in the script to be tested is abnormal.

Illustratively, by initializing the day, month, week and year in the scheduling time corresponding to the task type in the script to be tested, which correspond to D, M, W1-W7, Y and 0, respectively, the preset format requirement is as follows: and if the initialized target scheduling time meets YYYMMDD, determining that the task type in the script to be tested is normal.

Further, the detecting the target table in the script to be tested comprises:

matching the script to be tested by adopting a regular expression according to a preset target table detection rule to obtain a first target table;

extracting the filled second target table from the configuration document;

detecting whether the first target table and the second target table are matched;

when the first target table is completely matched with the second target table, determining that a detection result is that the target table in the script to be tested is normal; or

And when the first target table and the second target table are not completely matched, determining that the detection result is that the target table in the script to be tested is abnormal.

In this embodiment, the first target table is a target table obtained by analyzing the script to be tested and matching the script with a regular expression according to a preset target table detection rule, and the second target table is a target table actually filled in during configuration.

In this embodiment, by matching the first target table with the second target table, whether the target table in the script to be tested is filled correctly can be quickly determined, and the accuracy of the script to be tested is further ensured.

In the embodiment, in order to further ensure the accuracy of the script to be tested, the detected abnormal result is timely sent to the client by detecting a plurality of preset script elements in the script to be tested, and the client maintains the script to be tested according to the detected abnormal result, so that the maintenance efficiency of the script to be tested is improved.

S14, analyzing the script to be tested to obtain the target table name of the data table, wherein the data table comprises a source table and a target table.

In an optional embodiment, the parsing the script to be tested to obtain the target table name of the data table includes:

In this embodiment, program analysis is performed by reading a program code in the script to be tested, and table name extraction of the data table is performed according to a preset keyword, where the preset key field may be a from key field, a join key field, or the like.

Illustratively, if the table name of the data table is an independent character string preceded by a space or a line break and followed by a from key before the table name, the data table is the source table, and if the table name of the data table is an independent character string preceded by a space or a line break and followed by a non-from key before the table name, the data table is the destination table.

In an optional embodiment, the preprocessing the script to be tested includes:

and performing null and annotation removing processing on the script to be tested.

In the embodiment, by performing the emptying and annotation removing processing on the script to be tested, the interference of the space script and the annotation script is reduced, and the testing efficiency and accuracy of the subsequent script to be tested are improved.

Further, the detecting the table name and determining the target table name of the data table according to the detection result includes:

And when the length of the table name after the emptying processing is detected to meet the preset table name determining condition, determining the table name as the target table name of the data table.

In this embodiment, before determining the table names of the data table, all variables and corresponding table names are stored in a key value pair form, a dictionary is created, and when the table names are detected to be the variable names, the table names corresponding to the variable names can be quickly detected in the created dictionary, so that the table name determination efficiency and accuracy are improved.

Further, the method further comprises:

and when the length of the table name after the emptying processing is detected not to meet the preset table name determination condition, abandoning the table name.

In this embodiment, a table name determination condition may be preset, for example, the table name determination condition may be preset that the character length is greater than or equal to 4 and includes a downward sliding line. And when the length of the table name after the emptying processing is less than 4 or no downslide line is included, determining that the table name is possibly in the next line, and abandoning the table name.

Further, the method further comprises:

and when detecting that the table name contains brackets, determining that the table name is not the target table name, determining that the table name is a false table name, and abandoning the table name.

In the embodiment, when extracting the table name of the data table, the target table name of the data table is determined by considering a plurality of aspects such as keywords, table name lengths, variable names and the like in the script to be tested, so that the accuracy of the target table name is improved, the correct target table name can be conveniently adopted for detecting the script to be tested subsequently, the detection accuracy of the script to be tested is improved, and the subsequent script testing accuracy is improved.

And S15, mapping a corresponding processing task based on the target table name of the source table, and detecting the script to be tested according to the processing task to obtain a detection result.

In this embodiment, when detecting a script to be tested, a reverse check is performed on the task management platform according to the target table name of the source table to obtain a processing task corresponding to the target table name of the source table, and the script to be tested is detected according to the processing task.

In an optional embodiment, the detecting the script to be tested according to the processing task to obtain a detection result includes:

In this embodiment, the first document content refers to content expected to be filled in a configuration document during a processing task, the first sub-content refers to content corresponding to each column in the first document content, the second document content refers to content actually filled in when the configuration document of a script to be tested is deployed, and the second sub-content refers to content corresponding to each column in the second document content, where there is a correspondence between the first document content and each column in the second document content.

In this embodiment, the first sub-content in each column of the first document content is sequentially compared with the second sub-content in the corresponding column of the second document content, whether an abnormal script exists is determined according to a comparison result, and when the abnormal script exists, it is determined that errors such as mis-filling, missing-filling, and multi-filling may occur in the script to be tested.

In the embodiment, the corresponding processing task is found back on the task management platform according to the target table name of the source table, because the first document content corresponding to the processing task is the content expected to be filled, namely the standard content, and the second document content is the content actually filled when the developer configures the document in the early stage, the second content is detected by taking the first content as the reference, the accuracy of the detection result is ensured, meanwhile, because the first document content and each column in the second document content have a corresponding relation, the first document content and each column of the second document content are depended on for comparison, whether the script to be tested has an abnormal script or not is quickly determined according to the comparison result, the problem of missed detection is avoided, and the detection accuracy is improved.

S16, testing the script to be tested based on the detection result.

In this embodiment, in order to ensure the efficiency and accuracy of the subsequent test, after the detection result is obtained, whether to test the script to be tested is determined according to the detection result.

In an optional embodiment, the testing the script to be tested based on the detection result includes:

when the detection result is that the script is normal, executing the test of the script to be tested; or

And when the detection result is that the script is abnormal, rejecting the test of the script to be tested.

In this embodiment, when the detection result is that the script is abnormal, it is determined that the script to be tested is an abnormal script, where the script abnormality indicates that abnormal data exists in the script to be tested, that is, error data exists in the configuration document, and the configuration document maintenance needs to be performed again, the script to be tested is rejected to be tested, and the abnormal script is sent to the client.

In the embodiment, the test is performed when the test result is the script is normal, so that the problem of low accuracy of the test result caused by errors in the configuration document is solved, and the test efficiency and accuracy are improved.

In summary, in the big data testing method based on artificial intelligence in this embodiment, the extraction path of the script to be tested is obtained and identified by analyzing the received task to be tested, when the extraction path of the script to be tested exists in the preset script association server, it is determined that the extraction path of the script to be tested is correct, and the corresponding script to be tested is extracted through the correct extraction path, so that the accuracy of the extracted script to be tested is improved. Analyzing the script to be tested extracted by extracting the path to obtain the target table name of the data table, mapping out the corresponding processing task based on the target table name of the source table, detecting the script to be tested according to the processing task, and searching out the corresponding processing task on a task management platform according to the target table name of the source table. And testing the script to be tested based on the detection result, and testing on the premise that the test result is a normal script, so that the test efficiency and accuracy are improved.

Example two

In some embodiments, the artificial intelligence based big data testing apparatus 20 may include a plurality of functional modules composed of program code segments. Program code of various program segments in the artificial intelligence based big data testing apparatus 20 may be stored in a memory of the electronic device and executed by the at least one processor to perform (see detailed description of fig. 1) the functions of the artificial intelligence based big data testing.

In this embodiment, the artificial intelligence based big data testing apparatus 20 may be divided into a plurality of functional modules according to the functions performed by the apparatus. The functional module may include: the system comprises a first analysis module 201, an identification module 202, an extraction module 203, a second analysis module 204, a first test module 205 and a second test module 206. The module referred to herein is a series of computer readable instruction segments stored in a memory that can be executed by at least one processor and that can perform a fixed function. In the present embodiment, the functions of the modules will be described in detail in the following embodiments.

The first analyzing module 201 is configured to analyze the received task to be tested to obtain an extraction path of the script to be tested.

In an optional embodiment, the analyzing, by the first analyzing module 201, the received task to be tested to obtain an extraction path of the script to be tested includes:

The identifying module 202 is configured to identify whether an extraction path of the script to be tested exists in a preset script association server.

In an optional embodiment, the identifying module 202 identifies whether an extraction path of the script to be tested exists in a preset script association server includes:

Further, when the preset script correlation server does not have the extraction path of the script to be tested, sending the result of the extraction path error of the script to be tested to the client.

The extracting module 203 is configured to, when the preset script correlation server has an extraction path of the script to be tested, extract the script to be tested based on the extraction path of the script to be tested.

In other optional embodiments, after the extracting module 203 extracts the script to be tested based on the extraction path of the script to be tested, a plurality of preset script elements in the script to be tested are detected.

detecting the scheduling time in the script to be tested; or

Detecting the task type in the script to be tested; or

And detecting a target table in the script to be tested.

Further, the detecting the scheduled time in the script to be tested includes:

Further, when the length of any one of the first tasks in the plurality of first tasks does not meet the length requirement of the corresponding task, determining that the first detection result is that the scheduling time is abnormal.

Further, the detecting the task type in the script to be tested includes:

Further, the detecting the target table in the script to be tested comprises:

extracting the filled second target table from the configuration document;

The second parsing module 204 is configured to parse the script to be tested to obtain a target table name of a data table, where the data table includes a source table and a target table.

In an optional embodiment, the parsing the script to be tested by the second parsing module 204 to obtain the target table name of the data table includes:

In an optional embodiment, the preprocessing the script to be tested includes:

Further, when the length of the table name after the emptying processing is detected not to meet the preset table name determination condition, the table name is abandoned.

Further, when detecting that the table name contains brackets, the table name is not the target table name, determining that the table name is a false table name, and abandoning the table name.

The first testing module 205 is configured to map a corresponding processing task based on the target table name of the source table, and detect the script to be tested according to the processing task to obtain a detection result.

In an optional embodiment, the detecting, by the first testing module 205, the script to be tested according to the processing task, and obtaining a detection result includes:

And the second testing module 206 is configured to test the script to be tested based on the detection result.

In an optional embodiment, the second testing module 206, based on the detection result, tests the script to be tested, including:

In summary, the big data testing apparatus based on artificial intelligence in this embodiment obtains and identifies the extraction path of the script to be tested by analyzing the received task to be tested, determines that the extraction path of the script to be tested is correct when the extraction path of the script to be tested exists in the preset script association server, and extracts the corresponding script to be tested through the correct extraction path, thereby improving the accuracy of the extracted script to be tested. Analyzing the script to be tested extracted by extracting the path to obtain the target table name of the data table, mapping out the corresponding processing task based on the target table name of the source table, detecting the script to be tested according to the processing task, and searching out the corresponding processing task on a task management platform according to the target table name of the source table. And testing the script to be tested based on the detection result, and testing on the premise that the test result is a normal script, so that the test efficiency and accuracy are improved.

EXAMPLE III

Fig. 3 is a schematic structural diagram of an electronic device according to a third embodiment of the present invention. In the preferred embodiment of the present invention, the electronic device 3 comprises a memory 31, at least one processor 32, at least one communication bus 33 and a transceiver 34.

It will be appreciated by those skilled in the art that the configuration of the electronic device shown in fig. 3 does not constitute a limitation of the embodiment of the present invention, and may be a bus-type configuration or a star-type configuration, and the electronic device 3 may include more or less other hardware or software than those shown, or a different arrangement of components.

In some embodiments, the electronic device 3 is an electronic device capable of automatically performing numerical calculation and/or information processing according to instructions set or stored in advance, and the hardware thereof includes but is not limited to a microprocessor, an application specific integrated circuit, a programmable gate array, a digital processor, an embedded device, and the like. The electronic device 3 may also include a client device, which includes, but is not limited to, any electronic product that can interact with a client through a keyboard, a mouse, a remote controller, a touch pad, or a voice control device, for example, a personal computer, a tablet computer, a smart phone, a digital camera, and the like.

It should be noted that the electronic device 3 is only an example, and other existing or future electronic products, such as those that can be adapted to the present invention, should also be included in the scope of the present invention, and are included herein by reference.

In some embodiments, the memory 31 is used for storing program codes and various data, such as the artificial intelligence based big data testing device 20 installed in the electronic equipment 3, and realizes high-speed and automatic access to programs or data during the operation of the electronic equipment 3. The Memory 31 includes a Read-Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Programmable Read-Only Memory (EPROM), a One-time Programmable Read-Only Memory (OTPROM), an electronically Erasable rewritable Read-Only Memory (Electrically-Erasable Programmable Read-Only Memory (EEPROM)), an optical Read-Only disk (CD-ROM) or other optical disk Memory, a magnetic disk Memory, a tape Memory, or any other medium readable by a computer capable of carrying or storing data.

In some embodiments, the at least one processor 32 may be composed of an integrated circuit, for example, a single packaged integrated circuit, or may be composed of a plurality of integrated circuits packaged with the same or different functions, including one or more Central Processing Units (CPUs), microprocessors, digital Processing chips, graphics processors, and combinations of various control chips. The at least one processor 32 is a Control Unit (Control Unit) of the electronic device 3, connects various components of the electronic device 3 by using various interfaces and lines, and executes various functions and processes data of the electronic device 3 by running or executing programs or modules stored in the memory 31 and calling data stored in the memory 31.

In some embodiments, the at least one communication bus 33 is arranged to enable connection communication between the memory 31 and the at least one processor 32 or the like.

Although not shown, the electronic device 3 may further include a power supply (such as a battery) for supplying power to each component, and optionally, the power supply may be logically connected to the at least one processor 32 through a power management device, so as to implement functions of managing charging, discharging, and power consumption through the power management device. The power supply may also include any component of one or more dc or ac power sources, recharging devices, power failure detection circuitry, power converters or inverters, power status indicators, and the like. The electronic device 3 may further include various sensors, a bluetooth module, a Wi-Fi module, and the like, which are not described herein again.

It is to be understood that the described embodiments are for purposes of illustration only and that the scope of the appended claims is not limited to such structures.

The integrated unit implemented in the form of a software functional module may be stored in a computer-readable storage medium. The software functional module is stored in a storage medium and includes several instructions to enable a computer device (which may be a personal computer, an electronic device, or a network device) or a processor (processor) to execute parts of the methods according to the embodiments of the present invention.

In a further embodiment, in conjunction with fig. 2, the at least one processor 32 may execute operating devices of the electronic device 3 and installed various types of applications (e.g., the artificial intelligence based big data testing device 20), program code, and the like, such as the various modules described above.

The memory 31 has program code stored therein, and the at least one processor 32 can call the program code stored in the memory 31 to perform related functions. For example, the modules illustrated in fig. 2 are program codes stored in the memory 31 and executed by the at least one processor 32, so as to implement the functions of the modules for the purpose of artificial intelligence based big data testing.

Illustratively, the program code may be partitioned into one or more modules/units that are stored in the memory 31 and executed by the processor 32 to accomplish the present application. The one or more modules/units may be a series of computer readable instruction segments capable of performing certain functions, which are used for describing the execution process of the program code in the electronic device 3. For example, the program code may be partitioned into a first parsing module 201, a recognition module 202, an extraction module 203, a second parsing module 204, a first testing module 205, and a second testing module 206.

In one embodiment of the present invention, the memory 31 stores a plurality of computer-readable instructions that are executed by the at least one processor 32 to implement the functionality of artificial intelligence based big data testing.

Specifically, the at least one processor 32 may refer to the description of the relevant steps in the embodiment corresponding to fig. 1, and details are not repeated here.

In the embodiments provided in the present invention, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is only one logical functional division, and other divisions may be realized in practice.

The modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.

In addition, functional modules in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional module.

It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned. Furthermore, it is obvious that the word "comprising" does not exclude other elements or that the singular does not exclude the plural. A plurality of units or means recited in the present invention may also be implemented by one unit or means through software or hardware. The terms first, second, etc. are used to denote names, but not any particular order.

Finally, it should be noted that the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A big data testing method based on artificial intelligence is characterized by comprising the following steps:

and testing the script to be tested based on the detection result.

2. The big data testing method based on artificial intelligence, as claimed in claim 1, wherein said parsing said script to be tested to obtain a target table name of a data table comprises:

3. The big data testing method based on artificial intelligence, as claimed in claim 2, wherein said detecting said table name, and said determining a target table name of the data table according to the detection result comprises:

4. The artificial intelligence based big data testing method of claim 1, wherein after extracting the script to be tested based on the extraction path of the script to be tested, the method further comprises:

and detecting a plurality of preset script elements in the script to be tested.

5. The artificial intelligence based big data testing method of claim 4, wherein the detecting the preset plurality of script elements in the script to be tested comprises:

detecting the scheduling time in the script to be tested; or

Detecting the task type in the script to be tested; or

And detecting a target table in the script to be tested.

6. The artificial intelligence based big data testing method of claim 5, wherein the detecting the scheduled time in the script to be tested comprises:

7. The big data testing method based on artificial intelligence, as claimed in claim 1, wherein said detecting said script to be tested according to said processing task, and obtaining a detection result comprises:

8. An artificial intelligence based big data testing device, the device comprising:

9. An electronic device, characterized in that the electronic device comprises a processor and a memory, the processor being configured to implement the artificial intelligence based big data testing method according to any one of claims 1 to 7 when executing the computer program stored in the memory.

10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, implements the artificial intelligence based big data testing method according to any one of claims 1 to 7.