Cross-platform compatibility automatic test system for mobile application

Abstract

The invention discloses a cross-platform compatibility automatic test system for mobile applications. The cross-platform compatibility automatic test system comprises a cross-platform adaptation module, a data acquisition module, an optimization module, a content generation module, a test module, an evaluation module and a template refreshing module. The method comprises the following steps of: generating a verification numerical value in an initial range by uniformly defining a variable set of an operating system, an equipment type and a network environment; optimizing the verification group based on a machine learning algorithm; dynamically generating content items in combination with a template specification structure; verification result information is generated through the test module, and the evaluation module evaluates the content items one by one and marks unqualified content; and the template refreshing module adjusts a recommendation algorithm, a page layout and a dynamic loading strategy according to an evaluation result to form an optimized feedback closed loop. According to the system, the test coverage rate and efficiency are improved, the adaptability of content generation logic is enhanced, and the cross-platform compatibility of mobile applications and the user experience are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of software testing, and in particular to a cross-platform compatibility automated testing system for mobile applications. Background Art

[0002] With the rapid popularization of mobile applications, the diversity of mobile device types and operating systems has increased significantly. Common operating systems include Android, iOS, etc., and device types cover various smart terminals of different brands, configurations and specifications. In this complex and diverse ecological environment, mobile applications need to maintain consistency in functionality and performance across different platforms, devices and network environments. However, the differences in multi-platform environments have brought huge technical challenges to application compatibility testing.

[0003] Traditional compatibility testing methods mainly rely on manual testing or simple scripted automated testing. These methods have many limitations, the first of which is the lack of test coverage. Manual testing is difficult to fully cover all possible combinations of variables, such as the cross-influence between different operating system versions, device types, and network environments. Especially in extreme scenarios or boundary conditions, it is often impossible to fully verify, resulting in the omission of potential problems. In addition, manual testing is inefficient, requires a lot of time and human resources, and is difficult to adapt to the rapid development rhythm of frequent updates and iterations of current mobile applications.

[0004] Existing automated testing tools also have the problem of poor adaptability. Many tools are often designed for a single platform and lack the ability to adapt to multiple platforms. This limitation makes it difficult to uniformly manage and verify the compatibility of multiple platforms. In addition, mobile applications involve real-time user interaction and dynamic changes in the external environment during operation. Traditional testing methods are difficult to simulate these scenarios and conduct effective verification, further exacerbating the complexity of testing.

[0005] Feedback and analysis of test data are also weak links in traditional methods. The existing testing process usually records and analyzes test results in a rough manner, which cannot provide sufficient support for subsequent optimization. This lack of effective data-driven testing makes the closed loop between testing and optimization inefficient, hindering the continuous improvement of mobile application quality.

[0006] As the complexity of mobile applications continues to increase, the number and relevance of test variables are also growing exponentially. For example, network latency, device resolution, operating system permission management methods, etc. can significantly affect application functionality and user experience. However, current testing methods are still in the early stages of optimizing multi-dimensional variable combinations, and cannot fully utilize historical test data and intelligent algorithms to improve test coverage and optimization efficiency.

[0007] In this context, compatibility testing of mobile applications in cross-platform scenarios faces unprecedented challenges. The market urgently needs an efficient and intelligent automated testing system to solve the complexity brought by the cross-platform and multi-environment variables, improve test coverage and efficiency, and realize closed-loop management of testing and optimization, providing strong guarantees for the reliability and user experience of mobile applications. Summary of the invention

[0008] In order to solve the above problems in the prior art, the present invention proposes a cross-platform compatibility automated testing system for mobile applications, comprising:

[0009] A cross-platform adaptation module, which is used to uniformly define a set of variables for multiple mobile application platforms, including compatibility features for different operating systems, device types, and network environments;

[0010] A data acquisition module, used for obtaining initial ranges of multiple variables in the variable set, and generating multiple verification values ​​for each variable according to its initial range;

[0011] An optimization module, used to optimize and generate multiple verification groups based on historical test data and machine learning algorithms, each verification group being adapted to a variable combination of different platforms, and provide the multiple optimized verification groups to a content generation module;

[0012] A content generation module, for dynamically generating mobile terminal content items adapted to the target platform in a real-time content generation unit for each optimized verification group according to a combination of verification values ​​of the verification group as an input of a template specification structure;

[0013] A testing module, for associating each mobile terminal content item output by the content generation module with a verification value of a verification group that generates the content item, generating and outputting verification result information;

[0014] An evaluation module, used to evaluate the compatibility performance of content items on multiple mobile application platforms according to the verification result information output by the test module, mark content items that do not meet the compatibility specifications and record the reasons;

[0015] The template refresh module is used to refresh the template specification structure according to the evaluation results, adjust the recommendation algorithm, page layout rules and dynamic adjustment strategy, and reintroduce the refreshed template specification structure into the test process to form an optimization feedback loop.

[0016] The cross-platform adaptation module includes:

[0017] The operating system adaptation unit is used to extract the operating system type and version information of the target device, analyze the permission management method, resource allocation strategy and interface rendering mechanism, and generate operating system difference attribute variables;

[0018] The device type adaptation unit is used to analyze the hardware characteristics of the target device, such as screen resolution, processor performance, and memory capacity, and to organize the device characteristic variables into a multi-dimensional matrix;

[0019] The network environment adaptation unit is used to abstract the characteristic variables of various network environments, including network delay range, bandwidth upper limit and dynamic fluctuation frequency;

[0020] The unified variable generation unit is used to integrate the variable sets of the operating system, device type and network environment to generate a unified variable model to support subsequent testing and optimization processes.

[0021] The data acquisition module comprises:

[0022] The initial range definition unit is used to determine the initial range of each variable in the variable set, including network latency, screen resolution, CPU performance, and memory capacity;

[0023] A verification value generation unit, used to generate a verification value according to an initial range, wherein the generation rules include a uniform distribution generation method, a random distribution generation method, an extreme value generation method, and a distribution function generation method;

[0024] The data storage and output unit is used to store the generated verification values ​​in the form of structured data and provide them to subsequent modules for use.

[0025] The optimization module includes:

[0026] A feature vector extraction unit is used to convert the variable combination of each verification group into a feature vector and encode the non-numeric variables;

[0027] A classification model training unit, which is used to train a classification model based on historical test data, evaluate the effectiveness of the validation group, and calculate the importance of variables;

[0028] A redundant data removal unit, used to remove redundant data according to the distance of feature vectors through a clustering algorithm and select the most representative verification group;

[0029] An optimized verification group generation unit is used to generate an optimized verification group according to the output results of the classification model and the cluster analysis results, and covers the key scenarios in the initial verification value set;

[0030] The verification group output unit is used to output the optimized verification group in the form of structured data for use by subsequent modules.

[0031] The content generation module comprises:

[0032] A template specification structure loading unit, used to load a template specification structure, wherein the template specification structure includes a page layout module, a recommendation algorithm module and a dynamic rule adjustment module;

[0033] A parameter parsing and mapping unit, for receiving verification group parameters and mapping the verification group parameters to corresponding parameters in the template specification structure;

[0034] A dynamic content generation unit, which is used to call the recommendation algorithm according to the template specification structure and the validation group parameters, dynamically adjust the resource loading strategy, and arrange the content items according to the page layout rules;

[0035] A logging unit is used to associate and log the generated content items with the verification group parameters and the performance indicators of the generated results.

[0036] The test module includes:

[0037] A content item and verification group associating unit, used to receive a content item and its corresponding verification group parameter, extract a content item feature and establish an associative record with the verification group parameter;

[0038] A verification result generating unit, configured to generate verification result information based on the performance data of the content item, wherein the verification result information includes:

[0039] Display quality indicators, including resolution and loading completeness;

[0040] User interaction data, including click-through rate and dwell time;

[0041] performance data, including build time and resource consumption;

[0042] The data output unit is used to output the verification result information to the evaluation module in a structured format.

[0043] The evaluation module includes:

[0044] An evaluation specification storage unit, used to store evaluation specifications related to display quality, performance, and user interaction;

[0045] The verification result item-by-item evaluation unit is used to compare the verification result information with the evaluation specifications item by item, determine whether each indicator is qualified, and obtain a comprehensive evaluation judgment of the content item based on the evaluation results of multiple indicators;

[0046] The non-conforming content marking unit is used to mark content items that do not meet the assessment specifications and record the content item identification, non-conforming reasons and recommended adjustment measures;

[0047] The data output unit is used to output the evaluation results and marking information to the template refresh module.

[0048] The template refreshing module comprises:

[0049] A data receiving unit, used to receive the reasons for non-conformity and recommended adjustment measures fed back by the evaluation module;

[0050] A recommendation algorithm adjustment unit, used to adjust the recommendation algorithm weight based on the evaluation results, and improve the recommendation matching degree by optimizing the heat weight of the recommended content and the user interest weight;

[0051] Page layout optimization unit, used to adjust page layout rules, including content quantity and module priority, according to content loading completeness or user interaction rate;

[0052] Dynamic adjustment strategy improvement unit, used to optimize dynamic loading strategy, including introducing asynchronous loading mechanism and adjusting resource loading priority according to network conditions;

[0053] The template test feedback unit is used to introduce the refreshed template specification structure into the test process and generate new test data to form an optimization feedback loop.

[0054] The cross-platform compatibility automated testing system for mobile applications of the present invention has the following beneficial effects:

[0055] Through the cross-platform adaptation module, the variable sets of multiple mobile application platforms are uniformly defined to achieve comprehensive adaptation to different operating systems, device types and network environments, ensuring wide coverage of the test.

[0056] The data acquisition module uses a variety of verification value generation rules to efficiently generate multi-dimensional data that meets the test requirements and provide high-quality input for subsequent tests.

[0057] The optimization module combines historical test data and machine learning algorithms to remove redundant verification groups and generate optimized verification groups covering key scenarios, significantly improving test efficiency.

[0058] The content generation module dynamically generates content items based on template specification structure and verification group parameters, supports personalization and platform optimization, and ensures the performance and matching of the generated content.

[0059] The testing module and the evaluation module are combined to generate and evaluate presentation quality, user interaction, and performance data, providing accurate judgments on the quality and performance of content items, and supporting issue tracking through a tagging mechanism.

[0060] The template refresh module dynamically adjusts the recommendation algorithm, page layout and loading strategy based on the evaluation feedback. The optimized template specification structure re-enters the testing process, forming a feedback loop of continuous improvement.

[0061] This system realizes efficient and intelligent automated testing, significantly improving the cross-platform compatibility and user experience of mobile applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of the present application, but do not constitute an improper limitation of the present invention. In the drawings:

[0063] Figure 1 A schematic diagram of the structure of a cross-platform compatibility automation testing system for mobile applications is shown. DETAILED DESCRIPTION

[0064] The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments, wherein the illustrative embodiments and descriptions are only used to explain the present invention but are not intended to limit the present invention.

[0065] like Figure 1 As shown, this embodiment provides a cross-platform compatibility automated testing system for mobile applications. The system includes a cross-platform adaptation module 101, a data acquisition module 102, an optimization module 103, a content generation module 104, a test module 105, an evaluation module 106 and a template refresh module 107, and each module is connected through a system bus and works in coordination.

[0066] The cross-platform adaptation module 101 aims to uniformly define the variable set of multiple mobile application platforms, covering the compatibility characteristics of operating systems, device types and network environments. This module generates a unified variable model applicable to multiple platforms by abstracting the key attributes of different platforms to support subsequent testing and optimization processes.

[0067] 1. Compatibility and adaptation of operating systems

[0068] The cross-platform adaptation module first analyzes the operating system characteristics of the target mobile application's running platform, including but not limited to the following steps:

[0069] Collect operating system information: Extract the target device’s operating system type and its version information, such as Android (version 10, 11, 12, etc.), iOS (version 14, 15, etc.).

[0070] Analyze the functional differences of operating systems: Analyze the specific permission management methods, resource allocation strategies, and interface rendering mechanisms of different operating systems. For example, the dynamic permission model in Android and the privacy protection mechanism in iOS have significant differences in resource calls.

[0071] Define operating system variables: Based on the above analysis, the different attributes of operating systems are abstracted into a set of common variables, such as "authority management complexity" and "resource allocation priority", to uniformly represent the key characteristics of different operating systems.

[0072] 2. Compatibility and adaptation of device types

[0073] The cross-platform adaptation module analyzes the hardware characteristics of the target device and generates a set of variables that adapt to different device types. The specific steps include:

[0074] Hardware attribute analysis: Extract key indicators such as the device's screen size, resolution, processor performance (such as the number of cores and main frequency of the ARM architecture), and memory capacity.

[0075] Device feature classification: Classify devices according to their hardware features, such as high-end devices, mid-range devices, and low-end devices, and abstract a set of variables for their resource allocation capabilities and performance. For example:

[0076] Screen resolution variables: 720p, 1080p, 4K;

[0077] Memory capacity variables: 4GB, 6GB, 8GB.

[0078] Generate device characteristic variable model: Based on the above attributes, the device characteristic variables are uniformly organized into a multi-dimensional matrix to support verification group generation.

[0079] For example:

[0080] 3. Compatibility adaptation of network environment

[0081] The cross-platform adaptation module supports the abstraction of variables for multiple network environments to reflect the impact of different connection conditions on application behavior. The specific steps include:

[0082] Network type analysis: covers mainstream network types such as Wi-Fi, 4G, and 5G, and records their latency, bandwidth, and stability characteristics. For example:

[0083] Wi-Fi: latency 10-50ms, bandwidth 50-500Mbps;

[0084] 4G: latency 30-100ms, bandwidth 10-50Mbps;

[0085] 5G: latency 1-10ms, bandwidth 100-1000Mbps.

[0086] Dynamic network simulation: Define the changing trends of dynamic network conditions through variables, such as simulating network fluctuations (sudden drop in bandwidth, surge in latency).

[0087] Define network variables: Based on network type and conditions, a unified set of network variables is abstracted, such as "delay range", "bandwidth upper limit" and "fluctuation frequency".

[0088] 4. Unified variable model generation

[0089] Through the above-mentioned analysis of the characteristics of operating systems, device types, and network environments, the cross-platform adaptation module integrates the differentiated attributes of multiple platforms into a unified variable model. The output form of the variable model can be a matrix or a configuration file, including:

[0090] Platform feature identifiers (e.g., OS version, hardware performance category);

[0091] The value range and distribution rules of the variable set;

[0092] Data interface with subsequent test modules.

[0093] This unified variable model supports subsequent verification group generation and content item testing, ensuring that cross-platform testing can efficiently adapt to diverse scenarios and cover key differences, ensuring system compatibility and test accuracy.

[0094] The data acquisition module 102 is used to extract the initial ranges of multiple variables from the variable set and generate multiple verification values ​​to form a test data set. The initial ranges of the variables reflect the different operating conditions that the target system may encounter, and the verification values ​​provide the basic input for multi-dimensional testing.

[0095] 1. Definition of initial scope

[0096] The data acquisition module first determines the initial range of each variable in the variable set according to its characteristics. The variable type and its corresponding initial range may include but are not limited to the following:

[0097] Network latency: Set the initial range to 10 ms to 200 ms to cover differences in application performance in low-latency and high-latency environments.

[0098] Screen resolution: The initial range is defined as 720p (1280x720 pixels), 1080p (1920x1080 pixels), and 4K (3840x2160 pixels) to cover the display capabilities of different devices.

[0099] CPU performance: Based on the processor characteristics of the target device, the initial range is set to 1.2GHz to 3.0GHz, covering the computing power of low-end to high-end devices.

[0100] Memory capacity: The initial range is 2GB, 4GB, and 8GB to reflect memory usage scenarios for resource-constrained devices and high-performance devices.

[0101] These range values ​​can be obtained through data statistics, industry standards or actual application scenario analysis.

[0102] 2. Verify the method of generating numerical values

[0103] In order to generate verification values ​​that meet the test coverage requirements, the data acquisition module provides the following generation rules:

[0104] Uniform distribution generation method

[0105] Divide the initial range of the variable into several segments evenly, and take one or more values ​​in each segment. For example, for the initial range of network delay from 10 milliseconds to 200 milliseconds, divide it into 10 millisecond intervals, take one value in each segment, and generate a verification value set:

[0106] {10,20,30,…,200}

[0107] Random distribution generation method

[0108] A certain number of validation values ​​are randomly generated from the initial range, with additional constraints to control bias. For example, for screen resolution, test combinations are randomly generated from 720p, 1080p, and 4K to ensure that each resolution is covered in the test set.

[0109] Extreme value generation method

[0110] Select boundary values ​​or values ​​close to the boundary within the initial range of the variable for testing. For example, for the memory capacity variable, select 2GB (low-end devices) and 8GB (high-end devices) as verification values ​​to cover test requirements under extreme conditions.

[0111] Distribution Function Generation Method

[0112] If the initial range of some variables conforms to a known distribution model (such as normal distribution or exponential distribution), the verification value is generated through the distribution function. For example, assuming that the distribution of the CPU performance variable is a normal distribution (mean 2.5GHz, standard deviation 0.5GHz), the generated verification values ​​may include:

[0113] {2.0,2.5,3.0}

[0114] 3. Verify the storage and output of values

[0115] The generated verification value is stored in the form of structured data and used by subsequent modules. The storage format may include:

[0116] Two-dimensional table: each variable is a column, and each row corresponds to a set of validation values. For example:

[0117] JSON file: Stored in the form of nested key-value pairs, which is convenient for direct import into subsequent test modules. The following is an example:

[0118] 4. Automated Implementation

[0119] The implementation of the data acquisition module can be automatically completed through program code, such as using Python to write a generation script. For the extreme value generation method or the distribution function generation method, the module can call a scientific computing library (such as NumPy or SciPy) to generate verification values ​​to ensure the accuracy and rationality of the data.

[0120] Through the above process, the data acquisition module 102 realizes the function of defining the initial range from the variable set and generating multi-dimensional verification values, providing high-quality test input for subsequent modules.

[0121] The optimization module 103 optimizes the verification value set generated by the data acquisition module through historical test data and machine learning algorithms to form a verification group suitable for different platforms, thereby improving test coverage and efficiency.

[0122] 1. Extraction of feature vectors

[0123] The optimization module first converts the variables in each validation value set into a feature vector to characterize the characteristics of the variable combination. The feature vector is composed of the variable values ​​in the validation group. The specific process is as follows:

[0124] Suppose a validation group contains the following variables:

[0125] User click frequency (30 times / minute);

[0126] Network latency (50 milliseconds);

[0127] Screen resolution (1080p).

[0128] The corresponding eigenvector is:

[0129] V = [30,50,1080p]

[0130] To facilitate subsequent calculations, non-numeric variables (such as resolution) are converted into numerical values ​​through encoding. For example:

[0131] 720p is mapped to 1, 1080p is mapped to 2, and 4K is mapped to 3.

[0132] The optimization module uses feature vectors to describe the variable combination characteristics of each verification group to form a feature vector set.

[0133] 2. Training of classification models

[0134] Based on historical test data, the optimization module trains the classification model to evaluate the effectiveness of the validation group. The specific steps are as follows:

[0135] Data annotation: Extract validation groups and their corresponding test results from historical test data and assign labels (such as pass or fail) to each feature vector.

[0136] Model selection: Select a classification model such as random forest, support vector machine (SVM), or neural network based on the characteristics of the data.

[0137] Model training: The labeled feature vectors and labels are input into the model as training data. For example, for the random forest model, the training process is as follows:

[0138] Construct multiple decision trees, each of which predicts the test results based on the characteristics of the validation group;

[0139] Combine the prediction results of multiple decision trees and calculate the importance of each variable on the test results.

[0140] 3. Removal of redundant data

[0141] The optimization module removes redundant data in the feature vector set through a clustering algorithm to reduce the generation of duplicate verification groups. The specific process is as follows:

[0142] Distance metric: Defines the distance between validation sets, such as Euclidean distance: Among them, V1 and V2 represent two eigenvectors respectively, n is the number of variables; V 1i Represents the i-th dimension coordinate value of the first point, V 2i Represents the i-th dimension coordinate value of the second point.

[0143] Clustering algorithm: K-means or hierarchical clustering algorithm is used to cluster the validation groups according to the distance of feature vectors. For example, if the distance between two validation groups is less than the set threshold, they are classified into the same category.

[0144] Representative validation group selection: Select the most representative validation group from each category and delete the remaining redundant validation groups. For example, select the validation group with the smallest feature vector distance from the category center point.

[0145] 4. Optimize the generation of validation groups

[0146] According to the output results of the classification model and the cluster analysis results, the optimization module generates an optimized validation group.

[0147] Specific methods include:

[0148] Priority assignment: Assign higher priority to key variable combinations based on the variable importance calculated by the classification model. For example, if "network delay" has a significant impact on the test results, the validation group containing different network delay values ​​is retained first.

[0149] Coverage analysis: Ensure that the generated verification group covers key scenarios in the initial verification value set, such as test conditions in low-latency and high-latency environments.

[0150] 5. Optimize the output of the validation group

[0151] The optimized validation group is output in the form of structured data for use by the content generation module. The output format can include:

[0152] Two-dimensional table: with validation group number and variable combination as columns:

[0153]

[0154] Through the above implementation, the optimization module effectively reduces redundant data, generates an optimized verification group that can cover key scenarios, improves test efficiency and resource utilization, and provides high-quality support for the test input of the content generation module.

[0155] The content generation module 104 dynamically generates mobile terminal content items adapted to the target platform in the real-time content generation unit. By loading the template specification structure and combining the verification group parameters, the recommendation algorithm and page layout rules are dynamically called to achieve personalized content generation and platform optimization.

[0156] 1. Loading of template specification structure

[0157] The content generation module first loads the template specification structure to provide rule guidance for dynamic content generation. The template specification structure includes the following core modules:

[0158] Page layout module: defines the arrangement and display rules of content items. For example, the news page uses a list layout, and the recommended video module uses a grid layout.

[0159] Recommendation algorithm module: provides recommendation strategies for content generation. For example, it prioritizes short videos or long articles based on user interests.

[0160] Dynamic rule adjustment module: allows dynamic adjustment of generation logic based on external conditions (such as network latency or device performance), for example, giving priority to displaying static content in high-latency environments.

[0161] The template specification structure is stored in a parameterized form to support dynamic adjustment. For example:

[0162] {

[0163] "Layout rule":{"Type":"List","Number of entries per page":10},

[0164] "Recommendation algorithm":{"Heat weight":0.5,"Interest weight":0.5},

[0165] "Dynamic Adjustment":{"Network Priority":"Low"}

[0166] }

[0167] 2. Parsing and mapping of verification group parameters

[0168] The content generation module receives the validation group parameters provided by the optimization module and maps them to the corresponding parameters in the template specification structure. For example:

[0169] Verify that the group parameter "Network Delay = 100 ms" is mapped to the "Network Priority" parameter of the dynamic adjustment module;

[0170] The verification group parameter "user click frequency = 30 times / minute" is mapped to the "interest weight" parameter of the recommendation algorithm module;

[0171] Verify that the group parameter “Screen Resolution = 1080p” is mapped to the “Display Size” parameter of the page layout module.

[0172] The mapping rules can be implemented through a lookup table, for example:

[0173] {

[0174] "Network delay":{"50ms":"High priority","100ms":"Medium priority","200ms":"Low priority"},

[0175] "Click frequency":{"30":"Interest weight = 0.7","50":"Interest weight = 0.5"}

[0176] }

[0177] 3. Dynamic content generation process

[0178] According to the template specification structure and verification group parameters, the content generation module dynamically generates mobile content items. The specific process includes the following steps:

[0179] Calling the recommendation algorithm: Calling the recommendation algorithm based on the user behavior related parameters (such as click frequency and dwell time) in the verification group. For example, when "click frequency = 30 times / minute", select algorithm model A to give priority to recommending short video content.

[0180] Dynamically adjust content logic: Dynamically adjust resource loading strategies based on network-related parameters (such as latency and bandwidth). For example, when the network latency is 100 milliseconds, switch to low-resolution resources to speed up loading.

[0181] Generate page layout: Arrange content items according to the rules of the page layout module. For example, a news page generates a list of 10 news items, each of which includes a title, thumbnail, and description information.

[0182] 4. Recording of content generation logs

[0183] Each generated content item is associated with the validation group parameters and a generation log is recorded, including the following information:

[0184] Characteristic information of the content item (such as type, generation time, resolution);

[0185] Verify group parameters (such as network latency, click frequency);

[0186] Performance indicators of the generated results (such as generation time, loading completion rate).

[0187] The storage format of log records is as follows:

[0188] {

[0189] "Generation time":"2024-11-26 10:00:00",

[0190] "Content Type":"Recommended Video",

[0191] "Verification Group":{"Click Frequency":30,"Delay":100},

[0192] "Performance indicators": {"Loading time":"800ms","Completion rate":"95%"}

[0193] }

[0194] 5. Example Operation

[0195] Assuming that the parameters of a verification group are "network delay = 100 milliseconds, click frequency = 30 times / minute, resolution = 1080p", the operation steps of the content generation module are:

[0196] Call the recommendation algorithm to generate a recommendation list containing 5 short videos;

[0197] Adjust resource loading logic to prioritize loading low-resolution thumbnails to speed up page display;

[0198] The layout module arranges the list of videos, displaying the thumbnail, title, and duration of each video.

[0199] The generated content items and log records are stored simultaneously to support subsequent testing and evaluation.

[0200] Through the above implementation, the content generation module can dynamically generate optimized content items according to the characteristics of the target platform, adapt to different scenarios and external conditions, provide users with the best experience, and ensure the integrity and consistency of the test data.

[0201] The testing module 105 is responsible for performing correlation analysis on the mobile terminal content items output by the content generation module and generating verification result information. This module generates display quality indicators, user interaction data and performance data by analyzing the content item features and the parameters of the verification group, and outputs them in a structured format for use by the evaluation module.

[0202] 1. Association of content items with verification groups

[0203] The test module receives the output content items and the corresponding verification group information of the content generation module, and forms a correlation record by associating the content item features with the verification group parameters. The specific steps are as follows:

[0204] Content item feature extraction: Obtain feature information of content items, such as:

[0205] Type: recommended videos, news lists, image resources;

[0206] Format: resolution (such as 1080p), file size (such as 1MB).

[0207] Verification group parameter mapping: Extract the verification group parameters corresponding to the content items, such as "network delay = 100ms, click frequency = 30 times / minute".

[0208] Association record generation: Establish an association relationship between content items and verification groups and generate records. For example:

[0209] 2. Generation of verification result information

[0210] The test module analyzes the performance data of the content item and generates verification result information. The verification result information includes the following three dimensions:

[0211] Display quality indicators

[0212] Resolution: Detects whether the resolution of a content item matches the expected one, such as verifying that a video is displayed in 1080p.

[0213] Loading completeness: Calculates the percentage of content loaded. For example: Loading If the total resource size is 2MB and 1.8MB has been loaded, the loading completeness is 90%.

[0214] User interaction data

[0215] Click-through rate: Counts the number of times a user clicks on a content item relative to the number of times it is displayed. For example: If the number of clicks is 30 and the number of impressions is 200, the CTR is 15%.

[0216] Dwell time: records the length of time a user stays on a content item, for example, 120 seconds.

[0217] Performance Data

[0218] Generation time: records the time required for the content generation module to output content items. For example, it takes 500 milliseconds to generate a video recommendation list.

[0219] Resource consumption: Statistics on CPU and memory usage during content generation. For example, the CPU usage is 30% and the memory usage is 50MB.

[0220] 3. Data formatting output

[0221] The test module outputs the generated verification result information to the evaluation module in a structured format, which supports the following formats: table, JSON, or log file:

[0222]

[0223] Log files

[0224] [2024-11-26 10:00:00] Verification group: [click frequency = 30, delay = 100]

[0225] Content item: Recommended Video, Resolution = 1080p

[0226] Verification results: Loading completeness = 95%, generation time = 500ms, click rate = 15%

[0227] 4. Example Process

[0228] Assuming that a content item is a list of recommended videos, and its corresponding verification group parameters are "network delay = 100ms, click frequency = 30 times / minute", the test module performs the following operations:

[0229] Extract content item features: "Recommended video, resolution = 1080p, size = 2MB".

[0230] Associate verification group parameters: "Network delay = 100ms, click frequency = 30 times / minute".

[0231] Generate verification result information:

[0232] Loading completeness: 95%

[0233] Generation time: 500ms

[0234] Click-through rate: 15%

[0235] Output verification result information to the evaluation module for subsequent evaluation of the compliance of the content items.

[0236] Through the above implementation, the test module realizes the effective association between content items and verification groups, and generates comprehensive verification result information, providing reliable data support for the evaluation module.

[0237] The evaluation module 106 receives the verification result information generated by the test module 105, and systematically evaluates the content items according to the preset evaluation specifications to determine whether they meet the quality and performance requirements. If the content items do not meet the specifications, the module will mark them, record the reasons for non-compliance and the recommended adjustment measures, and provide data support for subsequent optimization.

[0238] 1. Setting of evaluation criteria

[0239] The evaluation module presets the evaluation specifications of the content items according to the system requirements and actual application scenarios as the basis for the evaluation and verification result information. The evaluation specifications may include the following:

[0240] Display quality requirements:

[0241] The relevance score of the recommended content should be greater than or equal to 4.0 (out of 5.0);

[0242] Page loading completeness should be no less than 95%.

[0243] Performance requirements:

[0244] Content generation time should be less than 1000 milliseconds;

[0245] Resource consumption (such as memory usage, CPU usage) should be within the specified range.

[0246] User interaction requirements:

[0247] The click-through rate should be greater than or equal to 15%;

[0248] The page dwell time should not be less than 30 seconds.

[0249] Evaluation specifications are stored in parameterized form, for example:

[0250] {

[0251] "Display quality":{"Relevance score":">=4.0","Loading completeness":">=95%"},

[0252] "Performance":{"Generation time":"<1000ms","Resource consumption":{"Memory":"<500MB","CPU":"<40%"}},

[0253] "Interaction":{"Click rate":">=15%","Dwell time":">=30s"}

[0254] }

[0255] 2. Item-by-item evaluation of verification results

[0256] The evaluation module checks the verification result information item by item. The specific steps include:

[0257] Single indicator evaluation:

[0258] For each verification result indicator, compare its value with the corresponding evaluation specification. For example:

[0259] If the "Recommended Content Relevance Score" in the verification result is 3.8, and the specification requires no less than 4.0, then the assessment is unqualified;

[0260] If the Generation Time is 900 milliseconds, and the specification requires less than 1000 milliseconds, then the assessment passes.

[0261] Comprehensive Assessment:

[0262] Summarize the evaluation results of all individual indicators and make a final decision according to the system rules. For example:

[0263] If any key indicator (such as "relevance score" or "generation time") fails, the overall assessment will be judged as failed.

[0264] 3. Marking non-compliant content items

[0265] For content items that are judged as non-compliant, the assessment module marks them and records the following information:

[0266] Content item identification: type of content item, generation time, verification group parameters, etc. For example: "recommended video list, generation time = 1200 milliseconds, click frequency = 30 times / minute".

[0267] Reason for failure: Describe the specific indicators and their values ​​that failed. For example: "Relevance score = 3.5 (less than 4.0), Generation time = 1200 milliseconds (exceeded 1000 milliseconds)".

[0268] Recommended adjustment measures: Provide optimization suggestions based on the reasons for non-conformity, such as:

[0269] If the "relevance score" is too low, it is recommended to adjust the recommendation algorithm weight to increase the priority of user interest;

[0270] If the "generation time" exceeds the standard, it is recommended to optimize the content generation logic to reduce computing overhead.

[0271] Tag information is stored in a structured format, an example is as follows:

[0272] {

[0273] "Content item":{"Type":"Recommended video list","Generation time":"1200ms","Verification group":{"Click frequency":30,"Delay":100}},

[0274] "Reason for failure": ["Relevance score = 3.5 (<4.0)", "Generation time = 1200ms (>1000ms)"],

[0275] "Suggested measures": ["Optimize recommendation algorithm weight", "Optimize content generation logic"]

[0276] }

[0277] 4. Data Output

[0278] The evaluation results and marking information are output to the template refresh module to support subsequent template optimization. The output forms include:

[0279] Real-time monitoring interface: Dynamically displays unqualified content items and their marking information to facilitate real-time adjustments.

[0280] Log file or database storage: to facilitate subsequent batch analysis and optimization.

[0281] 5. Example Operation

[0282] Assuming that the content item generated by a verification group "click frequency = 30 times / minute, network delay = 100 milliseconds" is "recommended video list", the verification result information is:

[0283] Recommended content relevance score: 3.5;

[0284] Page loading completeness: 97%;

[0285] Generation time: 1200 milliseconds.

[0286] The operations of the evaluation module include:

[0287] Item by Item Assessment:

[0288] "Relevance score = 3.5" does not meet the specification (required ≥ 4.0) and is marked as unqualified;

[0289] “Loading completeness = 97%” meets the specification (requirement ≥ 95%) and is marked as qualified;

[0290] "Generation time = 1200 ms" exceeds the specification (required to be < 1000 ms) and is marked as unqualified.

[0291] Comprehensive evaluation: Due to failure of key indicators (relevance score, generation time), the overall evaluation result of the content item is judged as failed.

[0292] Record mark information:

[0293] Reason for failure: relevance score is below specification, generation time is out of range;

[0294] Suggested measures: Optimize the recommendation algorithm and simplify the generation process.

[0295] Through the above implementation, the evaluation module can comprehensively and accurately determine the compliance of the content items and provide reliable data support for subsequent template updates.

[0296] The template refresh module 107 dynamically refreshes the template specification structure based on the content item evaluation results fed back by the evaluation module to optimize the content generation logic and improve the system adaptability. The refreshed template specification structure is reintroduced into the test process to form an optimization feedback loop of continuous improvement.

[0297] 1. The basis for updating the template specification structure

[0298] The template refresh module receives tag information from the evaluation module, including the following:

[0299] Failure Reason: Records the specific reason why the content item failed the assessment, such as "Recommendation content relevance score is insufficient" or "Generation time exceeded."

[0300] Recommended adjustment measures: Provide optimization suggestions for the reasons for non-compliance, such as adjusting the weight of the recommendation algorithm, optimizing the page layout, or improving the dynamic loading strategy.

[0301] Refresh basis ensures that the template update process is targeted and efficient.

[0302] 2. Adjustment of recommendation algorithm weights

[0303] In response to the evaluation results that the recommended content is not relevant enough, the template refresh module optimizes the recommendation algorithm weight to improve the matching degree of the recommended content. The adjustment steps include:

[0304] Determine variable influencing factors: Calculate the contribution of user behavior variables (such as click frequency and dwell time) to the recommendation effect based on the evaluation results.

[0305] Weight formula adjustment: adjust the weight factor of the recommendation algorithm, for example: S = w1·H+w2·I, where S is the recommendation score, H represents the popularity of the content, I represents the user's interest, and w1 and w2 are weight factors. If the evaluation results show that the interest relevance is insufficient, reduce the value of w1 and increase the value of w2.

[0306] Updated weight configuration: For example:

[0307] {

[0308] "Heat weight": 0.4,

[0309] "Interest Weight": 0.6

[0310] }

[0311] 3. Optimization of page layout rules

[0312] If the page loading completeness or user interaction rate is lower than expected, the template refresh module improves the user experience by adjusting the page layout rules. The optimization steps include:

[0313] Adjust the amount of content: Reduce the amount of content loaded at a time on the page. For example, reduce the number of content displayed per screen from 10 to 5 to reduce the pressure of loading resources.

[0314] Module priority adjustment: Rearrange the display order of page modules and place modules with high user interest (such as recommended videos) at the top of the page.

[0315] Example layout rule update:

[0316] {

[0317] "Number of contents per screen":5,

[0318] "Module Order": ["Recommended Videos", "News Content", "Advertisements"]

[0319] }

[0320] 4. Improvements in dynamic adjustment strategies

[0321] For evaluation results that exceed the generation time limit or are incompletely loaded, the template refresh module optimizes the dynamic adjustment strategy to improve system performance. Specific improvements include:

[0322] Introduce an asynchronous loading mechanism: Split the content generation process into core content priority loading and secondary content background loading. For example, load the user-visible area content first, and delay loading the non-visible area content.

[0323] Dynamic resource priority adjustment: Dynamically adjust resource loading strategies based on network latency and bandwidth. For example:

[0324] When the network latency is low, load high-definition resources;

[0325] When network latency is high, low-resolution resources are loaded first.

[0326] An example of dynamically adjusting the policy update is as follows:

[0327] {

[0328] "async loading":true,

[0329] "Priority resources": "Core content",

[0330] "Network Adjustment":{

[0331] "Low latency":"High-definition resources",

[0332] "High latency": "Low resolution resources"

[0333] }

[0334] }

[0335] 5. Test cycle after template refresh

[0336] The updated template specification structure is reintroduced into the test process to form an optimization feedback loop. The specific steps include:

[0337] New test data generation: Generate new content items and validation data based on the updated template.

[0338] Effect verification: Compare the evaluation results before and after the template is updated. For example, verify whether the relevance score is improved from 3.5 to 4.2, and whether the generation time is shortened from 1200 milliseconds to 800 milliseconds.

[0339] Loop optimization: Continuously collect new evaluation results, iteratively optimize the template, and gradually improve system performance and user experience.

[0340] 6. Example refresh scenario

[0341] Assume that the evaluation result of a content item is as follows:

[0342] Reasons for failure: The recommendation content relevance score is insufficient (3.5<4.0); the generation time exceeds the standard (1200ms>1000ms).

[0343] Update measures:

[0344] Adjust the weight of the recommendation algorithm, reduce the priority of content popularity, and increase the weight of user interest;

[0345] Optimize page layout rules to reduce the amount of content loaded per screen;

[0346] Introduce an asynchronous loading mechanism to prioritize loading core content.

[0347] The refreshed template specification structure is as follows:

[0348] {

[0349] "Recommendation algorithm":{"Heat weight":0.4,"Interest weight":0.6},

[0350] "Page layout":{"Number of content per screen":5,"Module order":["Recommended videos","News content"]},

[0351] "Dynamic strategy":{"asynchronous loading":true,"priority resources":"core content"}

[0352] }

[0353] Through the above refresh measures, the template refresh module can effectively improve the system performance and adaptability of the content generation logic, provide support for subsequent testing and optimization, and ensure the closed-loop and efficient operation of template updates.

[0354] The above description is only a preferred embodiment of the present invention, so all equivalent changes or modifications made according to the structure, characteristics and principles described in the scope of the patent application of the present invention are included in the scope of the patent application of the present invention.

Claims

1. A cross-platform compatibility automated testing system for mobile applications, characterized by: The automated testing system comprises: A cross-platform adaptation module, which is used to uniformly define a set of variables for multiple mobile application platforms, including compatibility features for different operating systems, device types, and network environments; A data acquisition module, used for obtaining initial ranges of multiple variables in the variable set, and generating multiple verification values ​​for each variable according to its initial range; An optimization module, used to optimize and generate multiple verification groups based on historical test data and machine learning algorithms, each verification group being adapted to a variable combination of different platforms, and provide the multiple optimized verification groups to a content generation module; A content generation module, for dynamically generating mobile terminal content items adapted to the target platform in a real-time content generation unit for each optimized verification group according to a combination of verification values ​​of the verification group as an input of a template specification structure; A testing module, for associating each mobile terminal content item output by the content generation module with a verification value of a verification group that generates the content item, generating and outputting verification result information; An evaluation module, used to evaluate the compatibility performance of content items on multiple mobile application platforms according to the verification result information output by the test module, mark content items that do not meet the compatibility specifications and record the reasons; The template refresh module is used to refresh the template specification structure according to the evaluation results, adjust the recommendation algorithm, page layout rules and dynamic adjustment strategy, and reintroduce the refreshed template specification structure into the test process to form an optimization feedback loop.

2. The cross-platform compatibility automated testing system for mobile applications according to claim 1, characterized in that: The cross-platform adaptation module includes: The operating system adaptation unit is used to extract the operating system type and version information of the target device, analyze the permission management method, resource allocation strategy and interface rendering mechanism, and generate operating system difference attribute variables; The device type adaptation unit is used to analyze the hardware characteristics of the target device, such as screen resolution, processor performance, and memory capacity, and to organize the device characteristic variables into a multi-dimensional matrix; The network environment adaptation unit is used to abstract the characteristic variables of various network environments, including network delay range, bandwidth upper limit and dynamic fluctuation frequency; The unified variable generation unit is used to integrate the variable sets of the operating system, device type and network environment to generate a unified variable model to support subsequent testing and optimization processes.

3. The cross-platform compatibility automated testing system for mobile applications according to claim 1, characterized in that: The data acquisition module comprises: The initial range definition unit is used to determine the initial range of each variable in the variable set, including network latency, screen resolution, CPU performance, and memory capacity; A verification value generation unit, used to generate a verification value according to an initial range, wherein the generation rules include a uniform distribution generation method, a random distribution generation method, an extreme value generation method, and a distribution function generation method; The data storage and output unit is used to store the generated verification values ​​in the form of structured data and provide them to subsequent modules for use.

4. The cross-platform compatibility automated testing system for mobile applications according to claim 1, characterized in that: The optimization module includes: A feature vector extraction unit is used to convert the variable combination of each verification group into a feature vector and encode the non-numeric variables; A classification model training unit, which is used to train a classification model based on historical test data, evaluate the effectiveness of the validation group, and calculate the importance of variables; A redundant data removal unit, used to remove redundant data according to the distance of feature vectors through a clustering algorithm and select the most representative verification group; An optimized verification group generation unit is used to generate an optimized verification group according to the output results of the classification model and the cluster analysis results, and covers the key scenarios in the initial verification value set; The verification group output unit is used to output the optimized verification group in the form of structured data for use by subsequent modules.

5. The cross-platform compatibility automated testing system for mobile applications according to claim 1, characterized in that: The content generation module comprises: A template specification structure loading unit, used to load a template specification structure, wherein the template specification structure includes a page layout module, a recommendation algorithm module and a dynamic rule adjustment module; A parameter parsing and mapping unit, for receiving verification group parameters and mapping the verification group parameters to corresponding parameters in the template specification structure; A dynamic content generation unit, which is used to call the recommendation algorithm according to the template specification structure and the validation group parameters, dynamically adjust the resource loading strategy, and arrange the content items according to the page layout rules; A logging unit is used to associate and log the generated content items with the verification group parameters and the performance indicators of the generated results.

6. The cross-platform compatibility automated testing system for mobile applications according to claim 1, characterized in that: The test module includes: A content item and verification group associating unit, used to receive a content item and its corresponding verification group parameter, extract a content item feature and establish an associative record with the verification group parameter; A verification result generating unit, configured to generate verification result information based on the performance data of the content item, wherein the verification result information includes: Display quality indicators, including resolution and loading completeness; User interaction data, including click-through rate and dwell time; performance data, including build time and resource consumption; The data output unit is used to output the verification result information to the evaluation module in a structured format.

7. The cross-platform compatibility automated testing system for mobile applications according to claim 1, characterized in that: The evaluation module includes: An evaluation specification storage unit, used to store evaluation specifications related to display quality, performance, and user interaction; The verification result item-by-item evaluation unit is used to compare the verification result information with the evaluation specifications item by item, determine whether each indicator is qualified, and obtain a comprehensive evaluation judgment of the content item based on the evaluation results of multiple indicators; The non-conforming content marking unit is used to mark content items that do not meet the assessment specifications and record the content item identification, non-conforming reasons and recommended adjustment measures; The data output unit is used to output the evaluation results and marking information to the template refresh module.

8. The cross-platform compatibility automated testing system for mobile applications according to claim 1, characterized in that: The template refreshing module comprises: A data receiving unit, used to receive the reasons for non-conformity and recommended adjustment measures fed back by the evaluation module; A recommendation algorithm adjustment unit, used to adjust the recommendation algorithm weight based on the evaluation results, and improve the recommendation matching degree by optimizing the heat weight of the recommended content and the user interest weight; Page layout optimization unit, used to adjust page layout rules, including content quantity and module priority, according to content loading completeness or user interaction rate; Dynamic adjustment strategy improvement unit, used to optimize dynamic loading strategy, including introducing asynchronous loading mechanism and adjusting resource loading priority according to network conditions; The template test feedback unit is used to introduce the refreshed template specification structure into the test process and generate new test data to form an optimization feedback loop.

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