CN116955159A

CN116955159A - APP detection system, method, electronic device and storage medium

Info

Publication number: CN116955159A
Application number: CN202310769267.6A
Authority: CN
Inventors: 贺晓阳; 徐迎迎
Original assignee: Du Xiaoman Technology Beijing Co Ltd
Current assignee: Du Xiaoman Technology Beijing Co Ltd
Priority date: 2023-06-27
Filing date: 2023-06-27
Publication date: 2023-10-27

Abstract

The application provides an APP detection system, an APP detection method, electronic equipment and a storage medium. The automated test platform is used for capturing APP page screenshots. The risk strategy analysis platform is used for calculating similarity values of risk images in the APP page screenshot and the preset risk image set, and outputting the similarity values larger than a preset threshold value and corresponding risk images; the method and the device are also used for extracting the characters of the APP page screenshot, matching the characters of the page screenshot with risk vocabularies in a preset risk dictionary, and outputting the successfully matched risk vocabularies, can monitor specific content displayed by the APP, analyze whether the risk exists in the APP display content, and improve the working efficiency compared with manual detection.

Description

APP detection system, method, electronic device and storage medium

Technical Field

The present application relates to the field of testing technologies, and in particular, to an APP detection system, a method, an electronic device, and a storage medium.

Background

At present, monitoring of the APP is focused on collapse rate, performance, service accessibility rate and the like, and effective technology or means for monitoring the APP specific display content are lacked.

Disclosure of Invention

In view of this, the embodiments of the present application provide an APP detection system, method, electronic device, and storage medium, so as to monitor the specific content displayed by APP.

According to an aspect of the present application, there is provided an APP detection system comprising:

the automatic test platform is used for capturing APP page screenshots;

the risk strategy analysis platform is used for calculating similarity values of the APP page screenshot and risk images in a preset risk image set and outputting similarity values larger than a preset threshold value and corresponding risk images; and the method is also used for extracting the characters of the APP page screenshot, matching the characters of the page screenshot with risk vocabularies in a preset risk dictionary, and outputting successfully matched risk vocabularies.

Preferably, calculating the similarity value between the APP screenshot and the risk image in the preset risk image set specifically includes:

inputting the APP webpage screenshot into a preset image recognition model to obtain a similarity value of the APP webpage screenshot and a risk image in a preset risk image set; the preset image recognition model is obtained by training a pad deep learning framework.

Preferably, the method further comprises:

the management platform is used for generating a detection task starting instruction; the automatic test platform is used for starting detection of the APP according to the detection task starting instruction.

Preferably, the management platform is further used for receiving an installation package of the APP transmitted by the pipeline, and installing the installation package of the APP to a preset physical device; the detection task starting instruction is generated after the installation of the installation package is completed; and the automatic test platform starts detection of the APP transmitted by the pipeline according to the detection task starting instruction.

Preferably, the management platform is further used for setting a timing inspection task and generating the detection task starting instruction when the timing condition is met; and the automatic test platform starts detection of all online versions of APP according to the detection task starting instruction.

Preferably, the automated test platform is used for starting an APP according to the detection task starting instruction, and operating the APP for detection according to a preset detection case; the automated test platform is also used for capturing the APP page screenshot in the detection process.

Preferably, the automated test platform is further configured to output a detection report according to a detection result, and construct an APP status portrait according to the detection report.

According to another aspect of the present application, there is provided an APP detection method comprising the steps of:

capturing an APP page screenshot;

calculating similarity values of the APP page screenshot and risk images in the risk image set, and outputting the similarity values larger than a preset threshold value and corresponding risk images; and extracting the characters of the APP page screenshot, matching the characters of the page screenshot with risk vocabularies in a risk dictionary, and outputting successfully matched risk vocabularies.

According to another aspect of the present application, there is provided an electronic apparatus including:

a processor; and

a memory in which a program is stored,

wherein the program comprises instructions which, when executed by the processor, cause the processor to perform the method described above.

According to another aspect of the present application, there is provided a non-transitory computer-readable storage medium storing computer instructions for causing a computer to perform the above-described method.

The one or more technical schemes provided by the embodiment of the application have the beneficial effects that:

(1) In the prior art, monitoring means for APP display content is absent, and the embodiment can monitor specific content displayed by the APP;

(2) At present, the mobile terminal App mostly adopts Hybrid technology, the content displayed by the H5 page has variability, the original page also mostly supports dynamic configuration, and the examination of the content of each version of App depends on manpower. According to the embodiment, an automatic test technology can be utilized to simulate manual clicking, and multiple versions can be inspected at the same time, so that manpower is saved, and the working efficiency is improved;

(3) The prior art detects through the manual work, and long-time detection can be higher because of tired error. According to the method, the display content of the APP is monitored by utilizing an image recognition technology, whether the display content has risks or not is analyzed, and compared with manual detection, the method is more accurate;

(4) The prior art relies on the manpower to detect regularly, and the frequency is less, can't discover the problem in the first time, and unsuitable content probably has been shown for a long time for some users, is difficult to accomplish in time to stop the damage. The embodiment can automatically detect the APP content at regular time or periodically, and can discover problems earlier, so that developers can process the problems in time, customer complaints and cost losses are avoided, and the control degree of the APP content quality is improved;

(5) The prior art can not generate a state image, and is difficult to trace the change history of App content; according to the embodiment, the detection report generated by each detection can be recorded, and the state portrait of the APP is constructed according to the detection report, so that the traceability of the APP change history is realized.

Drawings

Further details, features and advantages of the application are disclosed in the following description of exemplary embodiments with reference to the following drawings, in which:

FIG. 1 shows a schematic diagram of an APP detection system according to an exemplary embodiment of the present application;

FIG. 2 shows another schematic structural diagram of an APP detection system according to an exemplary embodiment of the present application;

FIG. 3 shows a schematic workflow diagram of an APP detection system according to an exemplary embodiment of the present application;

FIG. 4 shows another workflow diagram of an APP detection system according to an exemplary embodiment of the present application;

FIG. 5 shows a flow diagram of an APP detection method according to an exemplary embodiment of the present application;

fig. 6 shows a block diagram of an exemplary electronic device that can be used to implement an embodiment of the application.

Detailed Description

Embodiments of the present application will be described in more detail below with reference to the accompanying drawings. While the application is susceptible of embodiment in the drawings, it is to be understood that the application may be embodied in various forms and should not be construed as limited to the embodiments set forth herein, but rather are provided to provide a more thorough and complete understanding of the application. It should be understood that the drawings and embodiments of the application are for illustration purposes only and are not intended to limit the scope of the present application.

It should be understood that the various steps recited in the method embodiments of the present application may be performed in a different order and/or performed in parallel. Furthermore, method embodiments may include additional steps and/or omit performing the illustrated steps. The scope of the application is not limited in this respect.

The term "including" and variations thereof as used herein are intended to be open-ended, i.e., including, but not limited to. The term "based on" is based at least in part on. The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments. Related definitions of other terms will be given in the description below. It should be noted that the terms "first," "second," and the like herein are merely used for distinguishing between different devices, modules, or units and not for limiting the order or interdependence of the functions performed by such devices, modules, or units.

It should be noted that references to "one", "a plurality" and "a plurality" in this disclosure are intended to be illustrative rather than limiting, and those skilled in the art will appreciate that "one or more" is intended to be construed as "one or more" unless the context clearly indicates otherwise.

The names of messages or information interacted between the devices in the embodiments of the present application are for illustrative purposes only and are not intended to limit the scope of such messages or information.

The following describes the solution of the present application with reference to the accompanying drawings, and as shown in fig. 1, an exemplary embodiment of the present application provides an APP detection system 1100, including an automated test platform 1101 and a risk policy analysis platform 1102. Wherein automated test platform 1101 is used to capture APP page shots. The risk policy analysis platform 1102 is used for calculating similarity values of risk images in the APP page screenshot and the preset risk image set, and outputting similarity values larger than a preset threshold value and corresponding risk images; the risk policy analysis platform 1102 is further configured to extract characters of the APP page screenshot, match the characters of the page screenshot with risk vocabularies in a preset risk dictionary, and output successfully matched risk vocabularies.

Wherein APP is an abbreviation of Application, namely an Application program. The mobile phone APP refers to software and application installed on the smart phone, and the APP can perfect the defect of an original system and provide richer and personalized use experience for users. The operation of the mobile phone software needs to have a corresponding mobile phone system, and the main mobile phone systems include iOS of apple company, android (Android) system of google company, a Seban platform, a Microsoft platform and the like. Automated testing (Automated Testing) refers to the automated execution of test tasks using computer programs and software tools to quickly, efficiently, and accurately test the functionality and performance of software. Automated testing typically involves a tester writing test scripts and then automatically performing test tasks, including unit testing, integration testing, system testing, etc., using software tools.

In this embodiment, the automated test platform 1101 may capture the APP page shots at preset time intervals according to actual needs. The risk policy analysis platform 1102 presets a risk policy set, which is used to determine whether the input image has risk, is a sample set, and supports manual maintenance. The risk policy set comprises a preset risk image set and a preset risk dictionary. The preset risk image set includes a plurality of pre-stored images. The risk policy analysis platform 1102 includes a set of trained image classification models and configurations, and after an image is received, a similarity value of the image and a risk image in a preset risk image set can be calculated and given, where the higher the similarity value, the higher the score. The magnitude of the preset threshold value can be set according to actual conditions.

The preset risk dictionary comprises a plurality of pre-stored risk vocabularies such as bare credits, bad record elimination and the like. After extracting the characters of the APP page screenshot, the risk policy analysis platform 1102 matches the extracted characters with risk words in a preset risk dictionary according to a preset character matching algorithm, and outputs successfully matched risk words. The preset text matching algorithm can adopt the existing algorithm.

In this embodiment, the risk policy analysis platform 1102 automatically monitors the specific content displayed by the APP by calculating the APP page screenshot similarity value and text matching.

At present, the mobile terminal App mostly adopts Hybrid technology, the content displayed by the H5 page has variability, the original page also mostly supports dynamic configuration, and the examination of the content of each version of App depends on manpower. The embodiment reduces labor cost, improves working efficiency, and can discover problems earlier so that developers can process the problems in time. The embodiment can also utilize an automatic test technology to simulate manual clicking, thereby saving manpower. The prior art detects through the manual work, and long-time detection can lead to the error rate higher because of tired. According to the application, the display content of the APP is monitored by using the image recognition technology, and whether the display content has risks or not is analyzed, so that the method is more accurate compared with manual detection.

In some embodiments, calculating the similarity value of the APP page screenshot and the risk image in the preset risk image set specifically includes:

inputting the APP webpage screenshot into a preset image recognition model to obtain a similarity value of the APP webpage screenshot and a risk image in a preset risk image set. The preset image recognition model is obtained through training of a pad deep learning framework.

Image recognition is a computer technology used to identify the identity or characteristics of a human or object through an image. It can be applied to many fields such as face recognition, vehicle recognition, biometric recognition, handwritten number recognition, etc. Image recognition is typically based on machine learning and deep learning algorithms that automatically extract features from the image and classify or identify based on the features. Image recognition may use techniques such as image processing, image segmentation, feature extraction, model training, and model evaluation, and in this embodiment, the similarity value is calculated by the image recognition technique.

In the embodiment, the similarity value is calculated by adopting the image recognition model based on the pad deep learning frame, so that the accuracy is improved.

In some embodiments, APP detection system 1100 further comprises management platform 1103. The management platform 1103 is used for generating a detection task starting instruction, and the automated testing platform 1101 is used for starting detection of the APP according to the detection task starting instruction.

As an implementation manner, the management platform 1103 is further configured to receive an installation package of the APP transmitted by the pipeline, install the installation package of the APP to the preset physical device, and generate a detection task start instruction after the installation of the installation package of the APP is completed.

As an example, as shown in fig. 2 and 3, the steps of the APP detection system operation include:

a) The assembly line outputs an APP installation package which is quasi-online;

b) The pipeline transmits the installation package to the management platform 1103;

c) The management platform 1103 installs the installation package to the physical device; the physical equipment comprises a mobile phone, a computer, a tablet and the like;

d) The management platform 1103 starts a content detection task for the APP version;

e) The management platform 1103 informs the automated test platform 1101 to start an automated detection task for the APP; the method comprises the following steps: the management platform 1103 generates a detection task starting instruction, and the automatic test platform 1101 starts detection of the APP according to the detection task starting instruction;

f) The automated test platform 1101 starts the APP installed on the physical device and operates the APP according to the specified detection case;

g) The automated testing platform 1101 continuously reports the APP page screenshot captured during the detection process to the risk policy analysis platform 1102;

h) The risk policy analysis platform 1102 analyzes the screenshot content of the page, specifically including: calculating similarity values of risk images in the APP page screenshot and the preset risk image set, and outputting the similarity values larger than a preset threshold value and corresponding risk images; extracting characters of the APP page screenshot, matching the characters of the page screenshot with risk vocabularies in a preset risk dictionary, and outputting successfully matched risk vocabularies;

i) When the risk is identified by the risk policy analysis platform 1102, recording the risk; when the similarity value is larger than a threshold value or the text matching is successful, judging that the risk exists;

j) The automated test platform 1101 judges whether the detection is finished, if not, repeating steps g) to i); if yes, the risk policy platform is informed to generate a detection report of the current detection, and an APP state portrait is constructed according to the detection report. The detection report comprises an APP page screenshot, wherein the APP page screenshot comprises a screenshot of a key frame, and a state portrait of the APP can be generated according to the screenshot of the key frame;

k) The risk policy platform informs relevant personnel of the detection report;

l) related personnel carry out rectification and start a new detection of the content of the APP aligned to the online through the management platform 1103;

m) waiting for a detection report of the next detection output to confirm that the risk treatment is finished.

The method and the device can be used for checking risks of APP content on line and are also suitable for detecting appointed versions of APP. The method can record the detection report generated by the quasi-online APP or the appointed version APP in each detection, wherein the detection report comprises the screenshot of the key frame, and the state portrait of the APP can be generated according to the screenshot of the key frame, so that the quasi-online APP or the appointed version APP change history can be traced.

As another embodiment, the management platform 1103 is further configured to set a timing inspection task, and generate a detection task start instruction when a timing condition is satisfied.

As an example, as shown in fig. 4, the APP detection system operates as follows:

a) The management platform 1103 sets a timing or periodic inspection task;

b) After the regular or periodic inspection conditions are met, the management platform 1103 starts an inspection task;

c) The management platform 1103 starts the APP content detection tasks for all online versions and generates detection task starting instructions;

d) The automatic test platform 1101 starts the detection of the APP according to the detection task starting instruction, starts the APP installed on the physical equipment concurrently, and operates the APP according to the appointed detection case;

e) The automated testing platform 1101 continuously reports the APP page screenshot captured during the detection process to the risk policy analysis platform 1102;

f) The risk policy analysis platform 1102 analyzes the screenshot content of the page, specifically including: calculating similarity values of risk images in the APP page screenshot and the preset risk image set, and outputting the similarity values larger than a preset threshold value and corresponding risk images; extracting characters of the APP page screenshot, matching the characters of the page screenshot with risk vocabularies in a preset risk dictionary, and outputting successfully matched risk vocabularies;

g) When the risk is identified by the risk policy analysis platform 1102, recording the risk; when the similarity value is larger than a threshold value or the text matching is successful, judging that the risk exists;

h) The automated test platform 1101 judges whether the detection is finished, if not, the steps e) to g) are repeated; if yes, the risk policy platform is informed to generate a detection report of the current detection, and an APP state portrait is constructed according to the detection report. The detection report comprises an APP page screenshot, wherein the APP page screenshot comprises a screenshot of a key frame, and a state portrait of the APP can be generated according to the screenshot of the key frame;

i) The risk policy platform informs relevant personnel of the detection report;

j) If the risk problem exists, the related personnel complete the modification, and a new detection is started for the content of the modified online version APP through the management platform 1103.

The content detection method and the device can detect the content of all online versions regularly or regularly, can discover potential risks in time, and avoid customer complaints and cost losses. This example can patrol and examine a plurality of versions simultaneously, uses manpower sparingly. The method and the device can record the detection report generated by the on-line version of the APP during each detection, the detection report comprises the screenshot of the key frame, and the state portrait of the APP can be generated according to the screenshot of the key frame, so that the traceability of the version change history of the APP on line is realized.

The exemplary embodiment of the present application also provides an APP detection method, as shown in fig. 5, including the steps of:

s1, capturing APP page screenshots;

s2, calculating similarity values of the APP page screenshot and risk images in the risk image set, and outputting the similarity values larger than a preset threshold value and corresponding risk images; and extracting characters of the APP page screenshot, matching the characters of the page screenshot with risk vocabularies in a risk dictionary, and outputting successfully matched risk vocabularies.

The calculating of the similarity value of the APP screenshot and the risk image in the preset risk image set specifically comprises the following steps:

inputting the APP webpage screenshot into a preset image recognition model to obtain a similarity value of the APP webpage screenshot and a risk image in a preset risk image set; the preset image recognition model is obtained by training a Paddle deep learning framework.

In some embodiments, the APP detection method further comprises:

receiving an APP installation package transmitted by a pipeline, and installing the APP installation package to a preset physical device;

after the installation of the installation package is completed, the detection of the APP for the pipeline transmission is started.

In other embodiments, the APP detection method further comprises:

and setting a timing inspection task, and starting detection of all online versions of APP when a timing condition is met.

In still other embodiments, the APP detection method further comprises:

starting an APP, and operating the APP for detection according to a preset detection case;

capturing APP page screenshot in the detection process.

In still other embodiments, the APP detection method further comprises:

outputting a detection report according to the detection result, and constructing an APP state portrait according to the detection report.

The exemplary embodiment of the application also provides an electronic device, including: at least one processor; and a memory communicatively coupled to the at least one processor. The memory stores a computer program executable by the at least one processor for causing the electronic device to perform a method according to an embodiment of the application when executed by the at least one processor.

The exemplary embodiments of the present application also provide a non-transitory computer readable storage medium storing a computer program, wherein the computer program, when executed by a processor of a computer, is for causing the computer to perform a method according to an embodiment of the present application.

The exemplary embodiments of the application also provide a computer program product comprising a computer program, wherein the computer program, when being executed by a processor of a computer, is for causing the computer to perform a method according to an embodiment of the application.

Referring to fig. 6, a block diagram of an electronic device 1200 that may be a server or a client of the present application will now be described, which is an example of a hardware device that may be applied to aspects of the present application. Electronic devices are intended to represent various forms of digital electronic computer devices, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the applications described and/or claimed herein.

As shown in fig. 6, the electronic device 1200 includes a computing unit 1201 that can perform various appropriate actions and processes according to a computer program stored in a Read Only Memory (ROM) 1202 or a computer program loaded from a storage unit 1208 into a Random Access Memory (RAM) 1203. In the RAM 1203, various programs and data required for the operation of the electronic device 1200 may also be stored. The computing unit 1201, the ROM 1202, and the RAM 1203 are connected to each other via a bus 1204. An input/output (I/O) interface 1205 is also connected to the bus 1204.

Various components in the electronic device 1200 are connected to the I/O interface 1205, including: an input unit 1206, an output unit 1207, a storage unit 1208, and a communication unit 1209. The input unit 1206 may be any type of device capable of inputting information to the electronic device 1200, and the input unit 1206 may receive input numeric or character information and generate key signal inputs related to user settings and/or function controls of the electronic device. The output unit 1207 may be any type of device capable of presenting information, and may include, but is not limited to, a display, speakers, video/audio output terminals, vibrators, and/or printers. Storage unit 1208 may include, but is not limited to, magnetic disks, optical disks. The communication unit 1209 allows the electronic device 1200 to exchange information/data with other devices over computer networks, such as the internet, and/or various telecommunications networks, and may include, but is not limited to, modems, network cards, infrared communication devices, wireless communication transceivers and/or chipsets, such as bluetooth (TM) devices, wiFi devices, wiMax devices, cellular communication devices, and/or the like.

The computing unit 1201 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of computing unit 1201 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital Signal Processors (DSPs), and any suitable processor, controller, microcontroller, etc. The computing unit 1201 performs the various methods and processes described above. For example, in some embodiments, the APP detection method may be implemented as a computer software program tangibly embodied on a machine-readable medium, such as storage unit 1208. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 1200 via the ROM 1202 and/or the communication unit 1209. In some embodiments, the computing unit 1201 may be configured to perform the APP detection method by any other suitable means (e.g., by means of firmware).

Program code for carrying out methods of the present application may be written in any combination of one or more programming languages. These program code may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus such that the program code, when executed by the processor or controller, causes the functions/operations specified in the flowchart and/or block diagram to be implemented. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.

In the context of the present application, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, apparatus, and/or device (e.g., magnetic discs, optical disks, memory, programmable Logic Devices (PLDs)) used to provide machine instructions and/or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term "machine-readable signal" refers to any signal used to provide machine instructions and/or data to a programmable processor.

To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and pointing device (e.g., a mouse or trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.

The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), and the internet.

The computer system may include a client and a server. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.

Claims

1. An APP detection system, comprising:

the automatic test platform is used for capturing APP page screenshots;

2. The APP detection system of claim 1, wherein the calculating a similarity value of the APP page screenshot and a risk image in a preset risk image set specifically comprises:

3. The APP detection system of claim 1 further comprising:

4. The APP detection system of claim 3, wherein,

the management platform is also used for receiving an APP installation package transmitted by the pipeline and installing the APP installation package to a preset physical device; the detection task starting instruction is generated after the installation of the installation package is completed; and the automatic test platform starts detection of the APP transmitted by the pipeline according to the detection task starting instruction.

5. The APP detection system of claim 3, wherein,

the management platform is also used for setting a timing inspection task and generating a detection task starting instruction when the timing condition is met; and the automatic test platform starts detection of all online versions of APP according to the detection task starting instruction.

6. The APP detection system of claim 4 or 5, characterized in that,

the automatic test platform is used for starting the APP according to the detection task starting instruction and operating the APP for detection according to a preset detection case; the automated test platform is also used for capturing the APP page screenshot in the detection process.

7. The APP detection system of claim 6, wherein,

the automatic test platform is also used for outputting a detection report according to the detection result and constructing an APP state portrait according to the detection report.

8. An APP detection method, comprising the steps of:

capturing an APP page screenshot;

9. An electronic device, comprising:

a processor; and

a memory in which a program is stored,

wherein the program comprises instructions which, when executed by the processor, cause the processor to perform the method of claim 8.

10. A non-transitory computer readable storage medium storing computer instructions, wherein the computer instructions are for causing a computer to perform the method of claim 8.