Interaction test method and device of interface and computer program product

Abstract

The invention discloses an interface interaction test method and device and a computer program product. Relates to the field of big data or financial science and technology, and comprises the following steps: extracting a behavior feature set of a target user group from a behavior feature database, the behavior feature database comprising behavior feature sets of N user groups, N being a positive integer, and N being a positive integer; the behavior characteristics in the behavior characteristic set are the characteristics that the user operates the interface of the application program of the financial institution; generating a random interaction path based on the behavior feature set of the target user group, and converting the random interaction path into a test script through a preset test tool; and executing the test script in the test environment to obtain a test result. Through the method and the device, the problem of incomplete interface interaction test in related technologies is solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of big data or the field of financial technology, in particular, to an interface interaction test method and device and computer program product. BACKGROUND

[0002] With the rapid development of the field of financial technology, the design and interactive experience of the financial product interface have become a key bridge connecting financial services and the general public. An intuitive, efficient and user-friendly interface not only improves user experience, but also significantly promotes the market competitiveness and user trust of the financial product. In this context, it is particularly important to conduct detailed and comprehensive quality testing on the financial product interface.

[0003] In related technologies, the financial product interface test relies on a function verification method, focusing on ensuring the accuracy of various basic operations of the interface, such as the responsiveness of buttons, the accurate display of data, etc. Although these tests can verify whether the core functions of the interface are working properly, they do not fully consider the diversified and complex behavior patterns of modern users. For example, quickly scrolling through product lists, continuously clicking to compare different investment options, etc. These operations often cannot be properly simulated in the existing testing system. Real users may encounter problems that have not been detected in advance when operating the interface, such as slow interface response, chaotic operation logic or unexpected crashes, etc. These problems directly affect the quality of user experience and weaken the user's confidence and satisfaction with the financial product. The testing method in related technologies tends to use fixed scripts to simulate user operations, and the test covers a limited and pre-set operation sequence. This method ignores the high randomness and unpredictability of real user behavior. Each user has different exploration paths, preferences and needs in the financial interface, and the fixed script path is difficult to capture this difference, resulting in some potential interaction problems being ignored.

[0004] In view of the problem of incomplete interface interaction test in related technologies, no effective solution has been proposed so far. SUMMARY

[0005] The main purpose of the present application is to provide an interface interaction test method, device and computer program product to solve the problem of incomplete interface interaction test in related technologies.

[0006] In order to achieve the above object, according to one aspect of the present application, an interface interaction test method is provided. The method comprises: extracting a behavior feature set of a target user group from a behavior feature database, wherein the behavior feature database contains behavior feature sets of N user groups, N is a positive integer, and the behavior features in the behavior feature set are characteristics of user operations on the interface of the application program of the financial institution; generating a random interaction path based on the behavior feature set of the target user group, converting the random interaction path into a test script through a preset test tool; executing the test script in a test environment to obtain a test result.

[0007] Optionally, the behavior feature database is obtained by: collecting user operation logs on the interface of the application program of the financial institution through a preset burying point method, collecting target data from the user operation logs, wherein the target data includes at least one of the following: operation data, performance data and user feedback data; dividing a user set of the financial institution based on user information to obtain N user groups, and extracting the behavior features of each user in the N user groups from the target data; for each user group, clustering the behavior features of the user group through a clustering algorithm to obtain the behavior feature set of the user group; and constructing the behavior feature database from the behavior feature sets of the N user groups.

[0008] Optionally, extracting the behavior features of each user in the N user groups from the target data comprises: in the case that the target data includes operation data, extracting operation behavior features from the operation data, wherein the operation behavior features include at least one of the following: click frequency of each click area of the interface, page jump path, page stay duration and user operation interval duration; in the case that the target data includes performance data, extracting performance features from the performance data, wherein the performance features include at least one of the following: operation response duration of the interface, memory occupancy rate and maximum occupied memory; in the case that the target data includes user feedback data, extracting user feedback features from the user feedback data, wherein the user feedback features include at least one of the following: user feedback frequency of the interface and operation interruption times; and determining at least one of the following as the behavior features: operation behavior features, performance features and user feedback features.

[0009] Optionally, generating a random interaction path based on the behavior feature set of the target user group comprises: determining M pages of the interface, constructing a page jump probability matrix of each page based on the behavior feature set, wherein M is a positive integer; determining a starting page and an ending page in the M pages, starting from the starting page, randomly selecting the next page to jump based on the page jump probability matrix of each page until jumping to the ending page; and determining the random interaction path based on all the pages jumped between the starting page and the ending page and the jump order.

[0010] Optionally, after obtaining the test result, the method further comprises: extracting an actual page jump path from the test result, determining whether the actual page jump path matches the preset page jump path; in a case where the actual page jump path does not match the preset page jump path, determining that the interface has a logic defect, detecting whether the preset page jump path covers all business scenarios to obtain a first detection result; in a case where the actual page jump path matches the preset page jump path, if a target page in the actual page jump path does not have a target page element, determining that the interface has a logic defect, detecting whether there is a missing parameter in an interface request parameter of the interface, and detecting whether a database and the interface have a same data synchronization state to obtain a second detection result; in a case where the actual page jump path matches the preset page jump path, if the target page in the actual page jump path has the target page element, determining that the interface does not have a logic defect; and in a case where the interface has a logic defect, determining the first detection result or the second detection result as a logic defect reason.

[0011] Optionally, after obtaining the test result, the method further comprises: extracting a maximum operation response time length and a memory occupancy rate from the test result; in a case where the maximum operation response time length is less than a response time length threshold and the memory occupancy rate is less than an occupancy rate threshold, determining that the interface does not have a performance defect; in a case where the maximum operation response time length is greater than or equal to the response time length threshold or the memory occupancy rate is greater than or equal to the occupancy rate threshold, determining that the interface has a performance defect; determining an operation time corresponding to the maximum operation response time length and the memory occupancy rate, determining a target operation behavior corresponding to the operation time, and determining a defect node of the interface operated by the target operation behavior; performing performance testing on the defect node to obtain a test result, and determining the test result as a performance defect reason.

[0012] Optionally, after obtaining the test result, the method further comprises: extracting a user feedback frequency and an operation interruption number from the test result; in a case where the user feedback frequency is less than a feedback frequency threshold and the operation interruption number is less than an interruption number threshold, determining that the interface does not have a user feedback defect; in a case where the user feedback frequency is greater than or equal to the feedback frequency threshold, determining a feedback type and a feedback keyword; in a case where the operation interruption number is greater than or equal to the interruption number threshold, determining an operation interruption behavior and an interruption page; and determining the operation interruption behavior and at least one of the feedback type and the feedback keyword as a feedback defect reason.

[0013] Optionally, after obtaining the test result, the method further includes: in the case that there is an interaction defect in the test result characterization interface, determining a first defect type and a first defect cause of the interaction defect, and acquiring a historical test record of the interface, wherein the interaction defect includes at least one of a logic defect, a performance defect, and a user feedback defect; determining a target test record from the historical test record, wherein a second defect type of the target test record has a similarity to the first defect type greater than or equal to a first similarity threshold, and a second defect cause of the target test record has a similarity to the first defect cause greater than or equal to a second similarity threshold; and determining a historical optimization strategy corresponding to the target test record as an optimization strategy of the interface.

[0014] To achieve the above object, according to another aspect of the present application, an interface interaction testing device is provided. The device comprises: an extraction unit configured to extract a behavior feature set of a target user group from a behavior feature database, wherein the behavior feature database contains behavior feature sets of N user groups, N is a positive integer, and the behavior features in the behavior feature set are characteristics of user operations on the interface of the application program of the financial institution; a generation unit configured to generate a random interaction path based on the behavior feature set of the target user group, and convert the random interaction path into a test script through a preset test tool; and an execution unit configured to execute the test script in a test environment to obtain a test result.

[0015] In the embodiments of the present application, the behavior feature set of the target user group is extracted from the behavior feature database, wherein the behavior feature database contains behavior feature sets of N user groups, N is a positive integer, and the behavior features in the behavior feature set are characteristics of user operations on the interface of the application program of the financial institution; a random interaction path is generated based on the behavior feature set of the target user group, and the random interaction path is converted into a test script through a preset test tool; and the test script is executed in a test environment to obtain a test result. By constructing the behavior feature database, the behavior pattern of the user group is more accurately reflected, a random interaction path is generated based on the behavior features in the behavior feature database, and the test script converted from the random interaction path is used for testing, so that the simulation accuracy of the random interaction path is improved, thereby achieving the technical effect of improving the comprehensiveness of interface testing, and further solving the technical problem of incomplete interface interaction testing. BRIEF DESCRIPTION OF DRAWINGS

[0016] The accompanying drawings, which form a part of the present application, are intended to provide further understanding of the present application, and are used to interpret the illustrative embodiments of the present application and their descriptions, and do not constitute improper limitations to the present application. In the drawings:

[0017] Figure 1 A hardware structure block diagram of a computer terminal (or a mobile device) for implementing an interface interaction testing method is shown;

[0018] Figure 2 is a flowchart of an interaction test method of an interface provided according to an embodiment of the present application;

[0019] Figure 3 is a schematic diagram of an interaction test device provided according to an embodiment of the present application;

[0020] Figure 4 is a structural block diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0021] In order to enable persons skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by persons skilled in the art without creative labor should fall within the scope of protection of the present application.

[0022] It should be noted that the terms "first", "second", and the like in the specification and claims of the present application and the above-described drawings are used to distinguish similar objects, and do not necessarily indicate a specific order or a chronological sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product, or device that includes a series of steps or units does not necessarily have to be limited to only those steps or units clearly listed, but can include other steps or units that are not clearly listed or inherent to the process, method, product, or device.

[0023] It should be noted that the information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for display, analyzed data, etc.) involved in the present application are information and data authorized by the user or authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure, and application of related data comply with relevant laws, regulations, and standards, take necessary security measures, do not violate public order and good customs, and provide corresponding operation portals for users to choose authorization or refusal. For example, interfaces are provided between the system and related users or institutions to provide corresponding operation portals for users to choose to agree or refuse automatic decision results; if the user chooses to refuse, the expert decision process is entered.

[0024] Embodiment 1

[0025] According to the method embodiment for testing the interaction of the interface, it should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that here.

[0026] The method embodiment provided by the embodiment of the present application can be executed in a mobile terminal, a computer terminal or similar computing device. Figure 1 A hardware structure block diagram of a computer terminal (or mobile device) for implementing the method for testing the interaction of the interface is shown. As shown in Figure 1 The computer terminal 10 (or mobile device) can include one or more processors 102 (the processor 102 can include but is not limited to a processing device such as a microcontroller unit (MCU) or a field-programmable gate array (FPGA)), a memory 104 for storing data, and a transmission device 106 for communication functions. In addition, it can also include a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which can be included as one of the ports in the bus (Business) port), a network interface, a power supply and / or a camera. Those skilled in the art can understand that Figure 1 The structure shown is only schematic, which does not limit the structure of the above-mentioned electronic device. For example, the computer terminal 10 can include more or fewer components than those shown in Figure 1 or have a different configuration than that shown in Figure 1 .

[0027] It should be noted that the one or more processors 102 and / or other data processing circuits described above can be referred to herein as "data processing circuits" in general. The data processing circuit can be embodied in whole or in part as software, hardware, firmware or any combination thereof. In addition, the data processing circuit can be a single independent processing module, or any one of the other elements combined into the computer terminal 10 (or mobile device) in whole or in part. As referred to in the embodiments of the present application, the data processing circuit serves as a processor to control (for example, selection of a variable resistance terminal path connected to an interface).

[0028] The memory 104 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the interactive test method of the interface in the embodiment of the present application. The processor 102 executes various functional applications and data processing by running the software programs and modules stored in the memory 104, that is, realizing the interactive test method of the interface mentioned above. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include a memory remotely located relative to the processor 102, and these remote memories may be connected to the computer terminal 10 via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0029] The transmission device 106 is configured to receive or transmit data via a network. A specific example of the aforementioned network may include a wireless network provided by the communications provider of the computer terminal 10. In one embodiment, the transmission device 106 includes a network interface controller (NIC), which can be connected to other network devices via a base station to enable communication with the Internet. In another embodiment, the transmission device 106 may be a radio frequency (RF) module, which is configured to communicate with the Internet wirelessly.

[0030] The display may be, for example, a touch screen liquid crystal display that enables a user to interact with a user interface of the computer terminal 10 (or mobile device).

[0031] In the above operating environment, this application provides an interactive testing method for an interface. Figure 2 is a flow chart of an interactive testing method for an interface provided in an embodiment of the present application, such as Figure 2 As shown, the method includes:

[0032] Step S201, extracting a behavioral feature set of a target user group from a behavioral feature database, wherein the behavioral feature database contains behavioral feature sets of N user groups, where N is a positive integer, and the behavioral features in the behavioral feature set are features of users operating the interface of the financial institution's application.

[0033] In step S201, the interaction data of users on the interface of the financial institution application is collected through front-end burying, user research, data analysis, etc., including operation type (such as click, slide, input), operation frequency, operation time, stay duration, emotional feedback, etc. Based on the collected data, clustering analysis, user portrait modeling, etc. are used to divide users into N different groups. Each group represents a set of users with similar behavior patterns and characteristics. A set of behavior characteristics is established for each user group, including operation preference, interface use habit, emotional tendency, etc. These characteristic sets are constructed through statistical analysis and machine learning models to ensure accurate reflection of the typical behavior of the user group.

[0034] According to the test purpose and requirement, one or more user groups are selected as the target group for testing. For example, if the experience of elderly users is concerned, the "elderly user" group is selected as the target user group. From the behavior characteristic database, the behavior characteristic set of the selected target user group is extracted. This includes all typical operation characteristics related to the group, such as high-frequency click areas, page jump patterns, average operation interval time, etc.

[0035] In step S202, a random interaction path is generated based on the behavior characteristic set of the target user group, and the random interaction path is converted into a test script through a pre-set test tool.

[0036] In step S202, a large number of random operation paths are generated based on the behavior characteristic set of the target user group using a random algorithm, highly restoring various operation behaviors of real users on the financial interface, ensuring the diversity and authenticity of the test scenario. The behavior characteristic set of the target user group contains various behavior characteristics for operating the interface of the financial institution application, such as: click hotspots: the interface elements most frequently clicked by users and their coordinates. Page flow pattern: the common sequence of user browsing between different pages. Operation time interval: the time interval between consecutive operations by the user. Input behavior: the length, type, and frequency of user input data. Slide habit: the direction and speed of user sliding on the interface.

[0037] With the set of behavioral characteristics of the target user group, the next step is to generate random interaction paths using algorithms. Determine the starting and ending pages or function points of the random path. Based on the set of behavioral characteristics, create a transition probability model for page-to-page jumps. For example, if the frequency of jumps from the home page to the product detail page is higher than the frequency of jumps from the home page to the account page, the probability of transitioning from the former to the latter will also be higher when generating paths. Using the above probability model and behavioral characteristics such as click frequency, swipe habits, etc., generate one or more random interaction paths through a random algorithm such as Markov process. Each path is coherent, simulating the possible sequence of operations from the starting page to the ending page. To make the generated paths more realistic, user feedback features such as user satisfaction, operation interruption index, etc. can be further integrated to adjust the appearance probability or order of certain operations in the path to reflect the emotional changes and possible interruption points of the user during operation.

[0038] After generating random interaction paths, use tools that support automated testing to build the basic framework of the test script. According to the generated random path, program the operation sequence (such as clicking buttons, swiping screens, inputting text, etc.) one by one into the test script. Ensure that each operation matches the characteristics described in the set of behavioral characteristics, such as the position of the click operation should correspond to the click hotspot coordinates. In the script, set the delay between operations according to the operation time interval recorded in the set of behavioral characteristics to simulate the real operation rhythm of the user. After writing, perform preliminary debugging to ensure that the script can smoothly pass through each operation point without syntax or logic errors. In addition, the script can also be optimized, such as reducing redundant operations to improve execution efficiency. Deploy the written test script to the test environment to ensure that the test environment can support the running of the script, including but not limited to the correct browser version, mobile device type, operating system environment, etc.

[0039] Step S203, execute the test script in the test environment to obtain the test results.

[0040] In step S203, ensure that the test environment is consistent with the production environment, including operating system version, browser type, database configuration, network conditions, etc. to accurately simulate the user's use in the actual environment. Reserve enough server resources such as memory and disk space for testing to avoid resource contention affecting the accuracy of the test results. According to the test requirements, prepare the necessary test data, including simulating user account information, financial product data, transaction records, etc. to ensure that the test can cover all business scenarios.

[0041] The automated testing tool and the generated test script are deployed in a test environment. The automated testing tool is started, and the test script converted from the random interaction path is executed to simulate user operations according to the preset behavior characteristics. During the test execution, key performance data such as interface response time, system performance indicators, and network delay are monitored in real time. At the same time, the results of each operation, including successful operations, failed operations, or abnormal conditions, are recorded, and the integrity of the log is ensured to facilitate subsequent analysis.

[0042] The interface interaction test method provided in the embodiments of the present application extracts a behavior characteristic set of a target user group from a behavior characteristic database, wherein the behavior characteristic database contains behavior characteristic sets of N user groups, N is a positive integer, and the behavior characteristics in the behavior characteristic set are characteristics of user operations on the interface of the application program of the financial institution. A random interaction path is generated based on the behavior characteristic set of the target user group, and the random interaction path is converted into a test script by a preset test tool. The test script is executed in a test environment to obtain a test result. The behavior characteristic database is constructed to more accurately reflect the behavior patterns of the user groups, and a random interaction path is generated based on the behavior characteristics in the behavior characteristic database. The test script converted from the random interaction path is used for testing, which improves the simulation accuracy of the random interaction path and improves the comprehensiveness of the interface test, thereby solving the technical problem of incomplete interface interaction test.

[0043] In order to more comprehensively simulate user behavior during interface testing, a behavior characteristic database needs to be constructed. In the interface interaction test method provided in the embodiments of the present application, the behavior characteristic database is obtained in the following manner: user operation logs are collected on the interface of the application program of the financial institution by a preset embedding method, target data is collected from the user operation logs, wherein the target data includes at least one of the following: operation data, performance data, and user feedback data; a user set of the financial institution is divided based on user information to obtain N user groups, and the behavior characteristics of each user in the N user groups are extracted from the target data; for each user group, the behavior characteristics of the user group are clustered by a clustering algorithm to obtain a behavior characteristic set of the user group; and the behavior characteristic database is constructed from the behavior characteristic sets of the N user groups.

[0044] In some embodiments, a front-end tracking method is used to implant tracking code in various interfaces of the application program of the financial institution, such as product information pages, purchase pages, and redemption pages, to record user operation logs. The logs include the following three types of data: operation data, user feedback data, and performance data. The operation data can include: click coordinates, page jump path, page dwell time. The user feedback data can include: user-initiated text feedback, help center access records, and page closing behavior. The performance data can include interface response time, CPU (Central Processing Unit) / memory occupancy. Invalid data is cleaned up, including abnormal logs caused by network interruption, repeated data caused by users submitting the same operation multiple times in a short period of time, incomplete data such as missing values, dirty data generated by test data or abnormal traffic, and logical error data.

[0045] The users are divided into different groups according to typical portraits (such as novice users, high-frequency transaction users, and elderly users), and the user portrait distribution and feature mean in each cluster are obtained based on the clustering algorithm, for example: the user portrait distribution of a certain user group is {"novice user": 0.8; "high-frequency transaction user": 0.1; "elderly user": 0.4}. The behavior feature mean is {"high-frequency click area": x=32.45%, y=61.08%; "typical jump path": menu1001-menu10012-menu100129; "user long dwell page": menu10012, menu1003; "average operation interval time": 320.5; "emotional score": 0.5; "help request frequency": 0.3; "operation interruption index": 0.4; "operation response time P95": 2100, which means that the mean of the operation response time of the top 95% of users in the user group is 2100; "memory occupancy peak": 512}.

[0046] By constructing the behavior feature database, the financial institution can obtain diversified behavior characteristics of different user groups when operating the application interface, providing a rich and detailed data basis for subsequent random interaction path generation, test script conversion, test execution and result analysis. The accuracy and comprehensiveness of the test are enhanced, helping the financial institution to optimize the interface design from the user's perspective, improve the user experience, and promote business growth.

[0047] After collecting the target data, behavioral features are extracted from the target data. Optionally, in the interface interaction testing method provided in an embodiment of the present application, extracting behavioral features of each user in N user groups from the target data includes: when the target data includes operation data, extracting operation behavior features from the operation data, wherein the operation behavior features include at least one of the following: click frequency of each click area of ​​the interface, page jump path, page dwell time, and user operation interval length; when the target data includes performance data, extracting performance features from the performance data, wherein the performance features include at least one of the following: operation response time of the interface, memory occupancy, and maximum occupied memory; when the target data includes user feedback data, extracting user feedback features from the user feedback data, wherein the user feedback features include at least one of the following: user feedback frequency of the interface and number of operation interruptions; and determining at least one of the following as a behavioral feature: operation behavior features, performance features, and user feedback features.

[0048] In some embodiments, the cleaned user behavior data is processed and standardized, and feature extraction is finally completed, which is divided into three categories of features: operation behavior, system performance, and user feedback. Operation behavior features: based on the user's click coordinates, mark the top 10% of high-frequency click areas, as well as the buttons and icons contained in the area, and normalize the click hot zone coordinates to the percentage of the interface resolution; based on the page jump path, extract the top 80% of the main inflow and outflow directions of each page, and extract the user's typical jump path; based on the top 30% of the pages where the user stays on the page, output the user's longest-stay pages; calculate the user's average operation interval time (milliseconds) based on the page stay time and the operation interval.

[0049] User feedback characteristics: Sentiment analysis is performed on user feedback text, and a pre-trained classification model is used to output a score, marking the probability of positive or negative user feedback. The frequency of user help requests is calculated based on the number of help center visits and session duration. The user's operation interruption index is derived from the ratio of the number of page closes abandoned by users to the total number of operations. System performance characteristics: The P95 response time (in milliseconds) for the top 95% of users on each interface is calculated. Peak memory usage is calculated based on CPU / memory usage, describing the maximum process memory usage during a single session.

[0050] This example extracts user behavior characteristics from target data to build a behavior characteristic database that comprehensively reflects user groups' operating habits, performance requirements, and user feedback. This provides solid data support for subsequent random interaction path generation and test script conversion, and offers a deep analytical perspective for understanding user needs, optimizing interface design, and improving the overall user experience.

[0051] In order to comprehensively simulate the operation behavior of the user on the interface, the random interaction path is generated based on the behavior characteristic set. Optionally, in the interface interaction test method provided in the embodiments of the present application, generating the random interaction path based on the behavior characteristic set of the target user group comprises: determining M pages of the interface, constructing a page jump probability matrix of each page based on the behavior characteristic set, wherein M is a positive integer; determining a starting page and an ending page in the M pages, starting from the starting page, randomly selecting the next page based on the page jump probability matrix of each page until the ending page is jumped to; determining the random interaction path based on all the pages jumped between the starting page and the ending page and the jump order.

[0052] In some embodiments, the user type and the behavior characteristic set of the target user group are input, starting from the starting page, the next page is randomly selected based on the page jump probability matrix until the ending page is jumped to, and the random interaction path generation is completed.

[0053] For example, the dynamic random path is generated based on the Monte Carlo algorithm, the page jump probability matrix is generated according to the typical jump path in the feature library, for example: "menu1001": {"menu10011": 0.7, "menu10012": 0.2, "menu100": 0.1}, the probability of jumping from the page menu1001 to the page menu10011 is 0.7, the probability of jumping from the page menu1001 to the page menu10012 is 0.2, and the probability of jumping from the page menu1001 to the page menu100 is 0.1.

[0054] "menu10012": {"menu100121": 0.4, "menu100122": 0.2, "menu1001": 0.2, "menu100": 0.2}, the probability of jumping from the page menu10012 to the page menu100121 is 0.4, the probability of jumping from the page menu10012 to the page menu100122 is 0.2, the probability of jumping from the page menu10012 to the page menu menu1001 is 0.2, and the probability of jumping from the page menu10012 to the page menu100 is 0.2.

[0055] The random path is optimized in combination with the long-stay page, the operation interruption index, the average operation interval time and the like, and the authenticity of the path is enhanced. For example, the transition probability of the long-stay page is considered to be increased, and whether the random path is interrupted is optimized according to the interruption index; and the average operation interval time is selected as the mean value, and the corresponding operation time interval is generated based on the mean value random wave; for example:

[0056] {"Operation path": "menu100-menu1001-menu10013-menu1001-menu10011-menu100

[0057] "Operation interval time (ms)": 450.10}。

[0058] The embodiment generates a plurality of random interaction paths close to the actual use of the user based on the behavior feature set of the target user group, provides a basis for subsequent automatic test script writing and execution, and helps to find and optimize potential problems and user experience of the application interface under real user operation.

[0059] After obtaining the test result, logical defects and defect causes are analyzed from the test result. Optionally, in the interface interaction test method provided in the interface provided in the embodiment of the application, after obtaining the test result, the method further includes: extracting an actual page jump path from the test result, judging whether the actual page jump path matches a preset page jump path; in the case where the actual page jump path does not match the preset page jump path, determining that the interface has a logical defect, detecting whether the preset page jump path covers all business scenarios to obtain a first detection result; in the case where the actual page jump path matches the preset page jump path, if a target page in the actual page jump path does not have a target page element, determining that the interface has a logical defect, detecting whether there is a missing parameter in an interface request parameter of the interface, and detecting whether a database and a data synchronization state of the interface are the same to obtain a second detection result; in the case where the actual page jump path matches the preset page jump path, if the target page in the actual page jump path has the target page element, determining that the interface does not have a logical defect; in the case where the interface has a logical defect, determining the first detection result or the second detection result as a logical defect cause.

[0060] In some embodiments, the interface interaction during the simulation operation process is monitored and evaluated, and performance index monitoring, logical rule checking and emotional analysis results are comprehensively used to identify three types of defects, i.e. performance, logic and user feedback, existing in the interface interaction from the test result, and to further analyze the causes and influence degree of the defects.

[0061] By defining a legal preset page jump path, whether an illegal scene appears in the generated actual page jump path is compared, whether the actual page jump path deviates from the business flow conventional operation path, i.e. whether the actual page jump path matches the preset page jump path is judged, if not, it is determined that the interface has a logical defect; if yes, the target page element such as a key button, a control element or the like is further checked to determine whether the target page element exists in the test execution.

[0062] For the scene of logical defects, check whether the pre-defined preset page jump path covers all business scenarios; track the interface request parameters of the interface through the log analysis tool, check whether the key parameters are missing; use the database transaction log to compare the data synchronization state to check whether there is a defect in data consistency. If any of the above detection stages finds a mismatch or a missing, it is confirmed that the application interface has a logical defect. The first detection result (path mismatch and insufficient business scenario coverage) or the second detection result (missing page elements, incomplete interface parameters, inconsistent data synchronization state) is determined as the specific reason for the logical defect, which provides a basis for subsequent repair.

[0063] The embodiment improves the stability and user experience of the application by identifying and solving the logical defects of the financial product interface. The logical defect detection method based on actual user behavior can not only find problems that may not be foreseen during design, but also evaluate the logical processing capability of the application to ensure its high reliability in complex and variable user operation scenarios.

[0064] After obtaining the test result, the performance defects and defect causes are analyzed from the test result. Optionally, in the interface interaction test method provided in the embodiment, after obtaining the test result, the method further includes: extracting the maximum operation response time and the memory occupancy rate from the test result; in the case that the maximum operation response time is less than the response time threshold and the memory occupancy rate is less than the occupancy rate threshold, it is determined that the interface does not have performance defects; in the case that the maximum operation response time is greater than or equal to the response time threshold, or the memory occupancy rate is greater than or equal to the occupancy rate threshold, it is determined that the interface has performance defects; determining the operation time corresponding to the maximum operation response time and the memory occupancy rate, determining the target operation behavior corresponding to the operation time, and determining the defect node of the interface operated by the target operation behavior; performing performance testing on the defect node to obtain a test result, and determining the test result as a performance defect cause.

[0065] In some embodiments, the performance key indicators are threshold preset, such as when the maximum operation response time exceeds 2s, 2.5s, 3s, or the memory occupancy rate grows more than 1.2, 1.3, 1.5 times the baseline, it is considered to have mild, moderate, or severe performance defects. According to the operation time when the performance defects exist in the log, the defect occurrence node is identified, recorded and played back; the slow query log is analyzed to analyze the query efficiency using the execution plan; the server resource time sequence data is monitored to detect abnormal fluctuations. The defect node is subjected to stepwise stress testing, the number of concurrent users is gradually increased, and the TPS (transactions per second) and error rate are recorded.

[0066] Based on the performance test results, analyze what causes extended response times or increased memory usage. Causes may include, but are not limited to, inefficient database queries, redundant or inefficient code, intense resource competition, and memory leaks. Record the root causes of the performance issues identified as specific causes of the performance defect to provide clear direction for subsequent performance optimization efforts.

[0067] This embodiment detects whether there are performance defects in the application interface, accurately locates the defective nodes, analyzes the causes of the defects, and performs targeted performance optimization, thereby comprehensively improving the performance of the application and user experience.

[0068] After obtaining the test results, user feedback defects and defect causes are analyzed from the test results. Optionally, in the interactive testing method of the interface provided in the embodiment of the present application, after obtaining the test results, the method further includes: extracting the user feedback frequency and the number of operation interruptions from the test results; when the user feedback frequency is less than the feedback frequency threshold and the number of operation interruptions is less than the interruption number threshold, determining that there is no user feedback defect in the interface; when the user feedback frequency is greater than or equal to the feedback frequency threshold, determining the user feedback type and feedback keywords; when the number of operation interruptions is greater than or equal to the interruption number threshold, determining the operation interruption behavior and the interruption page; determining the operation interruption behavior and at least one of the following as the cause of the feedback defect: feedback type and feedback keyword.

[0069] In some embodiments, the total number of times users submit feedback is counted from the test results, and the average feedback frequency within a specific time interval is calculated, i.e., the user feedback frequency. Similarly, the number of times users actively interrupt or abandon an operation during an operation is counted from the test results, especially the number of interruptions related to specific interface operations, i.e., the number of operation interruptions. Feedback frequency thresholds and operation interruption number thresholds are pre-set, and normal and abnormal levels are defined based on historical data or industry standards.

[0070] If the user feedback frequency is lower than the feedback frequency threshold, and the number of operation interruptions is lower than the interruption count threshold, then the app interface is preliminarily judged to have no user feedback defects under the current test conditions. If the user feedback frequency is greater than or equal to the feedback frequency threshold, or the number of operation interruptions is greater than or equal to the interruption count threshold, then the app interface may have user experience issues, that is, a user feedback defect.

[0071] In the case of abnormal user feedback frequency, the feedback content submitted by the user is classified and keyword extracted to understand the main dissatisfaction or problem type of the user. For example, the feedback type can include interface design problems, function implementation problems, performance problems, etc.; the keywords can include descriptive words such as "slow loading", "button unresponsive", "incomplete information", etc. In the case of abnormal operation interruption frequency, analyze where the interruption occurs in specific operations and pages, and identify the interface or function point that causes the user to interrupt the operation. The interruption behavior can be directly related to complex operations, long waiting time, interface errors, etc. Combine the feedback type, feedback keyword, and operation interruption behavior to determine the specific cause of the user feedback defect. For example, if the user frequently mentions "interface clutter" and "can't find the function", it may be due to unreasonable interface design; the operation interruption is concentrated on the transaction confirmation page, which may be due to performance problems or overly complex operation process of the page.

[0072] The embodiment detects and analyzes user feedback defects of the application interface systematically, identifies obstacles in user experience, and generates targeted optimization suggestions to improve the overall user experience and user satisfaction of the application.

[0073] After determining that the interface has an interaction defect, a corresponding interface optimization strategy is determined based on the defect cause. Optionally, in the interface interaction test method provided in the embodiment, after obtaining the test result, the method further includes: in the case that the test result indicates that the interface has an interaction defect, determining a first defect type and a first defect cause of the interaction defect, obtaining a historical test record of the interface, wherein the interaction defect includes at least one of the following: a logic defect, a performance defect, and a user feedback defect; determining a target test record from the historical test record, wherein a second defect type of the target test record has a similarity greater than or equal to a first similarity threshold with the first defect type, and a second defect cause of the target test record has a similarity greater than or equal to a second similarity threshold with the first defect cause; determining a historical optimization strategy corresponding to the target test record as the optimization strategy of the interface.

[0074] In some embodiments, first, based on the test result, the type of the interaction defect existing in the interface is confirmed. The defect type can include but is not limited to a logic defect, a performance defect, and a user feedback defect. For each defect type, its specific cause is analyzed. For example, a logic defect can be caused by page jump logic error, unreasonable business process design, etc.; a performance defect can be caused by low code execution efficiency, improper resource management, etc.; a user feedback defect can be related to user experience related factors such as non-intuitive interface design and complex function operation.

[0075] The historical test records of the interface are extracted from the test database. These records contain information such as defect types, causes, and optimization measures under various test scenarios. The similarity between the defect types of the historical test records and the first defect type detected is calculated. If the similarity between the second defect type in the historical records and the first defect type is greater than or equal to the first similarity threshold, it is preliminarily screened as a potential target test record. Further comparison is made between the second defect cause in the potential target test record and the first defect cause currently detected. If the similarity is greater than or equal to the second similarity threshold, the record becomes the final target test record.

[0076] The historical optimization strategies that best match the current defect type and cause are read from the target test records. These strategies may include specific measures such as code optimization, design modification, function adjustment, performance optimization, etc. The extracted historical optimization strategies are determined as the optimization strategies for the current interface and applied to the improvement process of the interface. Based on the current application architecture, technology stack, and specific business scenarios, the development team needs to adjust the historical optimization strategies appropriately to ensure the applicability and effectiveness of the optimization measures. The optimization strategies are executed to modify the application interface, including but not limited to code refactoring, interface design optimization, database query optimization, resource management improvement, etc.

[0077] This embodiment can quickly locate and solve the interaction defects of the current financial product interface by using the optimization cases in the historical test records, avoiding repetitive work and improving the efficiency and effectiveness of defect repair.

[0078] It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than here.

[0079] Embodiment 2

[0080] The embodiments of the present application also provide an interface interaction testing device. It should be noted that the interface interaction testing device of the embodiments of the present application can be used to execute the interface interaction testing method provided by the embodiments of the present application. The interface interaction testing device provided by the embodiments of the present application is introduced as follows.

[0081] According to the embodiments of the present application, a device for implementing the above-mentioned interface interaction testing method is also provided, Figure 3 is a schematic diagram of the interface interaction testing device provided by the embodiments of the present application, as Figure 3 shown, the device comprises:

[0082] The extraction unit 301 is configured to extract a behavior feature set of a target user group from a behavior feature database, wherein the behavior feature database contains behavior feature sets of N user groups, N is a positive integer, and the behavior features in the behavior feature set are features of user operations on an interface of an application program of a financial institution.

[0083] The generation unit 302 is configured to generate a random interaction path based on the behavior feature set of the target user group, and convert the random interaction path into a test script through a preset test tool.

[0084] The execution unit 303 is configured to execute the test script in a test environment to obtain a test result.

[0085] The interface interaction test device provided by the embodiment of the application is configured to extract a behavior feature set of a target user group from a behavior feature database through the extraction unit 301, wherein the behavior feature database contains behavior feature sets of N user groups, N is a positive integer, and the behavior features in the behavior feature set are features of user operations on an interface of an application program of a financial institution; the generation unit 302 is configured to generate a random interaction path based on the behavior feature set of the target user group, and convert the random interaction path into a test script through a preset test tool; and the execution unit 303 is configured to execute the test script in a test environment to obtain a test result. By constructing the behavior feature database, the behavior pattern of the user group can be more accurately reflected, a random interaction path is generated based on the behavior features in the behavior feature database, and the test script converted from the random interaction path is used for testing, so that the simulation accuracy of the random interaction path is improved, the technical effect of improving the comprehensiveness of interface testing is achieved, and the technical problem of incomplete interface interaction testing is solved.

[0086] Optionally, in the interface interaction test device provided by the embodiment of the application, the device further includes a collection unit configured to collect user operation logs on an interface of an application program of a financial institution through a preset burying point method, and collect target data from the user operation logs, wherein the target data includes at least one of operation data, performance data and user feedback data; a division unit configured to divide a user set of the financial institution based on user information to obtain N user groups, and extract behavior features of each user in the N user groups from the target data; a clustering unit configured to, for each user group, cluster the behavior features of the user group through a clustering algorithm to obtain a behavior feature set of the user group; and a construction unit configured to construct a behavior feature database from the behavior feature sets of the N user groups.

[0087] Optionally, in the interface interaction test apparatus provided by the embodiments of the present application, the dividing unit comprises: a first extraction module, configured to extract operation behavior features from the operation data if the target data comprises the operation data, wherein the operation behavior features comprise at least one of the following: click frequency of each click area of the interface, page jump path, page stay duration and user operation interval duration; a second extraction module, configured to extract performance features from the performance data if the target data comprises the performance data, wherein the performance features comprise at least one of the following: operation response duration of the interface, memory occupancy rate and maximum occupied memory; a third extraction module, configured to extract user feedback features from the user feedback data if the target data comprises the user feedback data, wherein the user feedback features comprise at least one of the following: user feedback frequency of the interface and operation interruption times; and a first determination module, configured to determine at least one of the following as the behavior features: the operation behavior features, the performance features and the user feedback features.

[0088] Optionally, in the interface interaction test apparatus provided by the embodiments of the present application, the generating unit 302 comprises: a second determination module, configured to determine M pages of the interface, and construct a page jump probability matrix of each page based on the behavior feature set, wherein M is a positive integer; a third determination module, configured to determine a start page and an end page in the M pages, and randomly select a next page to jump from the start page based on the page jump probability matrix of each page until the end page is jumped to; and a fourth determination module, configured to determine a random interaction path based on all the pages and the jump order between the start page and the end page.

[0089] Optionally, in the interface interaction test apparatus provided by the embodiments of the present application, the apparatus further comprises: a first judgment unit, configured to extract an actual page jump path from the test result, and judge whether the actual page jump path matches a preset page jump path; a first determination unit, configured to determine that the interface has a logic defect in a case where the actual page jump path does not match the preset page jump path, detect whether the preset page jump path covers all business scenarios to obtain a first detection result; a first detection unit, configured to determine that the interface has a logic defect in a case where a target page in the actual page jump path does not have a target page element, detect whether there is a missing parameter in an interface request parameter of the interface, and detect whether a database and the interface have a same data synchronization state to obtain a second detection result; a second determination unit, configured to determine that the interface does not have a logic defect in a case where the target page in the actual page jump path has the target page element; and a third determination unit, configured to determine the first detection result or the second detection result as a logic defect reason in a case where the interface has a logic defect.

[0090] Optionally, in the interface interaction testing apparatus provided by the embodiment of the present application, the apparatus further comprises: a first test result extraction unit, configured to extract the maximum operation response time length and the memory occupancy rate from the test result; a fourth determination unit, configured to determine that the interface does not have performance defects when the maximum operation response time length is less than the response time length threshold and the memory occupancy rate is less than the occupancy rate threshold; a fifth determination unit, configured to determine that the interface has performance defects when the maximum operation response time length is greater than or equal to the response time length threshold or the memory occupancy rate is greater than or equal to the occupancy rate threshold; a sixth determination unit, configured to determine the operation time corresponding to the maximum operation response time length and the memory occupancy rate, determine the target operation behavior corresponding to the operation time, and determine the defect node of the interface operated by the target operation behavior; and a test unit, configured to perform performance testing on the defect node to obtain a test result, and determine the test result as the performance defect cause.

[0091] Optionally, in the interface interaction testing apparatus provided by the embodiment of the present application, the apparatus further comprises: a second test result extraction unit, configured to extract the user feedback frequency and the operation interruption number from the test result; a seventh determination unit, configured to determine that the interface does not have user feedback defects when the user feedback frequency is less than the feedback frequency threshold and the operation interruption number is less than the interruption number threshold; an eighth determination unit, configured to determine the feedback type and the feedback keyword when the user feedback frequency is greater than or equal to the feedback frequency threshold; a ninth determination unit, configured to determine the operation interruption behavior and the interruption page when the operation interruption number is greater than or equal to the interruption number threshold; and a tenth determination unit, configured to determine the operation interruption behavior and at least one of the feedback type and the feedback keyword as the feedback defect cause.

[0092] Optionally, in the interface interaction testing apparatus provided by the embodiment of the present application, the apparatus further comprises: an eleventh determination unit, configured to determine the first defect type and the first defect cause of the interaction defect and acquire the historical test record of the interface when the test result indicates that the interface has interaction defects, wherein the interaction defects include at least one of the following: a logic defect, a performance defect, and a user feedback defect; a twelfth determination unit, configured to determine a target test record from the historical test record, wherein the similarity between the second defect type of the target test record and the first defect type is greater than or equal to a first similarity threshold, and the similarity between the second defect cause of the target test record and the first defect cause is greater than or equal to a second similarity threshold; and a thirteenth determination unit, configured to determine the historical optimization strategy corresponding to the target test record as the optimization strategy of the interface.

[0093] It should be noted that the extraction unit 301, the generation unit 302 and the execution unit 303 correspond to steps S201 to S203 in Embodiment 1, and the three units have the same instances and application scenarios as the corresponding steps, but are not limited to the content disclosed in the above embodiment. It should be noted that the above modules or units can be hardware components or software components stored in the memory (for example, the memory 104) and processed by one or more processors (for example, the processors 102a, 102b, …, 102n), or can be run in the computer terminal 10 provided in Embodiment 1 as part of the device.

[0094] Embodiment 3

[0095] Embodiments of the present application can provide an electronic device, Figure 4 is a structural block diagram of an electronic device according to an embodiment of the present application. As Figure 4 indicated, the electronic device can include one or more (only one is shown in the figure) processors 402, a memory 404, a storage controller, and a peripheral interface, wherein the peripheral interface is connected with a radio frequency module, an audio module and a display. Figure 4

[0096] The memory can be used to store software programs and modules, such as program instructions / modules corresponding to the methods and devices in the embodiments of the present application. The processor executes various functions and data processing by running the software programs and modules stored in the memory, that is, implements the above-mentioned methods. The memory can include a high-speed random access memory, and can further include a non-volatile memory, such as one or more magnetic storage devices, flash memories, or other non-volatile solid-state memories. In some examples, the memory can further include a memory remotely arranged with respect to the processor, which can be connected to the terminal through a network. Examples of the above-mentioned network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof.

[0097] The processor can call the information and application programs stored in the memory through the transmission device to perform the following steps: extracting a behavior feature set of a target user group from a behavior feature database, wherein the behavior feature database contains behavior feature sets of N user groups, N is a positive integer, and the behavior features in the behavior feature set are characteristics of user operations on the interface of the application program of the financial institution; generating a random interaction path based on the behavior feature set of the target user group, converting the random interaction path into a test script through a preset test tool; executing the test script in a test environment to obtain a test result.

[0098] ​The processor can also call the information and application stored in the memory through the transmission device to perform the following steps: collect user operation logs on the interface of the financial institution's application through a preset embedding method, and collect target data from the user operation logs, wherein the target data includes at least one of the following: operation data, performance data and user feedback data; divide the user set of the financial institution based on user information to obtain N user groups, and extract the behavioral characteristics of each user in the N user groups from the target data; for each user group, cluster the behavioral characteristics of the user group through a clustering algorithm to obtain a behavioral feature set of the user group; and construct a behavioral feature database from the behavioral feature sets of the N user groups.

[0099] The processor can also call the information and application stored in the memory through the transmission device to perform the following steps: when the target data includes operation data, extracting operation behavior characteristics from the operation data, wherein the operation behavior characteristics include at least one of the following: the click frequency of each click area of ​​the interface, the page jump path, the page stay time and the user operation interval time; when the target data includes performance data, extracting performance characteristics from the performance data, wherein the performance characteristics include at least one of the following: the operation response time of the interface, the memory occupancy rate and the maximum occupied memory; when the target data includes user feedback data, extracting user feedback characteristics from the user feedback data, wherein the user feedback characteristics include at least one of the following: the user feedback frequency of the interface and the number of operation interruptions; and determining at least one of the following as a behavior characteristic: operation behavior characteristics, performance characteristics and user feedback characteristics.

[0100] The processor can also call the information and application stored in the memory through the transmission device to perform the following steps: determine M pages of the interface, and construct a page jump probability matrix for each page based on the behavioral feature set, where M is a positive integer; determine the starting page and the ending page among the M pages, starting from the starting page, randomly select the next page to jump to based on the page jump probability matrix of each page until jumping to the ending page; determine the random interaction path based on all pages and the jump order between the starting page and the ending page.

[0101] The processor can further call information and application programs stored in the memory through the transmission device to perform the following steps: extracting an actual page jump path from the test result, and determining whether the actual page jump path matches a preset page jump path; in the case that the actual page jump path does not match the preset page jump path, determining that the interface has a logic defect, detecting whether the preset page jump path covers all business scenarios to obtain a first detection result; in the case that the actual page jump path matches the preset page jump path, if a target page in the actual page jump path does not have a target page element, determining that the interface has a logic defect, detecting whether there is a missing parameter in an interface request parameter of the interface, and detecting whether a database and a data synchronization state of the interface are the same to obtain a second detection result; in the case that the actual page jump path matches the preset page jump path, if the target page in the actual page jump path has the target page element, determining that the interface does not have a logic defect; in the case that the interface has a logic defect, determining the first detection result or the second detection result as a logic defect reason.

[0102] The processor can further call information and application programs stored in the memory through the transmission device to perform the following steps: extracting a maximum operation response time and a memory occupancy rate from the test result; in the case that the maximum operation response time is less than a response time threshold and the memory occupancy rate is less than an occupancy rate threshold, determining that the interface does not have a performance defect; in the case that the maximum operation response time is greater than or equal to the response time threshold or the memory occupancy rate is greater than or equal to the occupancy rate threshold, determining that the interface has a performance defect; determining an operation time corresponding to the maximum operation response time and the memory occupancy rate, determining a target operation behavior corresponding to the operation time, and determining a defect node of the interface operated by the target operation behavior; performing performance testing on the defect node to obtain a test result, and determining the test result as a performance defect reason.

[0103] The processor can further call information and application programs stored in the memory through the transmission device to perform the following steps: extracting a user feedback frequency and an operation interruption number from the test result; in the case that the user feedback frequency is less than a feedback frequency threshold and the operation interruption number is less than an interruption number threshold, determining that the interface does not have a user feedback defect; in the case that the user feedback frequency is greater than or equal to the feedback frequency threshold, determining a feedback type and a feedback keyword; in the case that the operation interruption number is greater than or equal to the interruption number threshold, determining an operation interruption behavior and an interruption page; and determining the operation interruption behavior and at least one of the feedback type and the feedback keyword as a feedback defect reason.

[0104] The processor can further call information and application programs stored in the memory through the transmission device to perform the following steps: in the case that there is an interaction defect in the test result representation interface, determining a first defect type and a first defect reason of the interaction defect, obtaining a historical test record of the interface, wherein the interaction defect comprises at least one of the following: a logic defect, a performance defect and a user feedback defect; determining a target test record from the historical test record, wherein a second defect type of the target test record has a similarity to the first defect type greater than or equal to a first similarity threshold, and a second defect reason of the target test record has a similarity to the first defect reason greater than or equal to a second similarity threshold; and determining a historical optimization strategy corresponding to the target test record as an optimization strategy of the interface.

[0105] By adopting the embodiment of the application, a behavior feature set of a target user group is extracted from a behavior feature database. The behavior feature database contains behavior feature sets of N user groups, N is a positive integer, and the behavior features in the behavior feature set are characteristics of user operations on the interface of the application program of the financial institution. A random interaction path is generated based on the behavior feature set of the target user group, the random interaction path is converted into a test script by a preset test tool, and the test script is executed in a test environment to obtain a test result scheme. By constructing the behavior feature database, the behavior pattern of the user group is more accurately reflected. The random interaction path is generated based on the behavior features in the behavior feature database, and the test script converted from the random interaction path is used for testing, so that the simulation accuracy of the random interaction path is improved, thereby achieving the technical effect of improving the comprehensiveness of interface testing, and further solving the technical problem of incomplete interface interaction testing.

[0106] Those skilled in the art can understand that Figure 4 The structure shown is only schematic, and the electronic device can also be a terminal device such as a smart phone, a tablet computer, a palm computer, a Mobile Internet Device (MID), a PAD, etc. Figure 4 It does not limit the structure of the above-mentioned electronic device. For example, the electronic device can further include more or fewer components (such as a network interface, a display device, etc.) than Figure 4 shown, or have a different configuration than Figure 4 shown.

[0107] Those skilled in the art can understand that all or part of the steps in the various methods of the above-mentioned embodiments can be completed by programs instructing the related hardware of the terminal device, and the programs can be stored in a computer readable storage medium, which can include a flash disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, etc.

[0108] Example 4

[0109] The embodiment of the present application further provides a storage medium. Optionally, in this embodiment, the storage medium can be used to store the program code executed by the interface interaction testing method provided in the first embodiment.

[0110] Optionally, in this embodiment, the storage medium may be located in any computer terminal in a computer terminal group in a computer network, or in any mobile terminal in a mobile terminal group.

[0111] The present application also provides a computer program product, which, when executed on a data processing device, is suitable for executing the steps of the method for interactive testing of an interface.

[0112] The serial numbers of the above embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0113] In the above embodiments of the present application, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, please refer to the relevant description of other embodiments.

[0114] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0115] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0116] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0117] The integrated unit, if implemented in the form of a software function unit and sold or used as an independent product, can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application essentially or say the part that contributes to the prior art or the whole or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server or a network device, etc.) to execute all or part of the steps of the method described in the various embodiments of the present application. The aforementioned storage medium includes: a U disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a mobile hard disk, a magnetic disk or an optical disk, and various media that can store program codes.

[0118] The above is only the preferred embodiment of the present application, and it should be pointed out that for those skilled in the art, without departing from the principles of the present application, a number of improvements and refinements can be made, which should be considered as the protection scope of the present application.

Claims

1. An interactive testing method for an interface, characterized in that: include: Extracting a behavioral feature set of a target user group from a behavioral feature database, wherein the behavioral feature database contains behavioral feature sets of N user groups, where N is a positive integer, and the behavioral features in the behavioral feature set are features of user operations on an interface of an application program of a financial institution; Generate a random interaction path based on the behavioral characteristics of the target user group, and convert the random interaction path into a test script using a preset testing tool; Execute the test script in the test environment to obtain the test results.

2. The method according to claim 1, characterized in that The behavior feature database is obtained in the following manner: Collecting user operation logs on the interface of an application program of a financial institution using a preset tracking method, and collecting target data from the user operation logs, wherein the target data includes at least one of the following: operation data, performance data, and user feedback data; Dividing the user set of the financial institution based on the user information to obtain N user groups, and extracting the behavioral characteristics of each user in the N user groups from the target data; For each user group, clustering the behavioral characteristics of the user group using a clustering algorithm to obtain a behavioral characteristic set of the user group; The behavior feature database is constructed based on the behavior feature sets of the N user groups.

3. The method according to claim 2, characterized in that Extracting the behavioral characteristics of each user in the N user groups from the target data includes: In the case where the target data includes operation data, extracting operation behavior features from the operation data, wherein the operation behavior features include at least one of the following: click frequency of each click area of ​​the interface, page jump path, page dwell time, and user operation interval length; In the case where the target data includes performance data, extracting performance characteristics from the performance data, wherein the performance characteristics include at least one of the following: operation response time of the interface, memory usage, and maximum memory usage; In a case where the target data includes user feedback data, extracting user feedback features from the user feedback data, wherein the user feedback features include at least one of the following: user feedback frequency and operation interruption times of the interface; At least one of the following is determined as the behavior feature: an operation behavior feature, a performance feature, and a user feedback feature.

4. The method according to claim 1, wherein Generating random interaction paths based on the behavioral characteristics of the target user group includes: Determine M pages of the interface, and construct a page jump probability matrix for each page based on the behavioral feature set, where M is a positive integer; Determine a start page and an end page among the M pages, and starting from the start page, randomly select a next page to jump to based on a page jump probability matrix of each page until jumping to the end page; The random interaction path is determined based on all pages jumped from the start page to the end page and the jump order.

5. The method according to claim 1, wherein After obtaining the test results, the method further includes: Extracting an actual page jump path from the test result, and determining whether the actual page jump path matches a preset page jump path; If the actual page jump path does not match the preset page jump path, determining that there is a logic defect in the interface, detecting whether the preset page jump path covers all business scenarios, and obtaining a first detection result; In the case where the actual page jump path matches the preset page jump path, if the target page in the actual page jump path does not have a target page element, determining that there is a logic defect in the interface, detecting whether there are missing parameters in the interface request parameters of the interface, and detecting whether the data synchronization status of the database and the interface is the same, to obtain a second detection result; In a case where the actual page jump path matches the preset page jump path, if the target page elements all exist on the target pages in the actual page jump path, it is determined that there is no logic defect in the interface; In the case that a logic defect exists in the interface, the first detection result or the second detection result is determined as a cause of the logic defect.

6. The method according to claim 1, characterized in that After obtaining the test results, the method further includes: Extracting the maximum operation response time and memory usage from the test results; When the maximum operation response time is less than the response time threshold and the memory occupancy rate is less than the occupancy rate threshold, determining that the interface does not have a performance defect; When the maximum operation response time is greater than or equal to the response time threshold, or the memory occupancy rate is greater than or equal to the occupancy rate threshold, determining that the interface has a performance defect; Determine the operation time corresponding to the maximum operation response time and the memory usage, determine the target operation behavior corresponding to the operation time, and determine the defective node of the interface operated by the target operation behavior; A performance test is performed on the defective node to obtain a test result, and the test result is determined as a cause of the performance defect.

7. The method according to claim 1, characterized in that After obtaining the test results, the method further includes: Extracting user feedback frequency and operation interruption times from the test results; When the user feedback frequency is less than the feedback frequency threshold and the number of operation interruptions is less than the interruption number threshold, determining that there is no user feedback defect in the interface; When the user feedback frequency is greater than or equal to the feedback frequency threshold, determining the user feedback type and feedback keywords; When the number of operation interruptions is greater than or equal to the interruption number threshold, determining the operation interruption behavior and the interruption page; The operation interruption behavior and at least one of the following are determined as feedback defect causes: the feedback type and the feedback keyword.

8. The method according to claim 1, characterized in that After obtaining the test results, the method further includes: If the test result indicates that the interface has an interaction defect, determining a first defect type and a first defect cause of the interaction defect, and obtaining a historical test record of the interface, wherein the interaction defect includes at least one of the following: a logic defect, a performance defect, and a user feedback defect; Determining a target test record from the historical test records, wherein a similarity between a second defect type of the target test record and the first defect type is greater than or equal to a first similarity threshold, and a similarity between a second defect cause of the target test record and the first defect cause is greater than or equal to a second similarity threshold; The historical optimization strategy corresponding to the target test record is determined as the optimization strategy of the interface.

9. An interactive testing device for an interface, characterized in that: include: an extraction unit, configured to extract a behavioral feature set of a target user group from a behavioral feature database, wherein the behavioral feature database contains behavioral feature sets of N user groups, where N is a positive integer, and the behavioral features in the behavioral feature set are features of user operations on an interface of an application program of a financial institution; A generating unit, configured to generate a random interaction path based on a set of behavioral characteristics of a target user group, and convert the random interaction path into a test script using a preset testing tool; The execution unit is used to execute the test script in the test environment to obtain the test result.

10. A computer program product comprising computer instructions, characterized in that When the computer instructions are executed by a processor, the steps of the interface interactive testing method described in any one of claims 1 to 8 are implemented.

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