Method, apparatus, device, and readable medium for determining an operating path

Abstract

Embodiments of the present disclosure provide a method, device, equipment and readable medium for determining an operation path. The method comprises: determining association information corresponding to a set of interactive elements of a target page; performing an arrangement operation on at least part of the set of interactive elements based on the association information to determine a respective arrangement order associated with at least two interactive elements of the at least part of the set of interactive elements; determining a respective probability that the at least two interactive elements are arranged in the respective arrangement order by using a trained machine learning model; and determining at least one operation path associated with the set of interactive elements based on the respective probability, the at least two interactive elements and the respective arrangement order. In this way, the efficiency of determining the operation path can be improved.

Description

Technical Field

[0001] The exemplary embodiments of this disclosure generally relate to the field of computer technology, and more specifically, to methods, apparatus, devices, and computer-readable storage media for determining operation paths. Background Technology

[0002] On the page, interactive elements can be triggered by corresponding interactive actions. Furthermore, interactive action paths can be generated by arranging the identified interactive elements in a predetermined order. Summary of the Invention

[0003] In a first aspect of this disclosure, a method for determining an operation path is provided. The method includes: determining association information corresponding to a set of interactive elements of a target page; performing a permutation operation on at least a portion of the interactive elements in the set based on the association information to determine a corresponding permutation order associated with at least two interactive elements in the set; determining a corresponding probability of the at least two interactive elements being arranged in the corresponding permutation order using a trained machine learning model; and determining at least one operation path associated with the set of interactive elements based on the corresponding probability, the at least two interactive elements, and the corresponding permutation order.

[0004] In a second aspect of this disclosure, an apparatus for determining an operation path is provided. The apparatus includes: a determining module configured to determine association information corresponding to a set of interactive elements of a target page; an arranging module configured to perform an arranging operation on at least a portion of the interactive elements in the set of interactive elements based on the association information, to determine a corresponding arrangement order associated with at least two interactive elements in the set of interactive elements; a utilizing module configured to utilize a trained machine learning model to determine a corresponding probability that the at least two interactive elements are arranged in the corresponding arrangement order; and an execution module configured to determine at least one operation path associated with the set of interactive elements based on the corresponding probability, the at least two interactive elements, and the corresponding arrangement order.

[0005] In a third aspect of this disclosure, an electronic device is provided. The electronic device includes at least one processing unit; and at least one memory coupled to the at least one processing unit and storing instructions for execution by the at least one processing unit, the instructions causing the electronic device to perform the method of the first aspect of this disclosure when executed by the at least one processing unit.

[0006] In a fourth aspect of this disclosure, a computer-readable storage medium is provided. This computer-readable storage medium stores a computer program that can be executed by a processor to perform the method according to a first aspect of this disclosure.

[0007] In a fifth aspect of this disclosure, a computer program product is provided, including computer-executable instructions, wherein the computer-executable instructions can be executed by a processor to perform a method according to a first aspect of this disclosure.

[0008] It should be understood that the description in the Summary of the Invention section is not intended to limit the key or essential features of the embodiments of this disclosure, nor is it intended to restrict the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0009] The above and other features, advantages, and aspects of various implementations of this disclosure will become more apparent in the following detailed description, taken in conjunction with the accompanying drawings. In the drawings, the same or similar reference numerals denote the same or similar elements, wherein:

[0010] Figure 1 A schematic diagram of an example environment in which embodiments of the present disclosure can be implemented is shown;

[0011] Figure 2 A flowchart illustrating a process for determining an operation path according to some embodiments of the present disclosure is shown;

[0012] Figure 3 A schematic structural block diagram of an apparatus for determining an operation path according to certain embodiments of the present disclosure is shown; and

[0013] Figure 4 A block diagram of an electronic device in which one or more embodiments of the present disclosure may be implemented is shown. Detailed Implementation

[0014] Embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this disclosure. It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.

[0015] In the description of embodiments of this disclosure, the term "comprising" and similar terms should be understood as open-ended inclusion, i.e., "including but not limited to". The term "based on" should be understood as "at least partially based on". The term "one embodiment" or "the embodiment" should be understood as "at least one embodiment". The term "some embodiments" should be understood as "at least some embodiments". Other explicit and implicit definitions may also be included below.

[0016] It should be noted that the acquisition, storage, and application of user personal information involved in the technical solution disclosed herein comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0017] It is understood that before using the technical solutions disclosed in the various embodiments of this disclosure, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in this disclosure through appropriate means in accordance with relevant laws and regulations, and user authorization should be obtained.

[0018] For example, in response to receiving a user's active request, a prompt message is sent to the user to clearly inform the user that the requested operation will require the acquisition and use of the user's personal information, thereby enabling the user to choose whether to provide personal information to the software or hardware such as electronic devices, applications, servers or storage media that perform the operation of the technical solution disclosed herein, based on the prompt message.

[0019] As an optional but non-restrictive implementation, in response to a user's active request, a prompt message can be sent to the user, for example, via a pop-up window, where the prompt message can be presented in text format. Furthermore, the pop-up window can also include a selection control allowing the user to choose whether to "agree" or "disagree" to provide personal information to the electronic device.

[0020] As used in this paper, the term "model" refers to a model that learns the relationship between inputs and outputs from training data, enabling it to generate corresponding outputs for a given input after training. Model generation can be based on machine learning techniques. Deep learning is a machine learning algorithm that processes inputs and provides corresponding outputs using multiple layers of processing units. A neural network model is an example of a deep learning-based model. In this paper, "model" may also be referred to as a "machine learning model," "learning model," "machine learning network," or "learning network," and these terms are used interchangeably.

[0021] A neural network is a machine learning network based on deep learning. A neural network processes input and provides a corresponding output, typically consisting of an input layer, an output layer, and one or more hidden layers between the input and output layers. Neural networks used in deep learning applications often include many hidden layers, thus increasing the network's depth. The layers of a neural network are connected sequentially, so that the output of the previous layer is provided as the input to the next layer. The input layer receives the input to the neural network, while the output layer's output serves as the final output. Each layer of a neural network includes one or more elements (also called processing elements or neurons), each of which processes the input from the layer above.

[0022] Machine learning typically comprises three phases: training, testing, and application (also known as inference). In the training phase, a given model is trained using a large amount of training data, iteratively updating parameter values ​​until the model can consistently generate inferences that meet the expected goals from the training data. Through training, the model can be considered to have learned the relationship between inputs and outputs (also known as an input-output mapping) from the training data. The parameter values ​​of the trained model are determined. In the testing phase, test inputs are applied to the trained model to test whether it can provide the correct output, thus determining the model's performance. The testing phase can sometimes be integrated into the training phase. In the application or inference phase, the trained model can be used to process actual model inputs based on the trained parameter values ​​to determine the corresponding model output.

[0023] On a webpage, interactive elements can be triggered by corresponding interactive actions. Traditionally, methods for generating action paths based on identified interactive elements typically employ the following approaches: The first is manual input. This method is too costly due to the need for manual intervention. The second approach is to perform a full permutation of all interactive elements according to a preset length. For example, if 100 interactive elements are identified and the action length is set to 5, the result of the full permutation will be 100. 5 This method uses a full permutation approach. However, when processing pages with multiple nodes, the full permutation approach results in a longer generation operation length and a longer operation path time.

[0024] In view of this, embodiments of the present disclosure provide a scheme for determining an operation path. The scheme includes: determining association information corresponding to a set of interactive elements on a target page; based on the association information, performing a permutation operation on at least a portion of the interactive elements in the set to determine a corresponding permutation order associated with at least two interactive elements in the set; then, using a trained machine learning model to determine the corresponding probability of the at least two interactive elements being arranged in the corresponding permutation order. Subsequently, based on the corresponding probabilities, the at least two interactive elements, and the corresponding permutation order, at least one operation path associated with the set of interactive elements is determined. In this manner, the efficiency of determining the operation path can be improved.

[0025] Figure 1 A schematic diagram of an example environment 100 in which embodiments of the present disclosure can be implemented is shown.

[0026] like Figure 1As shown, terminal device 110 communicates with server 120 to generate at least one operation path associated with a set of interactive elements (hereinafter also referred to as elements) determined from page 130. In some embodiments, the operation path may indicate a path of interactive operations corresponding to a set of interactive elements. In some examples, page 130 may be a target page for generating test cases.

[0027] Terminal device 110 can be any type of mobile terminal, fixed terminal, or portable terminal, including mobile phones, desktop computers, laptop computers, notebook computers, netbook computers, tablet computers, media computers, multimedia tablets, personal communication system (PCS) devices, personal navigation devices, personal digital assistants (PDAs), audio / video players, digital cameras / camcorders, positioning devices, television receivers, radio receivers, e-book devices, gaming devices, or any combination thereof, including accessories and peripherals of these devices or any combination thereof. In some embodiments, terminal device 110 may also support any type of user-facing interface (such as "wearable" circuitry). Server 120 can be any type of computing system / server capable of providing computing power, including but not limited to mainframes, edge computing nodes, computing devices in cloud environments, etc.

[0028] It should be understood that the structure and function of the various elements in environment 100 are described for illustrative purposes only and do not imply any limitation on the scope of this disclosure.

[0029] The following description will continue with reference to the accompanying drawings, which will provide some exemplary embodiments of this disclosure. It should be understood that, in the following description, the exemplary embodiments will be described primarily with respect to terminal device 110. It should be understood that the actions described with respect to terminal device 110 can be performed by terminal device 110 in conjunction with its server (e.g., server 120).

[0030] Figure 2 A flowchart of a process 200 for determining an operation path according to some embodiments of the present disclosure is shown. Process 200 can be implemented at terminal device 110. For ease of discussion, reference will be made to... Figure 1 The environment 100 is used to describe the process 200.

[0031] In box 210, terminal device 110 determines the association information corresponding to a set of interactive elements of the target page.

[0032] In some embodiments, terminal device 110 may determine at least one interactive element from a target page. Then, based on the at least one interactive element, a set of interactive elements is determined. In some examples, interactive elements may indicate at least one of a link element, button element, form element, tag, button, input box, selector, or textarea output box. In other examples, interactive elements may also be associated with interactive events. Specifically, interactive events may include at least one of a click event, hover event, scroll event, drag event, or focus event. In many more examples, terminal device 110 may determine interactive elements from the target page that match a predetermined selector group used to select interactive elements of a predetermined style on the target page. In some examples, such interactive elements may indicate at least one of a link, button, form element, tag, button, input box, selector, or textarea output box. In some examples, the "Document.querySelectorAll()" method can return a list of elements in the document that match a specified selector group. That is, the "Document.querySelectorAll()" method can be used to determine the first set of candidate elements.

[0033] In some embodiments, the associated information may indicate at least one of the following: interactive action type, element text, element display state, element attributes, and predefined identifier. In some examples, the interactive action type may indicate at least one of click, drag, hover, and open input. In some examples, the element text may indicate the content (innerText) of the text rendered by the element and / or its child elements. In other examples, the element display state may indicate either a display state or a visibility state. In some examples, the display state may indicate that the element, its child elements, and their corresponding element layout are displayed, such as a flow layout, grid layout, or flex layout. In other examples, the visibility state may indicate that the element is hidden without changing its layout.

[0034] In other examples, the predefined identifier can indicate that an element is an open input class, such as an input (form input) element, a textarea element, etc. In still other examples, the target page can indicate the page used to generate test cases.

[0035] In other examples, a set of interactive elements may include at least one combination of elements from the target page. Specifically, each combination of elements includes at least one element. It is to be understood that this is merely exemplary and not restrictive.

[0036] In box 220, terminal device 110 performs a permutation operation on at least a portion of the interactive elements in a set of interactive elements based on association information, in order to determine the corresponding permutation order associated with at least two interactive elements in the at least a portion of the interactive elements.

[0037] In some embodiments, if the associated information indicates that at least some of the interactive elements are one-time interactive elements, the terminal device 110 can determine the operation path corresponding to at least some of the interactive elements based on the operation path generation rules. In some examples, at least some interactive elements may indicate a combination of elements on the target page. In some examples, one-time interactive elements may indicate an tag element. Elements carrying such identifiers may not participate in the arrangement process of other nodes, and use cases may be generated. In some examples, the operation path generated for one-time interactive elements may indicate "Access page - Click tag - (assert) The jump link is correct". It should be understood that this is merely exemplary and not restrictive.

[0038] In some embodiments, if the associated information indicates that at least some of the interactive elements are non-one-time interactive elements, the terminal device 110 can perform an arrangement operation on at least some of the interactive elements. In some examples, it is assumed that a set of interactive elements includes element S, element T, and element L. Further, the terminal device 110 can perform an arrangement on elements S and element T, and the above two types of interactive elements may be arranged in two ways: element S-element T and element T-element S. It should be understood that this is merely exemplary and not restrictive.

[0039] In box 230, terminal device 110 uses a trained machine learning model to determine the probability of at least two interactive elements being arranged in a corresponding permutation order.

[0040] In some embodiments, the machine learning model can be determined as follows. Specifically, the terminal device 110 can determine a set of candidate interactive elements and corresponding candidate association information from the page used for model training. In other examples, a set of candidate interactive elements may include at least one combination of candidate interactive elements of the page. Specifically, each combination of candidate interactive elements may include at least one candidate interactive element. In some examples, the candidate association information may indicate at least one of the following: interactive operation type, element text, element display state, element attributes, and predetermined identifier.

[0041] Further, the terminal device 110 determines a candidate arrangement order corresponding to at least two candidate interactive elements from a set of candidate interactive elements. In some examples, a candidate arrangement order corresponding to each combination of candidate interactive elements can be determined. Further, at least one final candidate interactive element combination can be determined through manual screening to serve as sample data for training a machine learning model. Then, at least one first combination is determined from the aforementioned multiple candidate interactive element combinations, wherein the candidate interactive elements in the at least one first combination are arranged according to the candidate arrangement order.

[0042] Terminal device 110 determines the probability of at least two candidate interactive elements being arranged in candidate order based on candidate association information. In some embodiments, terminal device 110 may determine candidate feature matrices corresponding to at least two candidate interactive elements based on the candidate association information. Then, terminal device 110 may determine the probability based on the candidate feature matrices. In some examples, the probability corresponding to at least one of the above-described first combinations may be determined. It is understood that this is merely exemplary and not limiting.

[0043] In some examples, the attribute data of interactive elements can be preprocessed. First, the vectorized element attributes of the interactive elements are determined. Specifically, the preferred approach is to convert the text data corresponding to the element into a numerical vector using methods such as Bag of Words, TF-IDF, or embedding techniques (e.g., Word2Vec). Further, the category information corresponding to the element can be determined. Specifically, the category information can be converted into numerical values ​​using other methods such as one-hot encoding or label encoding. In some examples, the category information can indicate the operation type, etc. In other examples, each category can be represented by a number; assuming the category information indicates the operation type, 1 can represent "click," 2 represents "input," and so on. This is merely exemplary and not restrictive. Further, a feature matrix can be constructed. In some embodiments, the row data in the candidate feature matrix can correspond to candidate interactive elements, and the column data can correspond to element attributes.

[0044] Furthermore, the terminal device 110 can use the aforementioned sample data to train a machine learning model. The input to the training set is a set of vectorized elements, and the output is a set of vectorized elements arranged in a predetermined order, representing predicted probabilities. In some examples, assuming elements M and N are provided to the machine learning model, the model can output the predicted probability that M is followed by N. In other examples, assuming elements N and M are provided to the model, the model can output the predicted probability that N is followed by M. In still more examples, assuming elements M and N are provided to the model, the model can output both the probability that M is followed by N and the predicted probability that N is followed by M. It should be understood that this is merely exemplary and not restrictive.

[0045] In some embodiments, the terminal device 110 may provide at least two candidate interactive elements to a machine learning model to determine prediction probabilities. In some examples, the terminal device 110 may determine the order and probability of possible permutations and combinations of the aforementioned set of vectorized elements. Then, based on the probabilities and prediction probabilities, the terminal device 110 trains the machine learning model to determine the machine learning model.

[0046] In box 240, terminal device 110 determines at least one operation path associated with a set of interactive elements based on corresponding probabilities, at least two interactive elements, and corresponding arrangement order.

[0047] In some embodiments, the terminal device 110 executes interactive operations corresponding to each interactive element based on a corresponding arrangement order. If an interactive operation receives a response or the number of operations is less than a threshold, the terminal device 110 determines at least one operation path. Then, the interactive operations corresponding to each group of candidate interactive elements are executed sequentially until no echo change occurs after a page operation. In some examples, an echo change may indicate that no interactive page change occurs after the interactive operation is executed or that the number of executed interactive operations has reached the operation number threshold. In some examples, page changes may include, but are not limited to, page element changes, page screenshot changes, Uniform Resource Locator (URL) changes, and local storage changes. At least one group of candidate interactive elements is determined, such as (aa), (ab), and (ca).

[0048] Furthermore, the terminal device 110 can retain the order corresponding to probabilities greater than a probability threshold (e.g., 0.2). In some examples, assuming the probability threshold is 0.2, the probability of N appearing after M is 0.5, and the probability of M appearing after N is 0.1, then by comparing the probabilities with the probability threshold, the predetermined order of M and N can be determined as MN, and the corresponding operation path (e.g., operation path M'-N') can be generated. Even further, the terminal device 110 can also generate test cases for testing the target page based on the operation path. It should be understood that this is merely exemplary and not restrictive.

[0049] In summary, the embodiments of this disclosure can generate operation paths based on the interactive elements determined from the target page and their corresponding arrangement order. This method improves the efficiency and accuracy of determining operation paths and also generates unique operation paths.

[0050] Embodiments of this disclosure also provide corresponding apparatus for implementing the above methods or processes.

[0051] Figure 3 A schematic structural block diagram of an apparatus 300 for determining an operation path according to certain embodiments of the present disclosure is shown. The apparatus 300 may be implemented as or included in a terminal device 110. The various modules / components in the apparatus 300 may be implemented by hardware, software, firmware, or any combination thereof.

[0052] like Figure 3 As shown, the device 300 includes a determining module 310 configured to determine association information corresponding to a set of interactive elements of a target page; the device 300 also includes an arranging module 320 configured to perform an arranging operation on at least a portion of the interactive elements in the set of interactive elements based on the association information, to determine a corresponding arrangement order associated with at least two interactive elements in the set of interactive elements; the device 300 also includes a utilizing module 330 configured to utilize a trained machine learning model to determine the corresponding probability of at least two interactive elements being arranged in the corresponding arrangement order; the device 300 also includes an execution module 340 configured to determine at least one operation path associated with the set of interactive elements based on the corresponding probability, at least two interactive elements, and the corresponding arrangement order.

[0053] In some embodiments, the arrangement module 320 is further configured to perform an arrangement operation on at least a portion of the interactive elements in response to an association information indicating that at least a portion of the interactive elements are non-one-time interactive elements.

[0054] In some embodiments, the execution module 340 is further configured to execute interactive operations corresponding to each interactive element based on the corresponding arrangement order; and to determine at least one operation path in response to a response to an interactive operation or a number of operations being less than a number threshold.

[0055] In some embodiments, the machine learning model is determined as follows: a set of candidate interactive elements and corresponding candidate association information are determined from a page used for model training; a candidate arrangement order is determined corresponding to at least two candidate interactive elements in the set of candidate interactive elements; based on the candidate association information, the probability that the at least two candidate interactive elements are arranged in the candidate arrangement order is determined; the at least two candidate interactive elements are provided to the machine learning model to determine the prediction probability; and the machine learning model is trained based on the probability and the prediction probability to determine the machine learning model.

[0056] In some embodiments, the apparatus 300 further includes a probability determination module configured to determine, based on candidate association information, candidate feature matrices corresponding to at least two candidate interactive elements; and to determine probabilities based on the candidate feature matrices.

[0057] In some embodiments, the row data in the candidate feature matrix corresponds to candidate interactive elements, and the column data corresponds to element attributes.

[0058] In some embodiments, the associated information indicates at least one of the following: interactive operation type, element text, element display status, element attributes, and predefined identifier.

[0059] In some embodiments, the device 300 further includes a first execution module configured to determine an operation path corresponding to at least a portion of the interactive elements based on operation path generation rules in response to an association information indicating that at least a portion of the interactive elements are one-time interactive elements.

[0060] The units and / or modules included in device 300 can be implemented in various ways, including software, hardware, firmware, or any combination thereof. In some embodiments, one or more units and / or modules can be implemented using software and / or firmware, such as machine-executable instructions stored on a storage medium. In addition to or as an alternative to machine-executable instructions, some or all of the units and / or modules in device 300 can be implemented at least partially by one or more hardware logic components. By way of example and not limitation, exemplary types of hardware logic components that can be used include field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), complex programmable logic devices (CPLDs), and so on.

[0061] It should be understood that one or more steps in the above methods can be performed by suitable electronic devices or combinations of electronic devices. Such electronic devices or combinations of electronic devices may include, for example, […]. Figure 1 Terminal device 110 in the middle.

[0062] Figure 4 A block diagram of an electronic device 400 in which one or more embodiments of the present disclosure may be implemented is shown. It should be understood that... Figure 4 The electronic device 400 shown is merely exemplary and should not be construed as limiting the functionality and scope of the embodiments described herein. Figure 4 The electronic device 400 shown can be used to achieve Figure 1 Terminal equipment 110.

[0063] like Figure 4 As shown, electronic device 400 is in the form of a general-purpose electronic device. Components of electronic device 400 may include, but are not limited to, one or more processors or processing units 410, memory 420, storage device 430, one or more communication units 440, one or more input devices 450, and one or more output devices 460. Processing unit 410 may be a physical or virtual processor and is capable of performing various processes according to programs stored in memory 420. In a multiprocessor system, multiple processing units execute computer-executable instructions in parallel to improve the parallel processing capability of electronic device 400.

[0064] Electronic device 400 typically includes multiple computer storage media. Such media can be any available media accessible to electronic device 400, including but not limited to volatile and non-volatile media, removable and non-removable media. Memory 420 can be volatile memory (e.g., registers, cache, random access memory (RAM)), non-volatile memory (e.g., read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory), or some combination thereof. Storage device 430 can be removable or non-removable media and can include machine-readable media, such as flash drives, disks, or any other media that can be used to store information and / or data and can be accessed within electronic device 400.

[0065] Electronic device 400 may further include additional removable / non-removable, volatile / non-volatile storage media. Although not explicitly stated... Figure 4As shown, disk drives for reading from or writing to removable, non-volatile disks (e.g., "floppy disks") and optical disk drives for reading from or writing to removable, non-volatile optical disks can be provided. In these cases, each drive can be connected to a bus (not shown) via one or more data media interfaces. Memory 420 may include computer program product 425 having one or more program modules configured to perform various methods or actions of various embodiments of this disclosure.

[0066] Communication unit 440 enables communication with other electronic devices via a communication medium. Additionally, the functionality of components of electronic device 400 can be implemented using a single computing cluster or multiple computing machines capable of communicating via communication connections. Therefore, electronic device 400 can operate in a networked environment using logical connections to one or more other servers, networked personal computers (PCs), or other network elements.

[0067] Input device 450 can be one or more input devices, such as a mouse, keyboard, trackball, etc. Output device 460 can be one or more output devices, such as a monitor, speaker, printer, etc. Electronic device 400 can also communicate with one or more external devices (not shown) via communication unit 440 as needed. These external devices include storage devices, display devices, etc., and can communicate with one or more devices that enable user interaction with electronic device 400, or with any device that enables electronic device 400 to communicate with one or more other electronic devices (e.g., network card, modem, etc.). Such communication can be performed via input / output (I / O) interface (not shown).

[0068] According to an exemplary implementation of this disclosure, a computer-readable storage medium is provided that stores computer-executable instructions thereon, wherein the computer-executable instructions are executed by a processor to implement the methods described above. According to an exemplary implementation of this disclosure, a computer program product is also provided, which is tangibly stored on a non-transitory computer-readable medium and includes computer-executable instructions, which are executed by a processor to implement the methods described above.

[0069] Various aspects of this disclosure are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatuses, devices, and computer program products implemented according to this disclosure. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions.

[0070] These computer-readable program instructions can be provided to a processing unit of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that, when executed by the processing unit of the computer or other programmable data processing apparatus, they create means for implementing the functions / actions specified in one or more blocks of the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium that causes a computer, programmable data processing apparatus, and / or other device to operate in a particular manner. Thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing aspects of the functions / actions specified in one or more blocks of the flowchart and / or block diagram.

[0071] Computer-readable program instructions can be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions that execute on the computer, other programmable data processing apparatus, or other device to perform the functions / actions specified in one or more boxes of a flowchart and / or block diagram.

[0072] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of an instruction, which contains one or more executable instructions for implementing the specified logical function. In some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0073] Various implementations of this disclosure have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed implementations. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described implementations. The terminology used herein is chosen to best explain the principles, practical applications, or improvements to technology in the market, or to enable others skilled in the art to understand the various implementations disclosed herein.

Claims

1. A method for determining an operation path, comprising: Determine the associated information corresponding to a set of interactive elements on the target page; Based on the association information, an arrangement operation is performed on at least a portion of the interactive elements in the group of interactive elements to determine the corresponding arrangement order associated with at least two interactive elements in the at least a portion of the interactive elements; The probability of arranging the at least two interactive elements in the corresponding permutation order is determined using a trained machine learning model; and Based on the corresponding probabilities, the at least two interactive elements, and the corresponding arrangement order, at least one operation path associated with the set of interactive elements is determined.

2. The method of claim 1, wherein performing the permutation operation comprises: In response to the association information indicating that at least a portion of the interactive elements are non-one-time interactive elements, the arrangement operation is performed on at least a portion of the interactive elements.

3. The method of claim 1, wherein determining the at least one operation path associated with the set of interactive elements comprises: Based on the corresponding arrangement order, execute the interactive operation corresponding to each interactive element; as well as In response to the interactive operation having a response or the number of operations being less than a quantity threshold, at least one operation path is determined.

4. The method according to claim 1, wherein the machine learning model is determined in the following manner: Identify a set of candidate interactive elements and their corresponding candidate association information from the page used for model training; Determine the candidate arrangement order corresponding to at least two candidate interactive elements in the set of candidate interactive elements; Based on the candidate association information, determine the probability that the at least two candidate interactive elements are arranged in the candidate arrangement order; The at least two candidate interactive elements are provided to the machine learning model to determine the prediction probability; as well as The machine learning model is trained based on the probability and the predicted probability to determine the machine learning model.

5. The method of claim 4, wherein determining the probability comprises: Based on the candidate association information, candidate feature matrices corresponding to the at least two candidate interactive elements are determined respectively; as well as The probability is determined based on the candidate feature matrix.

6. The method according to claim 4, wherein the row data in the candidate feature matrix corresponds to candidate interactive elements, and the column data corresponds to element attributes.

7. The method of claim 1, wherein the associated information indicates at least one of the following: Interactive operation types, element text, element display status, element attributes, and predefined identifiers.

8. The method according to claim 1, further comprising: In response to the association information indicating that the at least part of the interactive elements are one-time interactive elements, an operation path corresponding to the at least part of the interactive elements is determined based on the operation path generation rules.

9. An apparatus for determining an operation path, comprising: The determination module is configured to determine the association information corresponding to a set of interactive elements on the target page; The arrangement module is configured to perform an arrangement operation on at least a portion of the interactive elements in the set of interactive elements based on the association information, so as to determine the corresponding arrangement order associated with at least two interactive elements in the at least a portion of the interactive elements; The module is configured to use a trained machine learning model to determine the probability of arranging the at least two interactive elements in the corresponding permutation order. as well as The execution module is configured to determine at least one operation path associated with the set of interactive elements based on the corresponding probabilities, the at least two interactive elements, and the corresponding arrangement order.

10. An electronic device, comprising: At least one processing unit; as well as At least one memory, coupled to the at least one processing unit and storing instructions for execution by the at least one processing unit, the instructions causing the electronic device to perform the method according to any one of claims 1 to 8 when executed by the at least one processing unit.

11. A computer-readable storage medium having a computer program stored thereon, the computer program being executable by a processor to implement the method according to any one of claims 1 to 8.

12. A computer program product comprising computer-executable instructions, wherein the computer-executable instructions, when executed by a processor, implement the method according to any one of claims 1 to 8.

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