CN115629993A - Software testing method and device, computer equipment and readable storage medium - Google Patents
Software testing method and device, computer equipment and readable storage medium Download PDFInfo
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- CN115629993A CN115629993A CN202211629242.8A CN202211629242A CN115629993A CN 115629993 A CN115629993 A CN 115629993A CN 202211629242 A CN202211629242 A CN 202211629242A CN 115629993 A CN115629993 A CN 115629993A
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/36—Preventing errors by testing or debugging software
- G06F11/3668—Software testing
- G06F11/3672—Test management
- G06F11/3688—Test management for test execution, e.g. scheduling of test suites
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20081—Training; Learning
Abstract
The application discloses a software testing method and device, computer equipment and a readable storage medium. The software testing method of the embodiment of the application comprises the following steps: acquiring an operation image displayed when the software to be tested operates in the terminal; identifying display information of preset characteristics in the running image through a pre-trained identification model; and testing the software to be tested based on the display information. The application also discloses a software testing device, computer equipment and a computer readable storage medium. The method comprises the steps of firstly obtaining an operation image displayed when the software to be tested operates, then identifying display information of preset characteristics in the operation image through a pre-trained identification model, wherein the display information can be used for reflecting whether a set function of the software to be tested can be correctly used in a terminal or not, then testing the software to be tested based on the display information, and testing the software to be tested by using the method for different terminals where the software to be tested operates, so that the universality is high.
Description
Technical Field
The present disclosure relates to the field of testing technologies, and in particular, to a software testing method, a software testing apparatus, a computer device, and a computer-readable storage medium.
Background
In the related art, the current automated testing of software is based on fixed-point screenshot, and manual verification or verification of a small amount of image recognition verification is performed on an image, for example, in Android-based automated testing, different scripts are required to be written for performing the automated testing in different software application environments, and the universality of a testing mode is poor.
Disclosure of Invention
In order to solve at least one technical problem in the foregoing background, embodiments of the present application provide a software testing method, a software testing apparatus, a computer device, and a computer-readable storage medium.
The software testing method of the embodiment of the application comprises the following steps:
acquiring an operation image displayed when the software to be tested operates in the terminal;
identifying display information of preset characteristics in the running image through a pre-trained identification model; and
and testing the software to be tested based on the display information.
In certain embodiments, the testing method further comprises: training a recognition model; the training recognition model comprises:
acquiring a training image displayed when software to be tested runs in a terminal;
identifying training display information of preset features in the training image; and
and taking the training image and the training display information as input training to obtain the recognition model.
In some embodiments, the display information includes one or more of the following:
whether the preset feature is displayed or not, the type of the preset feature displayed, the time of displaying the preset feature, the coordinate of displaying the preset feature, the time of displaying the preset feature and the time of disappearing the preset feature.
In some embodiments, the testing the software under test based on the display information includes:
comparing the display information with standard display information to calculate the test score of the software to be tested; and
and judging whether the software to be tested meets the test standard or not based on the test score.
In some embodiments, the software to be tested includes navigation software, and the preset features include any one or more of a speed limit sign, an enlarged intersection, a camera, and a navigation beacon.
In some embodiments, the acquiring a running image displayed when the software to be tested runs in the terminal includes:
and acquiring a navigation image of the software to be tested during navigation through the terminal.
In certain embodiments, the testing method further comprises:
identifying a reliability score of the display information through the identification model; and
the recognition model is retrained based on the reliability score and the confirmation information input by the user.
The software testing device according to the embodiment of the present application includes:
the acquisition module is used for acquiring an operation image displayed when the software to be tested operates in the terminal;
the recognition module is used for recognizing the display information of the preset characteristics in the running image through a pre-trained recognition model; and
and the testing module is used for testing the software to be tested based on the display information.
The computer device of the embodiment of the application comprises: one or more processors; a memory; and one or more computer programs, wherein the one or more computer programs are stored in the memory and configured to be executed by the one or more processors, the one or more computer programs configured to: the software testing method according to any embodiment of the present application is executed.
The non-transitory computer-readable storage medium of the embodiments of the present application stores a computer program that, when executed by one or more processors, causes the processors to perform the method of testing software described in any of the embodiments of the present application.
In the software testing method, the software testing device, the computer equipment and the computer readable storage medium, the running image displayed when the software to be tested runs is obtained, the display information of the preset features in the running image is identified through the pre-trained identification model, the display information can be used for reflecting whether the set function of the software to be tested can be correctly used in the terminal, and then the software to be tested is tested based on the display.
Additional aspects and advantages of embodiments of the present application will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the present application.
Drawings
The above and/or additional aspects and advantages of the present application will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
FIG. 1 is a schematic flow chart illustrating a method for testing software according to a first embodiment of the present application;
FIG. 2 is an exemplary illustration of a running image of a second embodiment of the present application;
FIG. 3 is an exemplary diagram of identifying a running image according to a second embodiment of the present application;
FIG. 4 is a flowchart illustrating a method for testing software according to a third embodiment of the present application;
FIG. 5 is an exemplary diagram of a labeled training image according to a fourth embodiment of the present application;
fig. 6 is a schematic flowchart of a software testing method according to a fifth embodiment of the present application;
FIG. 7 is a flowchart illustrating a method for testing software according to a sixth embodiment of the present application;
fig. 8 is a schematic flow chart of a software testing apparatus according to a seventh embodiment of the present application;
fig. 9 is a schematic flow chart of a software testing apparatus according to an eighth embodiment of the present application;
FIG. 10 is a schematic representation of a computer readable storage medium in communication with a processor according to some embodiments of the present application;
FIG. 11 is a schematic diagram of a computer device according to some embodiments of the present application.
Detailed Description
Reference will now be made in detail to embodiments of the present application, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below by referring to the drawings are exemplary only for the purpose of explaining the embodiments of the present application, and are not to be construed as limiting the embodiments of the present application.
In the related art, the current automated testing of software is based on fixed-point screenshot, and manual verification or verification of a small number of image recognition verifications is performed on images, such as Android-based automated testing:
1. the client side is a test script, namely a webdriver test script;
2. the server is opened by the apium at the server (the default port is 4723), and the apium server receives the request sent by the client, analyzes the content of the request and calls the corresponding frame response operation;
3. the api server forwards the request to a middleware Bootstrap. Jar, the Bootstrap. Jar is installed on the equipment to monitor 4723 ports and receive the command of the api, and then the command of the UiAutomator is called for execution;
4. bootstrap returns the executed result to the appium server;
5. the appium server returns the result to the appium client;
at the heart of the Appium is a Web server that exposes the REST API. The method comprises the steps that the connection is received from a client, commands are intercepted, the commands are executed on the mobile equipment, then the mobile equipment returns the executed result to the apium server, and the apium server returns the executed result to the client.
The major drawbacks of the apple-based automated testing are: less systematic data can be referred to; the input speed of the text box is low, and Chinese input is not supported; only UI tests are supported, unit tests are not supported, and the like; the cross-application test is not supported, and different scripts are required to be written for different mobile phones, such as functions of photographing and the like; UI positioning is based on component parsing, and capturing cannot be performed on some videos and OpenGL rendering effects.
Referring to fig. 1, fig. 1 is a schematic flow chart of a software testing method according to a first embodiment of the present application, the software testing method including the steps of:
01: acquiring an operation image displayed when the software to be tested operates in the terminal;
02: identifying display information of preset characteristics in the running image through a pre-trained identification model; and
03: and testing the software to be tested based on the display information.
According to the testing method, the running image displayed when the software to be tested runs is obtained, the display information of the preset characteristics in the running image is identified through the pre-trained identification model, the display information can be used for reflecting whether the set function of the software to be tested can be correctly used in the terminal, the software to be tested is tested based on the display information, and the method can be used for testing the software to be tested for different terminals where the software to be tested runs, and the universality is high.
Specifically, in step 01, an operation image displayed when the software to be tested runs in the terminal is obtained, it is understood that the software to be tested may be any type of software, such as navigation software, multimedia software, communication software, game software, and the like, which is not limited herein, and the software to be tested is exemplified by the navigation software in the present application. The running image may be in the form of a picture, a video stream, and the like, and when the running image is in the form of a picture, the running image may be one or more running images, which is not limited herein. The software to be tested can run in the terminal, for example, the software to be tested can run in the terminal such as a mobile phone, a tablet, a smart watch and a vehicle-mounted navigator, the terminal can display a running image of the software to be tested during running, and the terminal can also collect the running image, for example, the running image is collected in a screen capturing or screen recording mode. In one example, a tensoflow detection api use environment may be configured in the terminal. In an example that the software to be tested is navigation software, please refer to fig. 2, where fig. 2 is an exemplary diagram of an operation image according to a second embodiment of the present application, and step 01 may specifically obtain the operation image by acquiring a navigation image of the software to be tested during navigation through a terminal, where the two acquired operation images are shown in fig. 2 and are P1 and P2, respectively.
In step 02, the display information of the preset feature in the running image is recognized through a pre-trained recognition model, wherein the recognition model can be obtained through pre-training, the display information of the preset feature can be recognized through the recognition model, the preset feature can be a feature determined in advance when the recognition model is trained, and the display information can be one or more of information such as whether the preset feature is displayed or not, the type of the displayed preset feature, the time for displaying the preset feature, the coordinates for displaying the preset feature, the time for disappearing the preset feature and the like, which is not limited herein. Taking the software to be tested as the navigation software as an example, the preset characteristics can be any one or more of a speed limit sign, an enlarged intersection image, a camera and a navigation mark. Referring to fig. 3, fig. 3 is an exemplary diagram of a recognition operation image according to a second embodiment of the present disclosure, in fig. 3, preset features such as an enlarged intersection image, a speed limit sign, a camera, and a navigation mark can be recognized through the recognition operation image P1, and the display information may include information such as a time when the preset features start to be displayed, a displayed coordinate, and a displayed duration; the preset features such as enlarged intersection images and navigation marks can be recognized by recognizing the running image P2, and the display information can include information such as the time for starting to display the preset features, the displayed coordinates and the displayed duration. Of course, the recognition results and the display information are different for different software to be tested or different preset characteristics.
In step 03, the software to be tested is tested based on the display information, it can be understood that the display information actually reflects the operation condition of the software to be tested, and whether the operation of the software to be tested meets the test standard can be tested by comparing the standard display information of the standard software under the same use scene. In the prior art of related navigation software testing, manual verification is mainly used, efficiency is low, yield is difficult to improve, meanwhile, recording of the whole working state of the navigation software is not detailed enough, and some problems in the running process, such as the fact that a lane line does not appear, the enlarged image is slower than the prior version display, are difficult to find, and the like, and by implementing the step 01, the step 02 and the step 03, the efficiency and the accuracy of testing the software to be tested are greatly improved.
Referring to fig. 4, fig. 4 is a schematic flow chart of a software testing method according to a third embodiment of the present application, and in some embodiments, the software testing method further includes step 04: training a recognition model, wherein step 04: training a recognition model, comprising the steps of:
041: acquiring a training image displayed when software to be tested runs in a terminal;
042: identifying training display information of preset features in the training images; and
043: and taking the training image and the training display information as input training to obtain the recognition model.
Before testing the software to be tested, the identification model needs to be trained, specifically, in step 041, the training image displayed when the software to be tested runs in the terminal is acquired, and the acquisition mode of the training image may refer to the acquisition mode of the running image, which is not described herein again. After the training image is obtained, step 042 is performed, that is, training display information of the preset features in the training image is identified, wherein the preset features are the same as the preset features in the actual test, the training display information can be identified by adopting a manual identification mode, the type of the training display information is similar to that of the display information, and details are not repeated here. After the training image and the training display information are obtained, the step 043 is implemented: training images and training display information are used as input training to obtain a recognition model, specifically, each training image and the training display information on the training image are used as a group of training data to be input, artificial intelligence training is carried out through multiple groups of training data, and the recognition model is output after multiple rounds of training are carried out.
In an example where the software to be tested is navigation software, please refer to fig. 5, where fig. 5 is an exemplary diagram of a labeled training image according to a fourth embodiment of the present application, the preset features include a navigation beacon, a camera, a speed limit label, and an enlarged view, and by labeling the training images P3 and P4, training display information of the preset features in the training images P3 and P4 can be obtained to be used as input for training, and a recognition model obtained through training will have the capability of recognizing display information of the preset features. In one example, the recognition model can be trained based on the ssd _ mobilenet _ v1 model.
Referring to fig. 6, fig. 6 is a flowchart illustrating a software testing method according to a fifth embodiment of the present application, in some embodiments, step 03: the method for testing the software to be tested based on the display information comprises the following steps:
031: comparing the display information with the standard display information to calculate the test score of the software to be tested; and
032: and judging whether the software to be tested meets the test standard or not based on the test score.
Specifically, in step 031, the standard display information is display information of preset features when the standard software runs under the same use scenario, the higher the similarity between the display information of the software to be tested and the standard display information, the higher the test score of the software to be tested, and the lower the similarity between the display information of the software to be tested and the standard display information, the lower the test score of the software to be tested, and the specific score may be calculated according to weights of different display information, and the standard for calculating the score may be set by a tester, which is not limited herein.
In step 032, it is determined whether the software to be tested meets the test standard based on the test score, specifically, a score threshold may be preset, if the test score is lower than the score threshold, it is determined that the software to be tested does not meet the test standard, and if the test score is higher than or equal to the score threshold, it is determined that the software to be tested meets the test standard. By means of calculating the test scores and judging, the software to be tested can be automatically tested conveniently, and the test efficiency and accuracy are improved.
Referring to fig. 7, fig. 7 is a flowchart illustrating a software testing method according to a sixth embodiment of the present application, in some embodiments, the software testing method further includes:
05: identifying a reliability score of the display information through the identification model; and
06: the recognition model is trained again based on the reliability score and the confirmation information input by the user.
Specifically, when step 05 is implemented, the reliability score of the display information is identified through the identification model, and when each preset feature is identified by the identification model, the reliability score of the feature as the preset feature is synchronously calculated, wherein the higher the reliability score is, the higher the identification accuracy is indicated, and the lower the reliability score is, the lower the identification accuracy is indicated. The user may enter confirmation information, such as confirming that the recognition result is correct or confirming that the recognition result is incorrect.
In the step 06, the recognition model is retrained again based on the reliability score and the confirmation information input by the user, whether the recognized feature is the preset feature can be determined through the confirmation information input by the user, and the confirmation information of the user can be used as the input data for further training the recognition model again, so that the recognition model is further perfected, and the recognition accuracy of the recognition model is continuously improved.
Referring to fig. 8, fig. 8 is a flowchart illustrating a software testing apparatus 10 according to a seventh embodiment of the present application, in which in some embodiments, the software testing apparatus 10 includes an obtaining module 11, an identifying module 12, and a testing module 13. The obtaining module 11 may be configured to implement step 01, that is, the obtaining module 11 may be configured to obtain an operation image displayed when the software to be tested operates in the terminal. The recognition module 12 may be configured to implement step 02, that is, the recognition module 12 may be configured to recognize the display information of the preset feature in the running image through a pre-trained recognition model. The test module 13 may be configured to perform step 03, that is, the test module 13 may be configured to test the software to be tested based on the display information.
Referring to fig. 9, fig. 9 is a flowchart illustrating a software testing apparatus 10 according to an eighth embodiment of the present application, in some embodiments, the software testing apparatus 10 further includes a training module 14, and the training module 14 may be configured to perform step 04, that is, the training module 14 may be configured to train the recognition module. Specifically, the training module 14 may be configured to implement step 041, step 042, and step 043, that is, the training module 14 may be configured to obtain a training image displayed when the software to be tested runs in the terminal; identifying training display information of preset features in the training images; and training the training image and the training display information as input to obtain the recognition model.
Referring to fig. 8, in some embodiments, the test module 13 may be configured to perform steps 031 and 032, that is, the test module 13 may be configured to compare the display information with the standard display information to calculate a test score of the software to be tested; and judging whether the software to be tested meets the test standard or not based on the test score.
With continued reference to fig. 9, in some embodiments, the training module 14 may be further configured to perform steps 05 and 06, that is, the training module 14 may be further configured to identify a reliability score of the displayed information through the recognition model; and retraining the recognition model based on the reliability score and the confirmation information input by the user.
It should be noted that, the details of the implementation and the effects achieved when the software testing apparatus 10 implements the software testing method according to any embodiment of the present application may refer to the description of the software testing method, and are not described herein again.
In addition, referring to fig. 10, an embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored, and the computer program, when executed by a processor, implements the software testing method according to any of the above embodiments. The computer-readable storage medium includes, but is not limited to, any type of disk including floppy disks, hard disks, optical disks, CD-ROMs, and magnetic-optical disks, ROMs (Read-Only memories), RAMs (Random Access memories), EPROMs (Erasable Programmable Read-Only memories), EEPROMs (Electrically Erasable Programmable Read-Only memories), flash memories, magnetic cards, or optical cards. That is, a storage device includes any medium that stores or transmits information in a form readable by a device (e.g., a computer, a cellular phone), and may be a read-only memory, a magnetic or optical disk, or the like.
The contents of the method embodiments of the present application are all applicable to the storage medium embodiments, the functions specifically implemented by the storage medium embodiments are the same as those of the method embodiments, and the beneficial effects achieved by the storage medium embodiments are also the same as those achieved by the method described above, and for details, refer to the description of the method embodiments, and are not described herein again.
In addition, referring to fig. 11, an embodiment of the present application further provides a computer device, where the computer device described in this embodiment may be a server, a personal computer, a network device, and other devices. The computer device includes one or more processors, memory, and one or more computer programs. Wherein the one or more computer programs are stored in the memory and configured to be executed by the one or more processors. The one or more computer programs are configured to perform the method of testing software as described in any of the embodiments above.
In the description herein, reference to the description of the terms "one embodiment," "some embodiments," "an illustrative embodiment," "an example," "a specific example" or "some examples" or the like means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the application. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and the scope of the preferred embodiments of the present application includes other implementations in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present application.
Although embodiments of the present application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present application and that variations, modifications, substitutions and alterations in the above embodiments may be made by those of ordinary skill in the art within the scope of the present application.
Claims (10)
1. A method for testing software, the method comprising:
acquiring an operation image displayed when the software to be tested operates in the terminal;
identifying display information of preset characteristics in the running image through a pre-trained identification model; and
and testing the software to be tested based on the display information.
2. The method for testing software according to claim 1, further comprising: training a recognition model; the training recognition model comprises:
acquiring a training image displayed when software to be tested runs in a terminal;
identifying training display information of preset features in the training image; and
and taking the training image and the training display information as input training to obtain the recognition model.
3. The method of testing software of claim 1, wherein the display information includes one or more of the following:
whether the preset feature is displayed or not, the type of the preset feature displayed, the time of displaying the preset feature, the coordinate of displaying the preset feature, the time of displaying the preset feature and the time of disappearing the preset feature.
4. The method for testing software according to claim 1, wherein the testing the software to be tested based on the display information comprises:
comparing the display information with standard display information to calculate the test score of the software to be tested; and
and judging whether the software to be tested meets the test standard or not based on the test score.
5. The software testing method according to claim 1, wherein the software to be tested comprises navigation software, and the preset features comprise any one or more of a speed limit sign, an enlarged intersection image, a camera and a navigation mark.
6. The method for testing software according to claim 5, wherein the obtaining of the running image displayed when the software to be tested runs in the terminal comprises:
and acquiring a navigation image of the software to be tested during navigation through the terminal.
7. The method for testing software according to claim 1, further comprising:
identifying a reliability score of the display information through the identification model; and
the recognition model is retrained based on the reliability score and the confirmation information input by the user.
8. A test apparatus for software, the test apparatus comprising:
the acquisition module is used for acquiring an operation image displayed when the software to be tested operates in the terminal;
the recognition module is used for recognizing the display information of the preset characteristics in the running image through a pre-trained recognition model; and
and the testing module is used for testing the software to be tested based on the display information.
9. A computer device, comprising:
one or more processors;
a memory; and
one or more computer programs, wherein the one or more computer programs are stored in the memory and configured to be executed by the one or more processors, the one or more computer programs configured to: a test method for executing software according to any one of claims 1 to 7.
10. A non-transitory computer-readable storage medium storing a computer program, wherein the computer program, when executed by one or more processors, causes the processors to perform a method of testing software according to any one of claims 1 to 7.
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