Game performance analysis method and device, equipment and storage medium

Abstract

The embodiment of the invention discloses a game performance analysis method and device, equipment and a storage medium, and the method comprises the steps: loading game scene data, collecting the game performance data in real time, carrying out the comparison processing of the data size of each element data in the game scene data, and determining the scene type based on the comparison result, the element data comprises competitive data, special effect data and scene map data in the game running process; determining a target test type corresponding to the scene type, updating a test priority in a preset test case according to the target test type to generate a target test case, executing the target test case, and generating a test result; and performing data alignment processing on the test result and the game performance data to generate performance mapping data, and performing difference analysis processing on the performance mapping data to generate a performance optimization strategy. The efficiency and accuracy of game performance testing and performance analysis can be improved.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of computer technology, and in particular to a game performance analysis method, apparatus, device, and storage medium. Background Art

[0002] In today's digital entertainment era, the gaming industry is booming, and the types and number of games are growing explosively. From traditional desktop games to mobile games, from stand-alone games to massively multiplayer online games, games have become one of the important forms of entertainment in people's daily lives. In order to ensure that the game has good performance, game performance testing has become an indispensable part of the game development process.

[0003] With the frequent iteration of game versions and the expansion of functions, different test cases need to be continuously written. In related technologies, in order to improve the efficiency of test case writing, test cases are usually written according to fixed test case templates, or new test cases are generated by simply replacing or adding relevant information in the original test cases based on updated functional data. This test case generation method is difficult to adapt to complex and changeable game scenarios, affecting the efficiency and accuracy of game performance testing and game performance analysis. Summary of the Invention

[0004] The embodiments of the present application provide a game performance analysis method, apparatus, device, and storage medium, which address the problem that the test case generation methods in related technologies are difficult to adapt to complex and changing game scenarios, resulting in low efficiency and accuracy in game performance testing. This solution determines the target test type and non-target test type based on the scenario type, and updates the test cases based on the target test type and non-target test type, thereby improving the efficiency and accuracy of game performance testing. By analyzing game performance data and test results, it is possible to accurately locate performance bottlenecks, thereby significantly improving the accuracy of performance optimization strategies.

[0005] In a first aspect, an embodiment of the present application provides a game performance analysis method, comprising: Loading game scene data and collecting game performance data in real time, comparing the data volume of each element data in the game scene data, and determining the scene type based on the comparison results, wherein the element data includes competitive data, special effects data, and scene map data during the game operation; Determine a target test type corresponding to the scenario type, update the test priority in the preset test case according to the target test type to generate a target test case, execute the target test case, and generate a test result; The test results and the game performance data are aligned to generate performance mapping data, and a difference analysis is performed on the performance mapping data to generate a performance optimization strategy.

[0006] Optionally, determining the scene type based on the comparison result includes: Determine the first target data corresponding to the largest data volume and the second target data corresponding to the smallest data volume in the comparison result; When the difference between the maximum data amount and the minimum data amount is within a preset difference range, determining the scene type as the first type; When the difference is not within the preset difference range, it is determined that the scene type corresponding to the first target data is the second type, and the scene complexity of the first type is greater than the scene complexity of the second type.

[0007] Optionally, the scene types include comprehensive scene types, special effect density types, scene map types and competitive types. The target test type corresponding to the comprehensive scene type is the hardware resource occupancy test type, the target test type corresponding to the competitive type is the network test type, the target test type corresponding to the special effect density type is the frame rate test type, and the target test type corresponding to the scene map type is the loading time test type.

[0008] Optionally, performing data alignment processing on the test result and the game performance data to generate performance mapping data includes: Performing time alignment processing on the test result and the game performance data according to a preset time window to generate first mapping data; Performing scene alignment processing according to the scene tags in the test results and the scene tags in the game performance data to generate second mapping data; Indicator alignment processing is performed according to the indicator identifier in the test result and the indicator identifier in the game performance test result to generate third mapping data.

[0009] Optionally, performing time alignment processing on the test result and the game performance data according to a preset time window to generate first mapping data includes: Determine a preset time window according to a first test timestamp of the test data and a second timestamp of the game performance data in the test result, wherein the preset time window is multiple; The game performance data and the test data in the same time window are aligned to generate first mapping data.

[0010] Optionally, updating the test priority in the preset test case according to the target test type to generate the target test case includes: Filtering multiple non-target test types in the preset test cases based on the target test type, and determining the test priority of the target test type as the first priority; Determine a second priority corresponding to each of the non-target test types according to a preset association matrix, wherein the first priority is greater than the second priority; The test priority in the preset test case is updated based on the first priority and each of the second priorities to generate a target test case.

[0011] Optionally, determining the second priority corresponding to each of the non-target test types according to a preset association matrix includes: Determine the correlation value between the target test type and each non-target test type according to a preset correlation matrix, sort the correlation values in descending order, and determine the second priority according to the sorting result.

[0012] In a second aspect, an embodiment of the present application provides a game performance analysis device, comprising: Data acquisition module, used to load game scene data and collect game performance data in real time; A scene type determination module is used to compare the competitive data, special effect data and scene map data in the game scene data, and determine the scene type based on the comparison result; a target test case generation module, configured to compare the data volume of each element data in the game scene data, and determine the scene type based on the comparison result, wherein the element data includes competitive data, special effect data, and scene map data during the game operation; A test module, configured to execute the target test case and generate test results; A performance mapping data generating module, configured to perform data alignment processing on the test results and the game performance data to generate performance mapping data; The optimization strategy generation module is used to perform difference analysis on the performance mapping data to generate a performance optimization strategy.

[0013] In a third aspect, an embodiment of the present application provides an electronic device comprising: one or more processors; a storage device configured to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the game performance analysis method described in the first aspect.

[0014] In a fourth aspect, an embodiment of the present application provides a storage medium comprising computer-executable instructions, which, when executed by a computer processor, are used to perform the game performance analysis method as described in the first aspect.

[0015] The embodiment of the present application loads game scene data and collects game performance data in real time, compares and processes the data volume of each element data in the game scene data, and determines the scene type based on the comparison result, wherein the element data includes competitive data, special effect data and scene map data during the game operation; determines the target test type corresponding to the scene type, updates the test priority in the preset test case according to the target test type to generate a target test case, executes the target test case, and generates a test result; performs data alignment processing on the test result and the game performance data to generate performance mapping data, performs difference analysis processing on the performance mapping data to generate a performance optimization strategy. It is able to determine the target test type and non-target test type based on the scene type, and update the test case based on the target test type and non-target test type, thereby improving the efficiency and accuracy of the game performance test. By analyzing the game performance data and test results, it is able to accurately locate the performance bottleneck, thereby significantly improving the accuracy of the performance optimization strategy. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is a flowchart of a game performance analysis method provided by an embodiment of the present application; Figure 2 This is a data interaction diagram provided by an embodiment of the present application; Figure 3 This is a flowchart of a method for generating performance mapping data provided by an embodiment of the present application; Figure 4 This is a flow chart of a method for generating a target test case provided by an embodiment of the present application; Figure 5 This is a schematic diagram of the structure of a game performance analysis device provided in an embodiment of the present application; Figure 6 This is a structural diagram of a game performance analysis device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0017] To further clarify the objectives, technical solutions, and advantages of this application, specific embodiments of the present application are described in further detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are intended only to illustrate this application and are not intended to limit it. It should also be noted that, for ease of description, the drawings only illustrate portions relevant to this application, not all of them. Before discussing the exemplary embodiments in more detail, it should be noted that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although the flowcharts depict the various operations (or steps) as sequential processes, many of the operations can be performed in parallel, concurrently, or simultaneously. Furthermore, the order of the operations can be rearranged. The process may terminate upon completion of its operations, but may also include additional steps not shown in the accompanying drawings. The process may correspond to a method, function, procedure, subroutine, subprogram, or the like.

[0018] The following will be combined with the accompanying drawings in the embodiments of the present application to clearly describe the technical solutions in the embodiments of the present application. Obviously, the embodiments described are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field are within the scope of protection of this application.

[0019] The terms "first," "second," and the like in the specification and claims of this application are used to distinguish similar objects, and are not used to describe a specific order or precedence. It should be understood that the terms used in this manner are interchangeable where appropriate, so that the embodiments of this application can be implemented in an order other than that illustrated or described herein, and that the objects distinguished by "first," "second," and the like are generally of the same type, and do not limit the number of objects; for example, the first object can be one or more. In addition, the term "and / or" in the specification and claims refers to at least one of the connected objects, and the character " / " generally indicates that the objects connected are in an "or" relationship.

[0020] The following, in conjunction with the accompanying drawings, describes in detail the game performance analysis and determination method, device, equipment and medium provided by the embodiments of the present application through specific embodiments and their application scenarios.

[0021] The game performance analysis method provided in the embodiments of this application is used for testing during the game development process, such as performance testing after game iterations and updates. Based on the above application scenarios, it can be understood that the execution subject of this solution can be a smart terminal integrated with the game performance testing system, such as a laptop computer.

[0022] Figure 1 This is a flowchart of a game performance analysis method provided by an embodiment of the present application. Figure 1 Shown, including: Step S101: Load game scene data and collect game performance data in real time, compare and process the data volume of each element data in the game scene data, and determine the scene type based on the comparison results, wherein the element data includes competitive data, special effect data and scene map data during the game operation.

[0023] Game scene data refers to the combination of various information sets used to construct, define, and describe the game world environment, objects, rules, and interaction logic in electronic games. Game performance data is a collection of key indicators used to measure, evaluate, and optimize the operation of electronic games. It covers multi-dimensional information from hardware resource utilization to player experience perception, and can include frame rate, hardware resource utilization data, input and latency data, etc. Element data is the fundamental information unit that constitutes the game system, covering various structured data from underlying rules to surface experience. This data, such as character data, item data, dynamic environmental parameters, and combat base value data, is linked through programming logic and algorithms to support game operation, interaction, and content presentation. Element data can optionally include competitive data, special effects data, and scene map data generated during game play. Competitive data refers to various information generated during the game that reflects the game status, player performance, team tactics, and other aspects. Special effects data refers to the underlying parameters and logical instructions that constitute the game's visual effects. It is the core data unit that realizes skill lighting, environmental dynamics, and character action effects. Game scene map data refers to the various information sets that describe and define the scene map in the game.

[0024] Figure 2 This is a data interaction diagram provided by an embodiment of the present application. Figure 2 As shown, in one embodiment, after the automated testing system responds to the user's start-up test instruction, it controls the game application to load the corresponding game scene data according to the start-up test instruction, and collects the game performance data of the user in the process of playing the game in real time, and compares the data volume of each basic information in the game scene. It can be understood that since the basic information with the largest data volume is the core element that affects the game performance in the current game scene, the scene type of the current game scene can be determined by the basic information with the largest data volume. For example, the element data in the game scene is classified and processed to obtain the competitive data, special effect data and scene map data during the game operation, and the data volume of the competitive data, the data volume of the special effect data and the data volume of the scene map data are counted respectively. The data volume of the competitive data, the data volume of the special effect data and the data volume of the scene map data are compared to determine the element data with the largest data volume, such as the special effect data. The test can consider the special effect data to be the core element that affects the game performance, and can determine the scene type to be the scene type corresponding to the special effect data, that is, the special effect type.

[0025] In one embodiment, determining the scene type based on the comparison results includes: determining the first target data corresponding to the maximum data volume and the second target data corresponding to the minimum data volume in the comparison results; when the difference between the maximum data volume and the minimum data volume is within a preset difference range, determining the scene type to be the first type; when the difference is not within the preset difference range, determining the scene type corresponding to the first target data to be the second type, and the scene complexity of the first type is greater than the scene complexity of the second type. The first target data refers to one type of data among competitive data, special effects data and scene map data. The second target data can also be one type of data among competitive data, special effects data and scene map data, and the data volume of the first target data is greater than the data volume of the second target data. The first type and the second type are both one of the pre-set game scene types, and the scene complexity of the first type is greater than the scene complexity of the second type.

[0026] In one embodiment, the first target data corresponding to the largest data volume and the second target data corresponding to the smallest data volume in the comparison result are determined. For example, if the data volume of the competitive data in the comparison result is greater than the data volume of the special effect data, and the data volume of the special effect data is greater than the data volume of the scene map data, then the competitive data can be considered as the first target data and the scene map data as the second target data. Since the scene type is determined based on the core elements in the current game scene, the core elements in the current game scene can be determined based on the comparison results. It can be understood that the data type corresponding to the maximum data volume and the data type corresponding to the minimum data volume are determined based on the comparison results, and the difference between the maximum data volume and the minimum data volume is calculated, and the calculated large difference is compared with the preset difference range. If the difference is not within the preset difference range, it can be considered that the difference between the maximum data volume and the minimum data volume is very different. At this time, the core elements that affect the game performance in the current scene can be represented by the data type corresponding to the maximum data volume, and the scene type corresponding to the competitive data corresponding to the maximum data volume can be the second type, that is, the competitive type. If the difference is not within the preset difference range, it can be considered that the data volume difference of each type of data is small, and each type of data can greatly affect the game performance in the current scene. At this time, it can be determined that the scene type is the first type, and the first type is a comprehensive scene type. The scene complexity corresponding to the comprehensive scene type is greater than the scene complexity corresponding to any type of scene type, special effect density type, scene map type and competitive type.

[0027] The embodiment of the present application determines the first target data corresponding to the maximum data volume and the second target data corresponding to the minimum data volume in the comparison result; when the difference between the maximum data volume and the minimum data volume is within the preset difference range, the scenario type is determined to be the first type; when the difference is not within the preset difference range, the scenario type corresponding to the first target data is determined to be the second type, and the scenario complexity of the first type is greater than the scenario complexity of the second type. In the above scheme, the scenario type is determined by judging whether the difference between the maximum data volume and the minimum data volume is within the preset difference range, which can accurately determine the core elements that affect the game performance of the current game scene, improve the accuracy of subsequent adjustments to the priority of preset test cases, and improve the efficiency and accuracy of performance testing.

[0028] Step S102: Determine a target test type corresponding to the scenario type, update the test priority in the preset test case according to the target test type to generate a target test case, execute the target test case, and generate a test result.

[0029] Target test types refer to test categories designed based on game performance optimization goals. Different types correspond to different performance dimension verifications, including loading speed tests, hardware resource consumption tests, and frame rate stability tests. Test cases refer to the core execution plans in the performance testing process, which can include test objectives, test scenarios, test environments, test objects, test steps, and expected results. Test priority is used to indicate the order in which the various test steps in a test case should be executed, such as prioritizing frame rate stability testing over loading speed testing.

[0030] In one embodiment, the target test type corresponding to the scenario type is determined according to the mapping relationship between the preset target test type and the scenario type, and the test step corresponding to the target test type in the preset test case is adjusted to the first test step, that is, the test priority of the test step corresponding to the target test type is adjusted to the first priority. After the priorities of other test steps in the preset test case are adjusted to the first priority, the adjusted preset test case is determined as the target test case, such as Figure 2 As shown, executing the target test case enables the target test case to preferentially execute the test steps corresponding to the target test type during the performance test process, and obtain the game performance test results under the game scenario.

[0031] In one embodiment, scene types include comprehensive scene type, special effects density type, scene map type, and competitive type. The target test type corresponding to the comprehensive scene type is the hardware resource usage test type, the target test type corresponding to the competitive type is the network test type, the target test type corresponding to the special effects density type is the frame rate test type, and the target test type corresponding to the scene map type is the loading time test type. The comprehensive scene type refers to scenes with a large number of high-precision models, rich texture details, complex lighting models, and numerous dynamic elements and special effects. For example, scenes such as cities and forests in large open-world games have numerous buildings, vegetation, NPCs, and may also have real-time lighting and shadow changes and complex weather systems. The special effects density type refers to the richness and complexity of special effects in the game scene, and can include low special effects density, medium special effects density, and high special effects density. The scene map type refers to the classification of different map forms, structures, and functions in the game, and can include plain maps, mountain maps, desert maps, and forest maps. Competitive game types are those centered around competition and confrontation between players, emphasizing strategy, skill, reaction speed, and other factors as the key to victory. These include multiplayer online tactical competitive scenarios, first-person shooter competitive scenarios, and real-time strategy competitive scenarios. Hardware resource utilization testing refers to a test category that quantitatively evaluates the hardware resource usage of electronic devices when running software, games, or performing specific tasks. Network testing refers to a test category that evaluates network systems, devices, links, and application scenarios based on performance, stability, and security. Frame rate testing refers to a test category that quantitatively evaluates the screen refresh rate of electronic devices, software applications, or games while they are running. Loading time testing refers to a test category that quantitatively evaluates the time required for software, games, web pages, or hardware systems to start up, switch, or load resources. By measuring the time interval from triggering the loading operation to the full availability of the content, it can measure system responsiveness, user experience, and potential for performance optimization. Since the scene complexity corresponding to the comprehensive scene type is relatively high, the hardware resource usage will directly affect the smoothness of the player's experience. Therefore, when the scene type is determined to be a comprehensive scene type, the corresponding target test type is determined to be a hardware resource usage test type. If the current game scene type is a competitive type, that is, the core element of the current scene is the competition between characters, the network speed will directly affect the competitive effect between characters and the game player's experience. Therefore, when the scene type is determined to be a competitive type, the corresponding target test type is determined to be a network test type. Similarly, when the scene type is determined to be a special effect density type, the corresponding target test type is determined to be a frame rate test type. When the scene type is determined to be a scene map type, the corresponding target test type is determined to be a loading time test type.

[0032] Step S103: align the test results and the game performance data to generate performance mapping data, and perform difference analysis on the performance mapping data to generate a performance optimization strategy.

[0033] Alignment processing refers to the standardization and integration of data from different sources, formats, or structures to maintain consistency in key attributes such as dimensions, units, and timestamps to facilitate subsequent analysis, modeling, or integrated applications. Performance mapping data refers to a comprehensive data system that accurately reflects game performance, constructed through systematic integration and correlation analysis. Variance analysis processing refers to the process of systematically comparing performance mapping data from different dimensions to identify deviations, anomalies, or changing trends between data, thereby locating performance issues, evaluating optimization effects, or discovering potential risks. Performance optimization strategies refer to a series of methodologies and practical measures that improve system efficiency, stability, and user experience by systematically analyzing performance bottlenecks and combining technical means, architectural adjustments, and resource management.

[0034] In one embodiment, Figure 2As shown, the real-time collected game performance data can be aligned with the test data in the test results according to the timestamp to obtain the mapping relationship between the game performance data and the test data corresponding to each timestamp, that is, the performance mapping data corresponding to each timestamp, and the performance mapping data corresponding to the timestamp is analyzed for differences to determine the performance bottleneck, and a performance optimization strategy is generated based on the performance bottleneck and displayed on the screen of the smart terminal, so that users can optimize the game performance based on the performance optimization strategy. For example, if a freeze occurs at T=15s during user operation and the frame rate drops sharply, but this problem does not occur during the test case test, the CPU (Central Processing Unit) occupancy rate in the game performance data increases from 30% to 85%, and the GPU (Graphics Processing Unit) rendering time increases from 16ms to 45ms. The CPU / GPU indicators corresponding to the same timestamp in the test results are stable. After determining the difference in the performance mapping data, the user operation sequence before and after the abnormal data is analyzed. It is found that before the freeze, the user performed a "role change", which triggered the creation of a large number of temporary objects, and the test case did not cover this scenario. At this time, it can be determined that the performance bottleneck is "the unreasonable allocation of content triggered by the user's specific operation, which leads to GC (Garbage Collection) The performance bottleneck can be solved by "converting dynamic memory allocation to static pool management, reducing GC trigger frequency by 90%, and reducing memory pressure at the source" or "asynchronizing and delaying loading to decompose the dressing operation into multi-stage coroutine execution, first displaying the low-poly outline and then asynchronously loading high-precision resources, reducing the total time from 45ms to 30ms, and avoiding the GC triggering of one-time resource loading".

[0035] The embodiment of the present application loads game scene data and collects game performance data in real time, compares and processes the data volume of each element data in the game scene data, and determines the scene type based on the comparison result, wherein the element data includes competitive data, special effect data and scene map data during the game operation; determines the target test type corresponding to the scene type, updates the test priority in the preset test case according to the target test type to generate a target test case, executes the target test case, and generates a test result; performs data alignment processing on the test result and the game performance data to generate performance mapping data, performs difference analysis processing on the performance mapping data to generate a performance optimization strategy. It is able to determine the target test type and non-target test type based on the scene type, and update the test case based on the target test type and non-target test type, thereby improving the efficiency and accuracy of the game performance test. By analyzing the game performance data and test results, it is able to accurately locate the performance bottleneck, thereby significantly improving the accuracy of the performance optimization strategy.

[0036] Figure 3 This is a flow chart of a method for generating performance mapping data provided by an embodiment of the present application. Figure 3 Shown, including: Step S1031: Time-align the test results and the game performance data according to a preset time window to generate first mapping data.

[0037] A time window refers to a specific time period with a specific attribute or suitable for performing a specific task. Time alignment refers to the synchronization of data or events with time attributes, making them consistent and comparable across the time dimension for easier analysis, integration, or collaborative processing. Mapping data refers to establishing certain rules or relationships, such as the correspondence between test results and game performance data. First mapping data refers to mapping data generated using time as the data mapping condition.

[0038] In one embodiment, a timestamp in a unified format is added to the test results and the game performance data to ensure the consistency of the time unit. The test result data and the game performance data are associated according to the timestamp, and the test result data and the game performance data belonging to the same time window are aligned to generate the first mapping data. For example, the time window is 100ms, such as 1685270400100~1685270400200 Test result data: {"action":"Skill Release", "timestamp":1685270400123}, game performance data: {"fps":60, "memory":2.5GB, "timestamp":1685270400150}. Both the test result data and the game performance data belong to the time window 1685270400100~1685270400200. The test result data and the game performance data are aligned to generate the first mapping data.

[0039] Step S1032: Perform scene alignment processing according to the scene tags in the test results and the scene tags in the game performance data to generate second mapping data.

[0040] Among them, scene tags are classification identifiers used to identify specific environments, gameplay or states within the game. They associate data with specific game scenes through structured fields to facilitate subsequent analysis and screening. They can include tags of types such as geographic scene tags, gameplay scene tags, system status tags, and environmental parameter tags. The scene tags can be map names, gameplay modes, etc. Scene alignment processing refers to the process of establishing associations between data from different sources and of different types through scene tags or scene features to ensure consistency and comparability of data in the scene dimension. Secondary mapping data refers to mapping data generated with the game scene as the mapping condition.

[0041] In one embodiment, if there are tags in both the test result data and the game performance data that can directly represent the corresponding scenes, the second mapping data is generated directly based on the test result data and the game performance data corresponding to the same scene tags. If there are no tags that directly represent the scenes in the test result data and the game performance data, the scenes can be inferred through data features such as spatiotemporal trajectories, operation modes, and environmental parameters, and association relationships can be suggested, and the second mapping data is generated based on the data with association relationships.

[0042] Step S1033: Perform indicator alignment processing according to the indicator identifier in the test result and the indicator identifier in the game performance test result to generate third mapping data.

[0043] Among them, the indicator identifier is a key identifier used to uniquely distinguish and locate various types of performance and functional test indicators, such as CPU usage, GPU usage, network latency, memory performance, etc. In one embodiment, the test result data is associated and matched with indicators with the same meaning in the game performance data to generate third mapping data. Exemplary, test result data indicators: such as Test_Result_battle scene_frame rate, Test_Result_load time. Game performance test indicators: such as Performance_battle scene_FPS, Performance_loading scene_LoadTime, and establish a mapping relationship between Test_Result_battle scene_frame rate and Performance_battle scene_FPS, as well as a mapping relationship between Test_Result_load time and Performance_loading scene_LoadTime.

[0044] The embodiment of the present application generates first mapping data by performing time alignment processing on the test results and game performance data according to a preset time window; generates second mapping data by performing scene alignment processing based on the scene tags in the test results and the scene tags in the game performance data; and generates third mapping data by performing indicator alignment processing based on the indicator identifiers in the test results and the indicator identifiers in the game performance test results. In the above scheme, the alignment processing of the test result data and the game performance data is performed based on multiple dimensions, ensuring the uniformity of the data dimensions and improving the accuracy of the subsequent difference analysis of the mapping data.

[0045] In one embodiment, time alignment of test results and game performance data based on a preset time window to generate first mapping data includes: determining the preset time window to which the test data and the game performance data belong based on a first test timestamp in the test results and a second timestamp in the game performance data, where the number of preset time windows is multiple; and aligning the game performance data and the test data in the same time window to generate the first mapping data. The first timestamp refers to the timestamp corresponding to each data item in the test results, and the second timestamp refers to the timestamp corresponding to each data item in the game performance data.

[0046] In one embodiment, the test result data is assigned to the corresponding time window based on the first timestamp and the range of each time window, and the game performance data is assigned to the corresponding time window based on the second timestamp and the range of each time window. The test result data and game performance data within the same time window are aligned to generate first mapping data. In the above scheme, the allocation of test result data and game performance data through the first timestamp and the second timestamp and the preset time window can achieve a precise correlation between the time dimension of the data, thereby improving the accuracy of subsequent difference analysis based on the first mapping data.

[0047] Figure 4 This is a flow chart of a method for generating a target test case provided by an embodiment of the present application. Figure 4 Shown, including: Step S1021: Filter multiple non-target test types in the preset test cases based on the target test type, and determine the test priority of the target test type as the first priority.

[0048] Step S1022: Determine the second priority corresponding to each non-target test type according to a preset correlation matrix, wherein the first priority is greater than the second priority.

[0049] Step S1023: Update the test priorities in the preset test cases based on the first priority and each second priority to generate a target test case.

[0050] A preset association matrix refers to a mathematical matrix structure used to represent the association relationship between different elements, entities, or variables. The existence of an association relationship is quantified by the numerical value at the intersection of rows and columns. For example, an element with an association is assigned a value of 1, and an element without an association is assigned a value of 0. In one embodiment, after determining the target test type, test types in the preset test cases that are different from the target test type are determined as non-target test types, and the priority of the target test type in the preset test cases is determined as a first priority, and the priority of each non-target test type in the preset test cases is determined as a second priority. Test data corresponding to non-target test types that have an association relationship with test data corresponding to the target test type are determined based on the preset association matrix. The data with an association relationship is determined as target data, and the data without an association relationship is determined as non-target data. The test priority of the non-target test type corresponding to the target data is determined to be greater than the test priority of the non-target test type corresponding to the non-target data. Finally, it is determined that the first priority corresponding to the target test type is greater than the test priority of the non-target test type corresponding to the target data, and the test priority of the non-target test type corresponding to the target data is greater than the test priority of the non-target test type corresponding to the non-target data. The test order in the preset test cases is adjusted based on the final priority to generate the target test case.

[0051] The embodiment of the present application determines the test priority of the target test type as the first priority by screening multiple non-target test types in the preset test case based on the target test type; determines the second priority corresponding to each non-target test type according to the preset association matrix, where the first priority is greater than the second priority; and updates the test priority in the preset test case based on the first priority and each second priority to generate a target test case. In the above scheme, by adjusting the test priority of the target test type to the first priority and adjusting the test priority of the non-target test type to the second priority, the core elements that affect the user experience are tested first, thereby improving the test efficiency and test quality.

[0052] In one embodiment, the second priority corresponding to each non-target test type is determined according to a preset association matrix, including: determining the correlation value between the target test type and each non-target test type according to the preset association matrix, sorting each correlation value in descending order, and determining the second priority according to the sorting result. The preset association matrix is a mathematical matrix structure used to represent the correlation relationship between different elements, entities or variables, and the degree of correlation is quantified by the numerical value at the intersection of rows and columns. According to the correlation value between the target test type and each non-target test type in the preset association matrix, the larger the correlation value, the stronger the correlation between the target test type and the corresponding non-target test type, and conversely, the smaller the correlation value, the weaker the correlation between the target test type and the corresponding non-target test type. The correlation value between the target test type and each non-target test type is sorted in descending order, and the second priority corresponding to each non-target test type is determined according to the sorting result.

[0053] The embodiment of the present application determines the correlation value between the target test type and each non-target test type based on a preset correlation matrix, sorts each correlation value in descending order, and determines the second priority based on the sorting result. It can determine the second priority of each non-target test type according to the degree of influence of the test type on the user experience of the game, thereby improving the test efficiency and test quality.

[0054] Figure 5 This is a schematic diagram of the structure of a game performance analysis device provided by an embodiment of the present application. Figure 5 Shown, including: Data acquisition module 21, used to load game scene data and collect game performance data in real time; A scene type determination module 22 is configured to compare the competitive data, special effect data, and scene map data in the game scene data, and determine the scene type based on the comparison result; a target test case generation module 23 for comparing the data volume of each element data in the game scene data and determining the scene type based on the comparison result, wherein the element data includes competitive data, special effect data, and scene map data during the game operation; The test module 24 is used to execute the target test case and generate test results; A performance mapping data generating module 25 is configured to perform data alignment processing on the test results and the game performance data to generate performance mapping data; The optimization strategy generating module 26 is configured to perform difference analysis on the performance mapping data to generate a performance optimization strategy.

[0055] The embodiment of the present application loads game scene data and collects game performance data in real time, compares and processes the data volume of each element data in the game scene data, and determines the scene type based on the comparison result, wherein the element data includes competitive data, special effect data and scene map data during the game operation; determines the target test type corresponding to the scene type, updates the test priority in the preset test case according to the target test type to generate a target test case, executes the target test case, and generates a test result; performs data alignment processing on the test result and the game performance data to generate performance mapping data, performs difference analysis processing on the performance mapping data to generate a performance optimization strategy. It is able to determine the target test type and non-target test type based on the scene type, and update the test case based on the target test type and non-target test type, thereby improving the efficiency and accuracy of the game performance test. By analyzing the game performance data and test results, it is able to accurately locate the performance bottleneck, thereby significantly improving the accuracy of the performance optimization strategy. In a possible embodiment, the scene type determination module 22 is specifically configured to: Determine the first target data corresponding to the largest data volume and the second target data corresponding to the smallest data volume in the comparison result; When the difference between the maximum data amount and the minimum data amount is within a preset difference range, determining the scene type as the first type; When the difference is not within the preset difference range, it is determined that the scene type corresponding to the first target data is the second type, and the scene complexity of the first type is greater than the scene complexity of the second type.

[0056] In a possible embodiment, the scene types include comprehensive scene types, special effects density types, scene map types and competitive types. The target test type corresponding to the comprehensive scene type is a hardware resource occupancy test type, the target test type corresponding to the competitive type is a network test type, the target test type corresponding to the special effects density type is a frame rate test type, and the target test type corresponding to the scene map type is a loading time test type.

[0057] In a possible embodiment, the performance mapping data generating module 25 is specifically configured to: Performing time alignment processing on the test result and the game performance data according to a preset time window to generate first mapping data; Performing scene alignment processing according to the scene tags in the test results and the scene tags in the game performance data to generate second mapping data; Indicator alignment processing is performed according to the indicator identifier in the test result and the indicator identifier in the game performance test result to generate third mapping data.

[0058] In a possible embodiment, the performance mapping data generating module 25 is specifically configured to: Determine a preset time window according to a first test timestamp of the test data and a second timestamp of the game performance data in the test result, wherein the preset time window is multiple; The game performance data and the test data in the same time window are aligned to generate first mapping data.

[0059] In a possible embodiment, the target test case generation module 23 is specifically configured to: Filtering multiple non-target test types in the preset test cases based on the target test type, and determining the test priority of the target test type as the first priority; Determine a second priority corresponding to each of the non-target test types according to a preset association matrix, wherein the first priority is greater than the second priority; The test priority in the preset test case is updated based on the first priority and each of the second priorities to generate a target test case.

[0060] In a possible embodiment, the target test case generation module 23 is specifically configured to: Determine the correlation value between the target test type and each non-target test type according to a preset correlation matrix, sort the correlation values in descending order, and determine the second priority according to the sorting result.

[0061] An embodiment of the present application also provides an electronic device, which can integrate a game performance analysis device provided in an embodiment of the present application. Figure 6 This is a schematic diagram of the structure of a game performance analysis device provided in an embodiment of the present application, with reference to Figure 6 The game performance analysis device includes: an input device 33, an output device 34, a memory 32, and one or more processors 31; the memory 32 is used to store one or more programs; when the one or more programs are executed by the one or more processors 31, the one or more processors 31 implement the game performance analysis method provided in the above embodiment. The input device 33, the output device 34, the memory 32, and the processor 31 can be connected by a bus or other means. Figure 6 The bus connection is taken as an example.

[0062] Memory 32, as a computing device-readable storage medium, can be used to store software programs, computer-executable programs, and modules, such as the program instructions / modules corresponding to the game performance analysis method provided in any embodiment of the present application. Memory 32 may primarily include a program storage area and a data storage area. The program storage area may store an operating system and at least one application required for a function; the data storage area may store data generated based on device usage, etc. Furthermore, memory 32 may include high-speed random access memory and non-volatile memory, such as at least one disk storage device, flash memory device, or other non-volatile solid-state memory device. In some instances, memory 32 may further include memory remotely located relative to processor 31, and such remote memory may be connected to the device via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0063] The input device 33 may be used to receive input digital or character information and generate key signal input related to user settings and function control of the device. The output device 34 may include a display device such as a display screen.

[0064] The processor 31 executes various functional applications and data processing of the device by running the software programs, instructions and modules stored in the memory 32, that is, implements the above-mentioned game performance analysis method.

[0065] The game performance analysis apparatus, device, and computer provided above can be used to execute the game performance analysis method provided in any of the above embodiments, and have corresponding functions and beneficial effects.

[0066] The present application also provides a storage medium storing computer-executable instructions. When executed by a computer processor, the computer-executable instructions are used to perform the game performance analysis method provided in the above embodiment. The game performance analysis method includes: Loading game scene data and collecting game performance data in real time, comparing the data volume of each element data in the game scene data, and determining the scene type based on the comparison results. The element data includes competitive data, special effects data, and scene map data during the game operation; Determine the target test type corresponding to the scenario type, update the test priority in the preset test case according to the target test type to generate the target test case, execute the target test case, and generate the test results; The test results and the game performance data are aligned to generate performance mapping data, and the performance mapping data is analyzed for differences to generate a performance optimization strategy.

[0067] Storage medium - any of various types of memory devices or storage devices. The term "storage medium" is intended to include: installation media, such as CD-ROMs, floppy disks, or tape drives; computer system memory or random access memory, such as DRAM, DDR RAM, SRAM, EDO RAM, Rambus RAM, etc.; non-volatile memory, such as flash memory, magnetic media (such as hard disks or optical storage); registers or other similar types of memory elements, etc. Storage media may also include other types of memory or combinations thereof. In addition, the storage medium may be located in the first computer system in which the program is executed, or it may be located in a different second computer system that is connected to the first computer system via a network (such as the Internet). The second computer system can provide program instructions to the first computer for execution. The term "storage medium" may include two or more storage media that can reside in different locations (e.g., in different computer systems connected via a network). The storage medium can store program instructions (e.g., embodied as a computer program) that can be executed by one or more processors.

[0068] Of course, the storage medium containing computer-executable instructions provided in an embodiment of the present application, whose computer-executable instructions are not limited to the game performance analysis method described above, can also execute related operations in the game performance analysis method provided in any embodiment of the present application.

[0069] The game performance analysis apparatus, device, and storage medium provided in the above embodiments can execute the game performance analysis method provided in any embodiment of the present application. For technical details not fully described in the above embodiments, please refer to the game performance analysis method provided in any embodiment of the present application.

[0070] The above are only preferred embodiments of the present application and the technical principles employed. The present application is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions that are possible for those skilled in the art will not depart from the scope of protection of the present application. Therefore, although the present application has been described in more detail through the above embodiments, the present application is not limited to the above embodiments and may include more other equivalent embodiments without departing from the concept of the present application. The scope of the present application is determined by the scope of the claims.

Claims

1. A game performance analysis method, characterized in that: include: Loading game scene data and collecting game performance data in real time, comparing the data volume of each element data in the game scene data, and determining the scene type based on the comparison results, wherein the element data includes competitive data, special effects data, and scene map data during the game operation; Determine a target test type corresponding to the scenario type, update the test priority in the preset test case according to the target test type to generate a target test case, execute the target test case, and generate a test result; The test results and the game performance data are aligned to generate performance mapping data, and a difference analysis is performed on the performance mapping data to generate a performance optimization strategy.

2. The game performance analysis method according to claim 1, characterized in that: Determining the scene type based on the comparison result includes: Determine the first target data corresponding to the largest data volume and the second target data corresponding to the smallest data volume in the comparison result; When the difference between the maximum data amount and the minimum data amount is within a preset difference range, determining the scene type as the first type; When the difference is not within the preset difference range, it is determined that the scene type corresponding to the first target data is the second type, and the scene complexity of the first type is greater than the scene complexity of the second type.

3. The game performance analysis method according to claim 1, characterized in that: The scene types include comprehensive scene type, special effect density type, scene map type and competitive type. The target test type corresponding to the comprehensive scene type is the hardware resource occupancy test type, the target test type corresponding to the competitive type is the network test type, the target test type corresponding to the special effect density type is the frame rate test type, and the target test type corresponding to the scene map type is the loading time test type.

4. The game performance analysis method according to claim 1, characterized in that: The step of performing data alignment processing on the test result and the game performance data to generate performance mapping data includes: Performing time alignment processing on the test result and the game performance data according to a preset time window to generate first mapping data; Performing scene alignment processing according to the scene tags in the test results and the scene tags in the game performance data to generate second mapping data; Indicator alignment processing is performed according to the indicator identifier in the test result and the indicator identifier in the game performance test result to generate third mapping data.

5. The game performance analysis method according to claim 4, characterized in that: The performing time alignment processing on the test result and the game performance data according to a preset time window to generate first mapping data includes: Determine a preset time window according to a first test timestamp of the test data and a second timestamp of the game performance data in the test result, wherein the preset time window is multiple; The game performance data and the test data in the same time window are aligned to generate first mapping data.

6. The game performance analysis method according to claim 1, characterized in that: The updating of the test priority in the preset test case according to the target test type to generate the target test case includes: Filtering multiple non-target test types in the preset test cases based on the target test type, and determining the test priority of the target test type as the first priority; Determine a second priority corresponding to each of the non-target test types according to a preset association matrix, wherein the first priority is greater than the second priority; The test priority in the preset test case is updated based on the first priority and each of the second priorities to generate a target test case.

7. The game performance analysis method according to claim 1, characterized in that: The determining the second priority corresponding to each of the non-target test types according to the preset association matrix includes: Determine the correlation value between the target test type and each non-target test type according to a preset correlation matrix, sort the correlation values in descending order, and determine the second priority according to the sorting result.

8. A game performance analysis device, characterized in that: include: Data acquisition module, used to load game scene data and collect game performance data in real time; A scene type determination module is used to compare the competitive data, special effect data and scene map data in the game scene data, and determine the scene type based on the comparison result; a target test case generation module, configured to compare the data volume of each element data in the game scene data, and determine the scene type based on the comparison result, wherein the element data includes competitive data, special effect data, and scene map data during the game operation; A test module, configured to execute the target test case and generate test results; A performance mapping data generating module, configured to perform data alignment processing on the test results and the game performance data to generate performance mapping data; The optimization strategy generation module is used to perform difference analysis on the performance mapping data to generate a performance optimization strategy.

9. An electronic device, comprising: one or more processors; A storage device for storing one or more programs, when the one or more programs are executed by the one or more processors, enables the one or more processors to implement the game performance analysis method according to any one of claims 1 to 7.

10. A storage medium storing computer-executable instructions, wherein the computer-executable instructions, when executed by a computer processor, are used to perform the game performance analysis method according to any one of claims 1 to 7.

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