A multi-mode game balance testing method and device

Abstract

The application relates to the technical field of game testing, and discloses a multi-mode game balance testing method and device, which is applied to a balance testing platform and comprises the following steps: determining testing requirements corresponding to a current game project; the testing requirements include testing game modes, testing mode setting parameters and testing objects; according to the testing requirements, a plurality of groups of testing data meeting the testing requirements are generated; when there are a plurality of testing game modes, the testing data include testing data of each testing game mode; according to the testing requirements, countermeasures corresponding to each group of testing data are generated based on a set data generation algorithm; balance testing is performed for each testing task based on a testing simulation algorithm, and testing results are obtained; each testing task includes one group of testing data and countermeasures corresponding to the testing data. It can be seen that the application can improve the efficiency of game balance testing and the accuracy of game balance testing results.

Description

Technical Field

[0001] The present invention relates to the field of game testing technology, and in particular to a multi-mode game balance testing method and device. Background Art

[0002] Game balance is a key factor influencing the player's gaming experience. Through game balance testing, developers can identify and address imbalances in gameplay, such as character, skill, or mechanics, ensuring a relatively fair competitive environment for all players. This helps improve game playability and ultimately increases player satisfaction.

[0003] In existing technologies, balance testing is typically performed manually by testers or through exhaustive testing of all test combinations. However, manual testing is prone to missing test combinations, and testers' testing skills vary widely, which can easily lead to inaccurate test results. Exhaustive testing, on the other hand, requires excessive computing resources and is costly. Both testing methods consume a long time, resulting in low game testing efficiency.

[0004] Therefore, it is particularly important to propose a technical solution that can improve the efficiency of game balance testing while improving the accuracy of game balance test results. Summary of the Invention

[0005] The present invention provides a multi-mode game balance testing method and device, which can be beneficial.

[0006] In order to solve the above technical problems, the first aspect of the present invention discloses a multi-mode game balance testing method, which is applied to a balance testing platform and includes:

[0007] Determine the test requirements corresponding to the current game project; the test requirements include test game mode, test mode setting parameters and test objects;

[0008] According to the test requirements, multiple groups of test data that meet the test requirements are generated; wherein, when the test requirements include at least two test game modes, all the test data include at least one group of test data for each test game mode;

[0009] Based on the data generation algorithm set by the balance test platform and according to the test requirements, generate adversarial data corresponding to each set of the test data;

[0010] The balance test is performed for each test task based on a test simulation algorithm set by the balance test platform, and a test result is obtained; each test task includes a group of test data and corresponding adversarial data of the test data.

[0011] As an optional implementation, in the first aspect of the application, the test game mode includes a PVP mode and / or a PVE mode; when the test game mode includes the PVP mode, the test mode setting parameter includes a test battle parameter, and the test battle parameter includes a test battle map and / or a test battle duration; when the test game mode includes the PVE mode, the test mode setting parameter includes a test level parameter, and the test level parameter includes a test level type and / or a test level difficulty; the test object includes a combination of one or more of a target role, a target lineup, a target value, a target level, and a target difficulty;

[0012] Each group of test data includes a test lineup, and the test lineup includes at least one test role; each group of test data further includes a test role parameter corresponding to each test role in the test lineup; the test role parameter includes a combination of one or more of a test role level, a test role equipment, and a test role skill;

[0013] For each group of test data, when the test game mode corresponding to the test data is the PVP mode, the adversarial data corresponding to the test data includes an adversarial lineup and an adversarial role parameter corresponding to each adversarial role in the adversarial lineup; when the test game mode corresponding to the test data is the PVE mode, the adversarial data corresponding to the test data includes a level enemy lineup and an enemy parameter corresponding to each level enemy in the level enemy lineup.

[0014] As an optional implementation, in the first aspect of the application, before the plurality of groups of test data meeting the test requirements are generated according to the test requirements, the method further includes:

[0015] receiving a custom account parameter input by a test personnel; or, generating a custom account parameter according to the test requirements; the custom account parameter includes a combination of one or more of an account basic parameter, an account role parameter, an account resource parameter, and an account mode parameter; the account basic parameter includes a combination of one or more of an account number, an account type, an account level, and an account resource; the account role parameter includes a combination of one or more of a role number, a role type, a role level, a role equipment, and a role skill; the account mode parameter includes a game mode type suitable for a test account to be generated;

[0016] Generate multiple test accounts according to the custom account parameters, and import all the test accounts into the balance testing platform;

[0017] Furthermore, generating multiple groups of test data that meet the test requirements according to the test requirements includes:

[0018] According to the test requirements, at least one set of test data is generated for each test account so that the test account meets the test requirements.

[0019] As an optional embodiment, in the first aspect of the present invention, the test simulation algorithm set based on the balance test platform performs a balance test for each test task to obtain a test result, including:

[0020] Based on the distributed computing technology corresponding to the balance test platform, each test task is assigned to a target server that meets the preset assignment conditions; the target server is one of the multiple test servers corresponding to the balance test platform;

[0021] Based on all the target servers, balance tests are performed in parallel for multiple test tasks until the balance tests are completed for all the test tasks, and test results are obtained; wherein the test results include test task results corresponding to each of the test tasks; the test task results include a combination of one or more of battle results, character performance results, and test resource consumption results; the battle results include win-loss results and / or battle duration;

[0022] In the process of performing the balance test on multiple test tasks in parallel based on all the target servers until the balance test is completed for all the test tasks and the test results are obtained, the method further includes:

[0023] Based on the caching technology corresponding to the balance test platform, the calculation results corresponding to each of the test tasks are cached to obtain cached calculation results; the calculation results include the test task results corresponding to the test task and / or the intermediate calculation results during the balance test process corresponding to the test task;

[0024] After detecting that the cache calculation result is not empty, before executing the balance test for each test task, detecting whether the balance test platform has cached the cache calculation result related to the test task;

[0025] For each of the test tasks, when it is detected that the balance test platform caches a cache calculation result related to the test task, the cache calculation result related to the test task is read, and based on the read cache calculation result, a test task result corresponding to the test task is determined;

[0026] For each of the test tasks, when it is detected that the balance test platform does not cache the cache calculation results related to the test task, a balance test is performed on the test task based on the target server corresponding to the test task to obtain the test task result corresponding to the test task.

[0027] As an optional embodiment, in the first aspect of the present invention, after performing a balance test for each test task based on the test simulation algorithm set by the balance test platform and obtaining the test results, the method further includes:

[0028] Analyze the test results to obtain a balance analysis result corresponding to the current game item;

[0029] Determining whether the current game item satisfies a preset balance condition based on the balance analysis result;

[0030] When it is determined that the current game item does not meet the balance condition, determining a key balance factor corresponding to the current game item according to the balance analysis result;

[0031] generating an optimization suggestion based on the key balance factor; the optimization suggestion includes a first optimization suggestion and / or a second optimization suggestion;

[0032] According to the optimization suggestion, the optimization object corresponding to the optimization suggestion is optimized; the optimization object includes the current game project and / or the balance testing platform.

[0033] As an optional implementation manner, in the first aspect of the present invention, generating optimization suggestions based on the key balance factors includes:

[0034] Determining a factor type of the key balance factor; the factor type includes a game design factor type and / or a test design factor type;

[0035] When the factor type of the key balance factor includes the game design factor type, generating a first optimization suggestion for the current game project based on the key balance factor; the first optimization suggestion is used to optimize the game design corresponding to the current game project;

[0036] When the factor type of the key balance factor includes the test design factor type, a second optimization suggestion for the balance test platform is generated based on the key balance factor; the second optimization suggestion is used to optimize the data generation algorithm corresponding to the balance test platform; the data generation algorithm includes a combination of one or more of a test data generation algorithm, an adversarial data generation algorithm and a battle matching algorithm.

[0037] As an optional implementation, in the first aspect of the present invention, generating a first optimization suggestion for the current game item based on the key balance factor includes:

[0038] Determining the combat power difference corresponding to each group of the test tasks based on the key balance factors;

[0039] Filtering the test tasks whose combat power differences are greater than or equal to a preset combat power difference from the combat power differences corresponding to all the test tasks as the first reference tasks;

[0040] Analyzing the test data included in all the first reference tasks to obtain design defect information corresponding to the current game project; the design defect information includes one or more combinations of character design defect information, team design defect information, equipment design defect information, enemy design defect information, and level design defect information;

[0041] A first optimization suggestion for the current game project is generated based on the design defect information.

[0042] A second aspect of the present invention discloses a multi-mode game balance testing device, which is applied to a balance testing platform and includes:

[0043] A determination module is used to determine the test requirements corresponding to the current game project; the test requirements include a test game mode, test mode setting parameters, and a test object;

[0044] a generating module, configured to generate, based on the test requirements, a plurality of sets of test data that meet the test requirements; wherein, when the test requirements include at least two test game modes, all the test data include at least one set of test data for each test game mode;

[0045] The generating module is further configured to generate adversarial data corresponding to each set of the test data based on the data generating algorithm set by the balance testing platform and in accordance with the test requirements;

[0046] A testing module is used to perform a balance test on each test task based on the test simulation algorithm set by the balance testing platform to obtain a test result; each of the test tasks includes a set of the test data and the adversarial data corresponding to the test data.

[0047] As an optional embodiment, in the second aspect of the present invention, the test game mode includes a PVP mode and / or a PVE mode; when the test game mode includes the PVP mode, the test mode setting parameters include test battle parameters, and the test battle parameters include a test battle map and / or a test battle duration; when the test game mode includes the PVE mode, the test mode setting parameters include test level parameters, and the test level parameters include a test level type and / or a test level difficulty; the test object includes a combination of one or more of a target character, a target lineup, a target value, a target level, and a target difficulty;

[0048] Each set of test data includes a test lineup; the test lineup includes at least one test character; each set of test data also includes test character parameters corresponding to each test character in the test lineup; the test character parameters include a combination of one or more of the test character level, test character equipment, and test character skills;

[0049] For each set of test data, when the test game mode corresponding to the test data is the PVP mode, the adversarial data corresponding to the test data includes the adversarial lineup and the adversarial character parameters corresponding to each adversarial character in the adversarial lineup; when the test game mode corresponding to the test data is the PVE mode, the adversarial data corresponding to the test data includes the level enemy lineup and the enemy parameters corresponding to each level enemy in the level enemy lineup.

[0050] As an optional embodiment, in the second aspect of the present invention, the device further includes:

[0051] A receiving module, configured to receive custom account parameters input by a tester before the generating module generates multiple sets of test data that meet the test requirements according to the test requirements; or, to generate custom account parameters according to the test requirements; the custom account parameters include a combination of one or more of account basic parameters, account role parameters, account resource parameters, and account mode parameters; the account basic parameters include a combination of one or more of the number of accounts, account type, account level, and account resources; the account role parameters include a combination of one or more of the number of characters, character type, character level, character equipment, and character skills; and the account mode parameters include the type of game mode applicable to the test account to be generated;

[0052] The generating module is further configured to generate a plurality of test accounts according to the self-defined account parameters, and import all the test accounts into the balance test platform.

[0053] The generating module generates a plurality of sets of test data satisfying the test requirements according to the test requirements in the following specific manner:

[0054] According to the test requirements, at least one set of test data satisfying the test requirements is generated for each test account.

[0055] As an optional implementation, in the second aspect of the present application, the test module performs balance test for each test task based on the test simulation algorithm set by the balance test platform to obtain a test result in the following specific manner:

[0056] Each test task is allocated to a target server satisfying a preset allocation condition based on the distributed computing technology corresponding to the balance test platform; the target server is one of a plurality of test servers corresponding to the balance test platform;

[0057] Balance test is performed in parallel for a plurality of test tasks based on all the target servers until the balance test is completed for all the test tasks to obtain a test result; the test result includes a test task result corresponding to each test task; the test task result includes one or more combinations of a battle result, a character performance result and a test resource consumption result; the battle result includes a win / loss result and / or a battle duration;

[0058] The apparatus further includes:

[0059] The cache module is configured to cache a calculation result corresponding to each test task based on the cache technology corresponding to the balance test platform to obtain a cached calculation result during the process that the test module performs balance test in parallel for a plurality of test tasks based on all the target servers until the balance test is completed for all the test tasks to obtain a test result; the calculation result includes a test task result corresponding to the test task and / or an intermediate calculation result in the process of performing balance test corresponding to the test task.

[0060] The detection module is configured to detect whether the balance test platform caches a cached calculation result related to each test task before balance test is performed for the test task after it is detected that the cached calculation result is not empty.

[0061] a reading module configured to, for each of the test tasks, read the cached calculation results related to the test task when the detection module detects that the balance test platform caches the cached calculation results related to the test task, and determine the test task result corresponding to the test task based on the read cached calculation results;

[0062] The test module is also used to, for each of the test tasks, when the detection module detects that the balance test platform does not cache the cache calculation results related to the test task, perform a balance test on the test task based on the target server corresponding to the test task to obtain the test task result corresponding to the test task.

[0063] As an optional embodiment, in the second aspect of the present invention, the device further includes:

[0064] an analysis module configured to execute a balance test for each test task in the test module based on a test simulation algorithm set by the balance test platform, and after obtaining a test result, analyze the test result to obtain a balance analysis result corresponding to the current game project;

[0065] A judgment module, configured to judge whether the current game item satisfies a preset balance condition based on the balance analysis result;

[0066] The determination module is further configured to determine a key balance factor corresponding to the current game item based on the balance analysis result when the judgment module determines that the current game item does not meet the balance condition;

[0067] The generating module is further configured to generate an optimization suggestion based on the key balance factor; the optimization suggestion includes a first optimization suggestion and / or a second optimization suggestion;

[0068] An optimization module is used to optimize the optimization object corresponding to the optimization suggestion according to the optimization suggestion; the optimization object includes the current game project and / or the balance testing platform.

[0069] As an optional implementation, in the second aspect of the present invention, the specific manner in which the generation module generates the optimization suggestions based on the key balance factors includes:

[0070] Determining a factor type of the key balance factor; the factor type includes a game design factor type and / or a test design factor type;

[0071] When the factor type of the key balance factor includes the game design factor type, generating a first optimization suggestion for the current game project based on the key balance factor; the first optimization suggestion is used to optimize the game design corresponding to the current game project;

[0072] When the factor type of the key balance factor includes the test design factor type, a second optimization suggestion for the balance test platform is generated based on the key balance factor; the second optimization suggestion is used to optimize the data generation algorithm corresponding to the balance test platform; the data generation algorithm includes a combination of one or more of a test data generation algorithm, an adversarial data generation algorithm and a battle matching algorithm.

[0073] As an optional implementation, in the second aspect of the present invention, the specific manner in which the generation module generates the first optimization suggestion for the current game project based on the key balance factor includes:

[0074] Determining the combat power difference corresponding to each group of the test tasks based on the key balance factors;

[0075] Filtering the test tasks whose combat power differences are greater than or equal to a preset combat power difference from the combat power differences corresponding to all the test tasks as the first reference tasks;

[0076] Analyzing the test data included in all the first reference tasks to obtain design defect information corresponding to the current game project; the design defect information includes one or more combinations of character design defect information, team design defect information, equipment design defect information, enemy design defect information, and level design defect information;

[0077] A first optimization suggestion for the current game project is generated based on the design defect information.

[0078] A third aspect of the present invention discloses another multi-mode game balance testing device, comprising:

[0079] a memory storing executable program code;

[0080] a processor coupled to the memory;

[0081] The processor calls the executable program code stored in the memory to execute part or all of the steps of the multi-mode game balance testing method disclosed in the first aspect of the present invention.

[0082] A fourth aspect of the present invention discloses a computer storage medium, which stores computer instructions. When the computer instructions are called, they are used to execute some or all steps of the multi-mode game balance testing method disclosed in the first aspect of the present invention.

[0083] Compared with the prior art, the present invention has the following beneficial effects:

[0084] In the present invention, the test requirements corresponding to the current game project are determined; the test requirements include test game modes, test mode setting parameters and test objects; according to the test requirements, multiple groups of test data that meet the test requirements are generated; wherein, when the test requirements include at least two test game modes, all test data include at least one group of test data for each test game mode; based on the data generation algorithm set by the balance test platform, adversarial data corresponding to each group of test data is generated according to the test requirements; based on the test simulation algorithm set by the balance test platform, a balance test is performed for each test task to obtain a test result; each test task includes one group of test data and adversarial data corresponding to the test data. It can be seen that the implementation of the present invention can generate multiple groups of test data that meet the test requirements according to the determined test requirements of the current game project, and generate corresponding adversarial data for each group of test data, so as to perform balance testing on each group of test data and the corresponding adversarial data based on the test simulation algorithm to obtain test results. It can realize balance testing based on the intelligent generation of test tasks that meet the test requirements, improve the generation efficiency and generation accuracy of test data, thereby improving the generation accuracy of corresponding adversarial data, and thus help to improve the efficiency of game balance testing while improving the accuracy of game balance test results, and thus help to improve the optimization accuracy of game content and balance testing process, and thus help to improve the playability of the game and the reliability of the testing process, and thus help to provide players with better quality game content. BRIEF DESCRIPTION OF THE DRAWINGS

[0085] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0086] Figure 1 This is a flowchart of a multi-mode game balance testing method disclosed in an embodiment of the present invention;

[0087] Figure 2 is a flowchart of another multi-mode game balance testing method disclosed in an embodiment of the present invention;

[0088] Figure 3 This is a flowchart of another multi-mode game balance testing method disclosed in an embodiment of the present invention;

[0089] Figure 4 1 is a schematic structural diagram of a multi-mode game balance testing device disclosed in an embodiment of the present invention;

[0090] Figure 5 1 is a schematic structural diagram of another multi-mode game balance testing device disclosed in an embodiment of the present invention;

[0091] Figure 6 This is a structural diagram of another multi-mode game balance testing device disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0092] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0093] The terms "first," "second," and so on, in the description and claims of the present invention and the accompanying drawings are used to distinguish between different items, not to describe a specific order. Furthermore, the terms "including," "having," and any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, apparatus, product, or end comprising a series of steps or elements is not limited to the listed steps or elements but may optionally include steps or elements not listed therein, or may optionally include other steps or elements inherent to such process, method, product, or end.

[0094] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present invention. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute a separate or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0095] The present invention discloses a multi-mode game balance testing method and device, which can generate multiple groups of test data that meet the test requirements according to the test requirements of the current game project determined, and generate corresponding adversarial data for each group of test data, so as to perform balance testing on each group of test data and the corresponding adversarial data based on the test simulation algorithm to obtain test results. It can realize balance testing based on intelligent generation of test tasks that meet the test requirements, improve the generation efficiency and generation accuracy of test data, thereby improving the generation accuracy of corresponding adversarial data, thereby facilitating the improvement of the efficiency of game balance testing while improving the accuracy of game balance test results, thereby facilitating the improvement of the optimization accuracy of game content and balance testing process, thereby facilitating the improvement of the playability of the game and the reliability of the testing process, thereby facilitating the provision of higher-quality game content to players. The following are detailed descriptions.

[0096] Example 1

[0097] See also Figure 1 , Figure 1 This is a flow chart of a multi-mode game balance testing method disclosed in an embodiment of the present invention. Figure 1 The multi-mode game balance test method described can be applied to a multi-mode game balance test device, which may include one of a test device, a test terminal, a test system, and a server, wherein the server may include a local server or a cloud server, which is not limited in the embodiment of the present invention; the method can also be applied to a balance test platform, which is not limited in the embodiment of the present invention. Figure 1 As shown, the multi-mode game balance testing method may include the following operations:

[0098] 101. Determine the testing requirements corresponding to the current game project.

[0099] In an embodiment of the present invention, the test requirements may include a test game mode, test mode setting parameters, and a test object.

[0100] 102. According to the test requirements, generate multiple sets of test data that meet the test requirements.

[0101] In an embodiment of the present invention, when the test requirements include at least two test game modes, all test data include at least one set of test data for each test game mode.

[0102] 103. Based on the data generation algorithm set by the balance test platform, generate adversarial data corresponding to each set of test data according to the test requirements.

[0103] In an embodiment of the present invention, optionally, step 103 may be specifically as follows: based on the data generation algorithm set by the balance test platform, according to the test requirements, generating adversarial data with a matching degree higher than or equal to a preset matching degree with each set of test data, which is not limited in the embodiment of the present invention.

[0104] 104. Based on the test simulation algorithm set by the balance test platform, perform a balance test for each test task to obtain a test result.

[0105] In the embodiment of the present invention, each test task includes a set of test data and adversarial data corresponding to the test data.

[0106] In an embodiment of the present invention, specifically, for each test task, when the test game mode corresponding to the test task is the PVP (Player Versus Player) mode, based on the above-mentioned test simulation algorithm and the test data and corresponding adversarial data included in the test task, a PVP battle is simulated to obtain the battle test result corresponding to the test task; when the test game mode corresponding to the test task is the PVE (Player Versus Environment) mode, based on the above-mentioned test simulation algorithm and the test data and corresponding adversarial data included in the test task, a PVE battle is simulated to obtain the level test result corresponding to the test task.

[0107] It can be seen that the implementation of the method described in the embodiment of the present invention can generate multiple groups of test data that meet the test requirements according to the determined test requirements of the current game project, and generate corresponding adversarial data for each group of test data, so as to perform balance testing on each group of test data and the corresponding adversarial data based on the test simulation algorithm to obtain test results. It can realize balance testing based on intelligent generation of test tasks that meet the test requirements, improve the generation efficiency and generation accuracy of test data, thereby improving the generation accuracy of corresponding adversarial data, which is beneficial to improving the efficiency of game balance testing while improving the accuracy of game balance test results, which is beneficial to improving the optimization accuracy of game content and balance testing process, which is beneficial to improving the playability of the game and the reliability of the testing process, and is beneficial to providing players with better quality game content.

[0108] In an optional embodiment, the balance testing platform may be provided with an automated testing tool, a fault tolerance mechanism, a load balancing mechanism, and a high-performance computing cluster.

[0109] It can be seen that this optional embodiment can use automated testing tools to reduce manual intervention and improve testing efficiency; and, by adopting a fault-tolerant mechanism, the system can still run stably when some servers fail, thereby improving test stability; and, by adopting load balancing technology, ensure that computing tasks are evenly distributed and avoid single-point bottlenecks; and, by using a high-performance computing cluster to support highly complex computing-intensive tasks, it is conducive to testing more diverse battle / level situations and improving the comprehensiveness of the test.

[0110] In an optional embodiment, the test game mode includes a PVP mode and / or a PVE mode; when the test game mode includes a PVP mode, the test mode setting parameters include test battle parameters, which include a test battle map and / or a test battle duration; when the test game mode includes a PVE mode, the test mode setting parameters include test level parameters, which include a test level type and / or a test level difficulty; the test object includes a combination of one or more of a target character, a target lineup, a target value, a target level, and a target difficulty;

[0111] Each set of test data includes a test lineup; the test lineup includes at least one test character; each set of test data also includes test character parameters corresponding to each test character in the test lineup; the test character parameters include one or more combinations of test character level, test character equipment, and test character skills;

[0112] For each set of test data, when the test game mode corresponding to the test data is PVP mode, the adversarial data corresponding to the test data includes the adversarial lineup and the adversarial character parameters corresponding to each adversarial character in the adversarial lineup; when the test game mode corresponding to the test data is PVE mode, the adversarial data corresponding to the test data includes the level enemy lineup and the enemy parameters corresponding to each level enemy in the level enemy lineup.

[0113] Among them, the antagonist character parameters corresponding to each antagonist character may include a combination of one or more of the antagonist character level, antagonist character equipment and antagonist character skills; the enemy parameters corresponding to each level enemy may include a combination of one or more of the enemy level, enemy equipment and enemy skills, which is not limited in the embodiment of the present invention.

[0114] It can be seen that this optional embodiment can determine more specific and comprehensive test requirements, which is conducive to improving the comprehensiveness and accuracy of subsequently generated test data; and, it records more specific and comprehensive test data types, which can improve the accuracy and comprehensiveness of test data generation; and, based on test data and test game modes, it determines the corresponding adversarial data type, which is conducive to improving the degree of matching between adversarial data and test data, thereby improving the accuracy of adversarial data generation.

[0115] In another optional embodiment, before generating multiple groups of test data that meet the test requirements according to the test requirements, the method may further include the following operations:

[0116] Receive custom account parameters input by the tester; or, generate custom account parameters based on test requirements; custom account parameters include one or more combinations of account basic parameters, account role parameters, account resource parameters, and account mode parameters; account basic parameters include one or more combinations of account number, account type, account level, and account resources; account role parameters include one or more combinations of character number, character type, character level, character equipment, and character skills; account mode parameters include the game mode type applicable to the test account to be generated;

[0117] Generate multiple test accounts based on custom account parameters and import all test accounts into the balance testing platform.

[0118] Among them, the number of accounts is the number of test accounts that need to be generated; the account type includes the account type of each test account that needs to be generated. For example, the account type can be an ordinary player type or a VIP player type, which is not limited in this embodiment of the present invention; the account level includes the account level range of each test account that needs to be generated; the number of characters in the account role parameter includes the number of characters owned by each test account that needs to be generated, and the role type in the account role parameter includes the role type of each character owned by each test account that needs to be generated. For example, the role type can be a warrior, a mage, a shooter, etc., or other character types, which is not limited in this embodiment of the present invention; the account resource parameter includes the account resources owned by each test account that needs to be generated. For example, the account resources can include gold coins, diamonds, props, etc., or they can be game resources that other players can use in the account, which is not limited in this embodiment of the present invention.

[0119] It can be seen that this optional embodiment can generate multiple test accounts according to the custom account parameters by receiving the custom account parameters input by the tester or generating custom account parameters according to the test requirements, so as to import the test accounts into the balance testing platform, which can improve the flexibility and accuracy of determining the custom account parameters, thereby realizing the intelligent generation of test accounts, improving the accuracy of test account generation, and further helping to improve the accuracy of importing test accounts.

[0120] In this optional embodiment, optionally, according to the test requirements, multiple groups of test data that meet the test requirements are generated, including:

[0121] According to the test requirements, at least one set of test data that meets the test requirements is generated for each test account.

[0122] It can be seen that this optional embodiment can also generate test data that meets the test requirements for each test account. Generating test data in combination with account information can further improve the accuracy of test data generation and is conducive to improving the accuracy and comprehensiveness of subsequent analysis of test results.

[0123] In yet another optional embodiment, based on a test simulation algorithm set by the balance test platform, performing a balance test for each test task to obtain a test result may include the following operations:

[0124] Based on the distributed computing technology corresponding to the balance test platform, each test task is assigned to a target server that meets the preset assignment conditions; the target server is one of the multiple test servers corresponding to the balance test platform;

[0125] Based on all target servers, balance tests are performed in parallel for multiple test tasks until the balance tests are completed for all test tasks to obtain test results; wherein, the test results include the test task results corresponding to each test task; the test task results include a combination of one or more of battle results, character performance results and test resource consumption results; the battle results include win-loss results and / or battle duration.

[0126] Optionally, based on the distributed computing technology corresponding to the balance test platform, each test task is assigned to the target server that meets the preset allocation conditions. Specifically, based on the distributed computing technology corresponding to the balance test platform, each test task is assigned to the target server with the least number of connections in turn. This is not limited in the embodiments of the present invention.

[0127] Among them, the victory and defeat results in the battle results include the victory and defeat results of each battle or level, and the battle duration in the battle results includes the duration of each battle or level; the character performance results include the performance data of each character. For example, the performance data may include one or more combinations of the character's damage output data, the character's treatment data, and the character's damage-bearing data, and may also include character performance data in other tests, which is not limited in the embodiment of the present invention; the test resource consumption results may include the resource consumption in each battle or level. For example, the resource consumption may include one or more combinations of the number of skill uses, the type of consumable items, and the number of consumable items, and may also include other resources that can be used in the game, which is not limited in the embodiment of the present invention.

[0128] It can be seen that this optional embodiment can allocate each test task to a target server that meets the preset allocation conditions based on distributed computing technology, and then perform balance tests in parallel for multiple test tasks based on the target server until the balance tests of all test tasks are completed and the test results are obtained. By using distributed technology, the test tasks can be allocated to multiple servers for parallel processing, thereby improving computing efficiency, thereby improving the processing efficiency of the test tasks, and further helping to further improve the efficiency of the balance test.

[0129] In this optional embodiment, in the process of performing the balance test on multiple test tasks in parallel based on all target servers until the balance test is completed for all test tasks and the test results are obtained, the method may further include the following operations:

[0130] Based on the caching technology corresponding to the balance test platform, the calculation results corresponding to each test task are cached to obtain cached calculation results; the calculation results include the test task results corresponding to the test task and / or the intermediate calculation results during the balance test process corresponding to the test task;

[0131] After detecting that the cached calculation result is not empty, before executing the balance test for each test task, detecting whether the balance test platform has cached the cached calculation result related to the test task;

[0132] For each test task, when it is detected that the balance test platform caches a cache calculation result related to the test task, the cache calculation result related to the test task is read, and based on the read cache calculation result, the test task result corresponding to the test task is determined;

[0133] For each test task, when it is detected that the balance test platform does not cache the cache calculation results related to the test task, a balance test is performed on the test task based on the target server corresponding to the test task to obtain the test task result corresponding to the test task.

[0134] Optionally, for each test task, determining a test task result corresponding to the test task based on the read cached calculation result may include the following operations:

[0135] When the cached calculation result read includes the intermediate calculation result required by the test task, based on the target server corresponding to the test task and the intermediate calculation result, a balance test is performed on the test task to obtain the test task result corresponding to the test task;

[0136] When the read cache calculation result includes the test task result required by the test task, the read test task result is used as the test task result corresponding to the test task.

[0137] It can be seen that, in the process of processing the test task in parallel, the optional embodiment can cache the calculation result based on the caching technology, and if the related calculation result is detected before the test task is processed again, the test task result can be determined based on the calculation result, which can reduce repeated calculation and save computing resources, thereby facilitating improvement of the balance test efficiency.

[0138] Embodiment two

[0139] Please refer to Figure 2 , Figure 2 is a flowchart of a multi-mode game balance test method according to an embodiment of the application. Among them, Figure 2 The multi-mode game balance test method described can be applied to a multi-mode game balance test device, which can include one of a test device, a test terminal, a test system and a server, wherein the server can include a local server or a cloud server, and the embodiments of the application are not limited; the method can also be applied to a balance test platform, and the embodiments of the application are not limited. As shown in Figure 2 The multi-mode game balance test method can include the following operations:

[0140] 201, determine the test requirement corresponding to the current game project.

[0141] In the embodiments of the application, the test requirement includes a test game mode, a test mode setting parameter and a test object.

[0142] 202, generate a plurality of sets of test data meeting the test requirement according to the test requirement.

[0143] In the embodiments of the application, when the test requirement includes at least two test game modes, all the test data include at least one set of test data for each test game mode.

[0144] 203, generate the adversarial data corresponding to each set of test data according to the test requirement based on the data generation algorithm set by the balance test platform.

[0145] 204, perform balance test for each test task based on the test simulation algorithm set by the balance test platform to obtain a test result.

[0146] In the embodiments of the application, each test task includes one set of test data and the adversarial data corresponding to the test data.

[0147] 205, analyze the test result to obtain a balance analysis result corresponding to the current game project.

[0148] In the embodiment of the present application, the balance analysis result is used to represent the numerical balance degree of the battle / level design of the current game project; wherein, optionally, the balance analysis result can include one or more combinations of the lineup information of the attack side / defense side, the character information of the attack side / defense side, the battle scene win rate, the average damage per scene, the average treatment per scene, the average damage per scene, the average treatment per scene, and the average survival round, and the embodiment of the present application is not limited thereto.

[0149] 206. According to the balance analysis result, it is judged whether the current game project meets the pre-set balance condition.

[0150] In the embodiment of the present application, when it is judged in step 206 that the current game project does not meet the balance condition, that is, the judgment result of step 206 is no, the operation of step 207 is performed; optionally, when the judgment result of step 206 is yes, the process is ended, or according to the test result, a test report of the current game project on the test requirement is generated, and the embodiment of the present application is not limited thereto.

[0151] 207. According to the balance analysis result, the balance key factor corresponding to the current game project is determined.

[0152] 208. According to the balance key factor, an optimization suggestion is generated.

[0153] In the embodiment of the present application, the optimization suggestion includes a first optimization suggestion and / or a second optimization suggestion; optionally, the optimization object corresponding to the first optimization suggestion can be the current game project, and the optimization object corresponding to the second optimization suggestion can be the balance test platform, and the embodiment of the present application is not limited thereto.

[0154] 209. According to the optimization suggestion, the optimization object corresponding to the optimization suggestion is optimized.

[0155] In the embodiment of the present application, the optimization object includes the current game project and / or the balance test platform.

[0156] In the embodiment of the present application, for other detailed descriptions of steps 201-204, please refer to the detailed description of steps 101-104 in embodiment one, and the embodiment of the present application will not be repeated here.

[0157] It can be seen that the implementation of the method described in the embodiment of the present invention can generate multiple groups of test data that meet the test requirements according to the determined test requirements of the current game project, and generate corresponding adversarial data for each group of test data, so as to perform balance testing on each group of test data and the corresponding adversarial data based on the test simulation algorithm to obtain test results. It can realize balance testing based on intelligent generation of test tasks that meet the test requirements, improve the generation efficiency and generation accuracy of test data, thereby improving the generation accuracy of corresponding adversarial data, which is beneficial to improving the efficiency of game balance testing while improving the accuracy of game balance test results, which is beneficial to improving the optimization accuracy of game content and balance testing process, which is beneficial to improving the playability of the game and the reliability of the testing process, and is beneficial to providing players with better quality game content. In addition, by analyzing the test results, the balance analysis results can be obtained. If it is judged based on the balance analysis results that the current game project does not meet the balance conditions, the key balance factors corresponding to the current game project are first determined, and then optimization suggestions are generated based on the key balance factors to optimize the corresponding optimization objects. This can improve the comprehensiveness and accuracy of the analysis of the test results, thereby helping to improve the accuracy of determining the key balance factors, so as to more accurately generate optimization suggestions for game content or testing platforms, and thus help to improve the optimization accuracy of game content and testing processes.

[0158] In an optional embodiment, generating optimization suggestions based on key balance factors may include the following operations:

[0159] Determine the factor type of key balance factors; the factor type includes game design factor type and / or test design factor type;

[0160] When the factor type of the key balance factor includes a game design factor type, generating a first optimization suggestion for the current game project based on the key balance factor; the first optimization suggestion is used to optimize the game design corresponding to the current game project;

[0161] When the factor type of the key balance factor includes a test design factor type, a second optimization suggestion for the balance test platform is generated based on the key balance factor; the second optimization suggestion is used to optimize the data generation algorithm corresponding to the balance test platform; the data generation algorithm includes a combination of one or more of a test data generation algorithm, an adversarial data generation algorithm, and a battle matching algorithm.

[0162] Among them, exemplary key balance factors may include one or more combinations of account level differences, character level differences, character equipment differences, character skill differences, card interstellar differences, and data matching differences between test data and adversarial data, which are not limited in the embodiments of the present invention.

[0163] It can be seen that this optional embodiment can generate optimization suggestions corresponding to different types according to the factor types of key balance factors, which can improve the accuracy of analysis of key balance factors, thereby facilitating targeted optimization of game design and / or test data generation logic, and further facilitating more accurate iterative optimization of game content and test processes.

[0164] In this optional embodiment, optionally, generating a first optimization suggestion for the current game project based on the key balance factor may include the following operations:

[0165] Determine the combat power difference corresponding to each set of test tasks based on key balance factors;

[0166] Filter out the test tasks whose combat power difference is greater than or equal to the preset combat power difference from the combat power difference values ​​corresponding to all test tasks, and use them as the first reference tasks;

[0167] Analyze all test data included in the first reference task to obtain design defect information corresponding to the current game project; the design defect information includes one or more combinations of character design defect information, team design defect information, equipment design defect information, enemy design defect information, and level design defect information;

[0168] Based on the design defect information, generate the first optimization suggestion for the current game project.

[0169] Among them, the combat power difference corresponding to each group of test tasks is used to represent the combat power difference between the two sides of each group of test tasks or the combat power difference between the test character and the level enemy, which is not limited in this embodiment of the present invention.

[0170] It can be seen that this optional embodiment can also screen out the first reference task with a larger combat power gap from all test tasks, and analyze the design defects of the game based on the first reference task, so as to accurately generate the first optimization suggestion for the current game project, and can accurately analyze the defects in the game design based on the test results, which is conducive to improving the accuracy of generating optimization suggestions for game content, thereby further improving the optimization accuracy of game content.

[0171] In this optional embodiment, optionally, generating a second optimization suggestion for the balance test platform based on the key balance factors may include the following operations:

[0172] Based on the key balance factor, target test data whose numerical settings do not satisfy a pre-set first numerical condition are screened out from all test data; and / or, based on the key balance factor, target adversarial data whose numerical settings do not satisfy a pre-set second numerical condition are screened out from all adversarial data; and / or, based on the key balance factor, second reference tasks whose data difference between the test data and the adversarial data does not satisfy a pre-set third numerical condition are screened out from all test tasks;

[0173] Analyze data generation defect information existing in the balance test platform based on all target test data and / or all target adversarial data and / or all second reference tasks; the data generation defect information may include at least one of a test data generation logic defect, an adversarial data generation logic defect, and a data pairing logic defect;

[0174] Defect information is generated based on the data, and a second optimization suggestion for the balance test platform is generated.

[0175] It can be seen that this optional embodiment can also filter out target data or second reference tasks that do not meet the preset numerical conditions from the test data, adversarial data, and test tasks, and analyze the data generation defects of the balance test platform based on the above data that do not meet the numerical conditions, so as to generate targeted optimization suggestions corresponding to the data generation process of the balance test platform, thereby improving the generation accuracy of the optimization suggestions, and further improving the optimization accuracy of the test process.

[0176] In another optional embodiment, the method may further include the following operations:

[0177] Based on the optimized optimization object, the next batch of test data and corresponding adversarial data are generated to conduct the next batch of balance tests, thereby iteratively optimizing the game content of the current detailed project and the test process of the balance test platform.

[0178] It can be seen that this optional embodiment can perform iterative testing and optimization based on the optimized content, which can be helpful in verifying whether the optimization results meet expectations, thereby helping to improve the optimization accuracy of game content and testing processes.

[0179] In the embodiment of the present invention, the process of the multi-mode game balance testing method can also be as follows: Figure 3 As shown, Figure 3 This is a flowchart of another multi-mode game balance testing method disclosed in an embodiment of the present invention. Specifically, the method may include the following operations:

[0180] First, determine the game testing requirements, then generate custom parameters, then import multiple test accounts and select test data that meets the requirements, or directly generate test data without generating test accounts; then generate adversarial data corresponding to the test data, and then perform a balance test based on the generated data, and generate a balance analysis result based on the test results, and then adjust the next batch of test data based on the balance analysis result, so as to iteratively optimize the test process and game content, which is not limited in the embodiment of the present invention.

[0181] Example 3

[0182] See also Figure 4 , Figure 4 : is a schematic diagram of the structure of a multi-mode game balance testing device disclosed in an embodiment of the present invention. Figure 4 The multi-mode game balance test device described may include one of a test device, a test terminal, a test system, and a server, wherein the server may include a local server or a cloud server, which is not limited in the embodiment of the present invention; the device may be applied to a balance test platform, which is not limited in the embodiment of the present invention. Figure 4 As shown, the multi-mode game balance testing device may include:

[0183] Determination module 301 is used to determine the test requirements corresponding to the current game project; the test requirements include test game mode, test mode setting parameters and test objects;

[0184] A generating module 302 is configured to generate multiple sets of test data that meet the test requirements according to the test requirements; wherein, when the test requirements include at least two test game modes, all test data include at least one set of test data for each test game mode;

[0185] The generation module 302 is further configured to generate adversarial data corresponding to each set of test data based on the data generation algorithm set by the balance test platform and according to the test requirements;

[0186] The testing module 303 is used to perform a balance test on each test task based on the test simulation algorithm set by the balance testing platform to obtain a test result; each test task includes a set of test data and adversarial data corresponding to the test data.

[0187] It can be seen that the device described in the embodiment of the present invention can generate multiple groups of test data that meet the test requirements according to the determined test requirements of the current game project, and generate corresponding adversarial data for each group of test data, so as to perform balance testing on each group of test data and the corresponding adversarial data based on the test simulation algorithm to obtain test results. It can realize balance testing based on the intelligent generation of test tasks that meet the test requirements, improve the generation efficiency and generation accuracy of test data, thereby improving the generation accuracy of corresponding adversarial data, which is beneficial to improving the efficiency of game balance testing while improving the accuracy of game balance test results, which is beneficial to improving the optimization accuracy of game content and balance testing process, which is beneficial to improving the playability of the game and the reliability of the testing process, and is beneficial to providing players with better quality game content.

[0188] In an optional embodiment, the test game mode includes a PVP mode and / or a PVE mode; when the test game mode includes a PVP mode, the test mode setting parameters include test battle parameters, which include a test battle map and / or a test battle duration; when the test game mode includes a PVE mode, the test mode setting parameters include test level parameters, which include a test level type and / or a test level difficulty; the test object includes a combination of one or more of a target character, a target lineup, a target value, a target level, and a target difficulty;

[0189] Each set of test data includes a test lineup; the test lineup includes at least one test character; each set of test data also includes test character parameters corresponding to each test character in the test lineup; the test character parameters include one or more combinations of test character level, test character equipment, and test character skills;

[0190] For each set of test data, when the test game mode corresponding to the test data is PVP mode, the adversarial data corresponding to the test data includes the adversarial lineup and the adversarial character parameters corresponding to each adversarial character in the adversarial lineup; when the test game mode corresponding to the test data is PVE mode, the adversarial data corresponding to the test data includes the level enemy lineup and the enemy parameters corresponding to each level enemy in the level enemy lineup.

[0191] It can be seen that the device described in the implementation of this optional embodiment can determine more specific and comprehensive testing requirements, which is conducive to improving the comprehensiveness and accuracy of the test data generated subsequently; and, it records more specific and comprehensive test data types, which can improve the accuracy and comprehensiveness of test data generation; and, based on the test data and test game mode, it determines the corresponding adversarial data type, which is conducive to improving the matching degree between adversarial data and test data, thereby improving the accuracy of adversarial data generation.

[0192] In another optional embodiment, Figure 5 As shown, the device may also include:

[0193] The receiving module 304 is configured to receive custom account parameters input by the tester before the generating module 302 generates multiple sets of test data that meet the test requirements according to the test requirements; or to generate custom account parameters according to the test requirements; the custom account parameters include one or more combinations of basic account parameters, account role parameters, account resource parameters, and account mode parameters; the basic account parameters include one or more combinations of the number of accounts, account type, account level, and account resources; the account role parameters include one or more combinations of the number of characters, character type, character level, character equipment, and character skills; and the account mode parameters include the game mode type applicable to the test account to be generated;

[0194] The generation module 302 is further used to generate multiple test accounts based on the custom account parameters and import all the test accounts into the balance testing platform;

[0195] Furthermore, the specific manner in which the generating module 302 generates multiple groups of test data that meet the test requirements according to the test requirements may include:

[0196] According to the test requirements, at least one set of test data that meets the test requirements is generated for each test account.

[0197] It can be seen that the device described in the implementation of this optional embodiment can generate multiple test accounts according to the custom account parameters by receiving the custom account parameters input by the tester or generating custom account parameters according to the test requirements, so as to import the test accounts into the balance testing platform, which can improve the flexibility and accuracy of determining the custom account parameters, thereby realizing the intelligent generation of test accounts, improving the accuracy of test account generation, and thus helping to improve the accuracy of importing test accounts; and, it can also generate test data that meets the test requirements for each test account. Generating test data in combination with account information can further improve the accuracy of test data generation, and is conducive to improving the accuracy and comprehensiveness of subsequent analysis of test results.

[0198] In another optional embodiment, the testing module 303 performs a balance test for each test task based on a test simulation algorithm set by the balance testing platform. The specific method of obtaining the test result may include:

[0199] Based on the distributed computing technology corresponding to the balance test platform, each test task is assigned to a target server that meets the preset assignment conditions; the target server is one of the multiple test servers corresponding to the balance test platform;

[0200] The balancing test is performed in parallel for the plurality of test tasks based on all target servers until the balancing test is completed for all test tasks, and test results are obtained; the test results include test task results corresponding to each test task; the test task results include one or more combinations of battle results, character performance results, and test resource consumption results; the battle results include win / loss results and / or battle durations;

[0201] As shown in FIG. 7, the apparatus can further include: Figure 5

[0202] The cache module 305 is configured to cache a calculation result corresponding to each test task based on a cache technology corresponding to the balancing test platform during the process in which the test module 303 performs the balancing test in parallel for the plurality of test tasks based on all target servers until the balancing test is completed for all test tasks, and obtains test results; the calculation result includes a test task result corresponding to the test task and / or an intermediate calculation result in the process of performing the balancing test corresponding to the test task.

[0203] The detection module 306 is configured to, after detecting that the cached calculation result is not empty, detect whether the balancing test platform caches a cached calculation result related to each test task before performing the balancing test for the test task.

[0204] The reading module 307 is configured to, for each test task, read the cached calculation result related to the test task when the detection module 306 detects that the balancing test platform caches the cached calculation result related to the test task, and determine the test task result corresponding to the test task based on the read cached calculation result.

[0205] The test module 303 is further configured to, for each test task, perform the balancing test for the test task based on the target server corresponding to the test task when the detection module 306 detects that the balancing test platform does not cache the cached calculation result related to the test task, and obtain the test task result corresponding to the test task.

[0206] ​It can be seen that the device described in the implementation of this optional embodiment can, based on distributed computing technology, allocate each test task to a target server that meets the preset allocation conditions, and then, based on the target server, perform balance tests in parallel for multiple test tasks until the balance tests of all test tasks are completed and the test results are obtained. By using distributed technology, the test tasks can be allocated to multiple servers for parallel processing, thereby improving computing efficiency, thereby improving the processing efficiency of the test tasks, and thus helping to further improve the efficiency of the balance test; and, in the process of parallel processing of test tasks, the calculation results can be cached based on caching technology, and if relevant calculation results are detected before processing the test task, the test task results can be determined based on the calculation results, which can reduce repeated calculations and save computing resources, thereby helping to improve the efficiency of the balance test.

[0207] In another optional embodiment, Figure 5 As shown, the device may also include:

[0208] The analysis module 308 is configured to perform a balance test on each test task based on the test simulation algorithm set by the balance test platform in the test module 303, and after obtaining the test results, analyze the test results to obtain a balance analysis result corresponding to the current game project;

[0209] The judgment module 309 is used to judge whether the current game project meets the preset balance conditions based on the balance analysis results;

[0210] The determination module 301 is further configured to determine the key balance factors corresponding to the current game item based on the balance analysis results when the judgment module 309 determines that the current game item does not meet the balance condition;

[0211] The generation module 302 is further configured to generate an optimization suggestion based on the key balance factors; the optimization suggestion includes a first optimization suggestion and / or a second optimization suggestion;

[0212] The optimization module 310 is used to optimize the optimization object corresponding to the optimization suggestion according to the optimization suggestion; the optimization object includes the current game project and / or the balance test platform.

[0213] It can be seen that the device described in the implementation of this optional embodiment can obtain a balance analysis result by analyzing the test results. If it is determined based on the balance analysis result that the current game project does not meet the balance conditions, the key balance factors corresponding to the current game project are first determined, and then optimization suggestions are generated based on the key balance factors to optimize the corresponding optimization objects. This can improve the comprehensiveness and accuracy of the analysis of the test results, thereby helping to improve the accuracy of determining the key balance factors, so as to more accurately generate optimization suggestions for game content or test platforms, and thus help to improve the optimization accuracy of game content and test processes.

[0214] In this optional embodiment, the specific manner in which the generation module 302 generates optimization suggestions based on the key balance factors may include:

[0215] Determine the factor type of key balance factors; the factor type includes game design factor type and / or test design factor type;

[0216] When the factor type of the key balance factor includes a game design factor type, generating a first optimization suggestion for the current game project based on the key balance factor; the first optimization suggestion is used to optimize the game design corresponding to the current game project;

[0217] When the factor type of the key balance factor includes a test design factor type, a second optimization suggestion for the balance test platform is generated based on the key balance factor; the second optimization suggestion is used to optimize the data generation algorithm corresponding to the balance test platform; the data generation algorithm includes a combination of one or more of a test data generation algorithm, an adversarial data generation algorithm, and a battle matching algorithm.

[0218] It can be seen that the device described in the implementation of this optional embodiment can also generate optimization suggestions corresponding to different types according to the factor type of the key balance factor, which can improve the accuracy of the analysis of the key balance factors, thereby facilitating the targeted optimization of game design and / or test data generation logic, and further facilitating more accurate iterative optimization of game content and testing processes.

[0219] In this optional embodiment, the specific manner in which the generation module 302 generates the first optimization suggestion for the current game item based on the key balance factor may include:

[0220] Determine the combat power difference corresponding to each set of test tasks based on key balance factors;

[0221] Filter out the test tasks whose combat power difference is greater than or equal to the preset combat power difference from the combat power difference values ​​corresponding to all test tasks, and use them as the first reference tasks;

[0222] Analyze all test data included in the first reference task to obtain design defect information corresponding to the current game project; the design defect information includes one or more combinations of character design defect information, team design defect information, equipment design defect information, enemy design defect information, and level design defect information;

[0223] Based on the design defect information, generate the first optimization suggestion for the current game project.

[0224] It can be seen that the device described in the implementation of this optional embodiment can also screen out the first reference task with a larger combat power gap from all test tasks, and analyze the design defects of the game based on the first reference task, so as to accurately generate the first optimization suggestion for the current game project. It can accurately analyze the defects in the game design based on the test results, which is conducive to improving the accuracy of generating optimization suggestions for game content, thereby further improving the optimization accuracy of game content.

[0225] Example 4

[0226] See also Figure 6 , Figure 6 This is a structural diagram of another multi-mode game balance testing device disclosed in an embodiment of the present invention. Figure 6 As shown, the multi-mode game balance testing device may include:

[0227] A memory 401 storing executable program code;

[0228] a processor 402 coupled to the memory 401;

[0229] The processor 402 calls the executable program code stored in the memory 401 to execute part or all of the steps in the multi-mode game balance testing method described in the first embodiment of the present invention or the second embodiment of the present invention.

[0230] Example 5

[0231] An embodiment of the present invention discloses a computer storage medium, which stores computer instructions. When the computer instructions are called, they are used to execute some or all of the steps in the multi-mode game balance testing method described in Example 1 or Example 2 of the present invention.

[0232] Example 6

[0233] An embodiment of the present invention discloses a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute some or all of the steps in the multi-mode game balance testing method described in Example 1 or Example 2.

[0234] The device embodiments described above are merely illustrative, wherein the modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical modules, i.e., they may be located in one place or distributed across multiple network modules. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Those skilled in the art can understand and implement the present invention without inventive effort.

[0235] Through the detailed description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus the necessary general hardware platform, or of course, by means of hardware. Based on this understanding, the above technical solution, in essence, or the portion that contributes to the prior art, can be embodied in the form of a software product, which can be stored in a computer-readable storage medium, including a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, magnetic disk storage, magnetic tape storage, or any other computer-readable medium capable of carrying or storing data.

[0236] Finally, it should be noted that the multi-mode game balance testing method and device disclosed in the embodiments of the present invention only disclose a preferred embodiment of the present invention, which is only used to illustrate the technical solution of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, it should be understood by those skilled in the art that the technical solutions described in the aforementioned embodiments can still be modified, or some of the technical features therein can be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A multi-mode game balance testing method, characterized in that: The method is applied to a balance test platform, and the method includes: Determine the test requirements corresponding to the current game project; the test requirements include test game mode, test mode setting parameters and test objects; According to the test requirements, multiple groups of test data that meet the test requirements are generated; wherein, when the test requirements include at least two test game modes, all the test data include at least one group of test data for each test game mode; Based on the data generation algorithm set by the balance test platform and according to the test requirements, generate adversarial data corresponding to each set of the test data; Based on the test simulation algorithm set by the balance test platform, a balance test is performed for each test task to obtain a test result; each test task includes a set of the test data and adversarial data corresponding to the test data; Wherein, the test game mode includes a PVP mode and / or a PVE mode; when the test game mode includes the PVP mode, the test mode setting parameters include test battle parameters, and the test battle parameters include a test battle map and / or a test battle duration; when the test game mode includes the PVE mode, the test mode setting parameters include test level parameters, and the test level parameters include a test level type and / or a test level difficulty; the test object includes a combination of one or more of a target character, a target lineup, a target value, a target level, and a target difficulty; Each set of test data includes a test lineup; the test lineup includes at least one test character; each set of test data also includes test character parameters corresponding to each test character in the test lineup; the test character parameters include a combination of one or more of the test character level, test character equipment, and test character skills; For each set of test data, when the test game mode corresponding to the test data is the PVP mode, the adversarial data corresponding to the test data includes the adversarial lineup and the adversarial character parameters corresponding to each adversarial character in the adversarial lineup; when the test game mode corresponding to the test data is the PVE mode, the adversarial data corresponding to the test data includes the level enemy lineup and the enemy parameters corresponding to each level enemy in the level enemy lineup.

2. The multi-mode game balance testing method according to claim 1, characterized in that: Before generating multiple groups of test data that meet the test requirements according to the test requirements, the method further includes: Receive custom account parameters input by the tester; or, generate custom account parameters based on the test requirements; the custom account parameters include one or more combinations of account basic parameters, account role parameters, account resource parameters, and account mode parameters; the account basic parameters include one or more combinations of the number of accounts, account type, account level, and account resources; the account role parameters include one or more combinations of the number of characters, character type, character level, character equipment, and character skills; the account mode parameters include the game mode type applicable to the test account to be generated; Generate multiple test accounts according to the custom account parameters, and import all the test accounts into the balance testing platform; Furthermore, generating multiple groups of test data that meet the test requirements according to the test requirements includes: According to the test requirements, at least one set of test data is generated for each test account so that the test account meets the test requirements.

3. The multi-mode game balance testing method according to claim 1 or 2, characterized in that: The test simulation algorithm set based on the balance test platform performs a balance test for each test task to obtain a test result, including: Based on the distributed computing technology corresponding to the balance test platform, each test task is assigned to a target server that meets the preset assignment conditions; the target server is one of the multiple test servers corresponding to the balance test platform; Based on all the target servers, balance tests are performed in parallel for multiple test tasks until the balance tests are completed for all the test tasks, and test results are obtained; wherein the test results include test task results corresponding to each of the test tasks; the test task results include a combination of one or more of battle results, character performance results, and test resource consumption results; the battle results include win-loss results and / or battle duration; In the process of performing the balance test on multiple test tasks in parallel based on all the target servers until the balance test is completed for all the test tasks and the test results are obtained, the method further includes: Based on the caching technology corresponding to the balance test platform, the calculation results corresponding to each of the test tasks are cached to obtain cached calculation results; the calculation results include the test task results corresponding to the test task and / or the intermediate calculation results during the balance test process corresponding to the test task; After detecting that the cache calculation result is not empty, before executing the balance test for each test task, detecting whether the balance test platform has cached the cache calculation result related to the test task; For each of the test tasks, when it is detected that the balance test platform caches a cache calculation result related to the test task, the cache calculation result related to the test task is read, and based on the read cache calculation result, a test task result corresponding to the test task is determined; For each of the test tasks, when it is detected that the balance test platform does not cache the cache calculation results related to the test task, a balance test is performed on the test task based on the target server corresponding to the test task to obtain the test task result corresponding to the test task.

4. The multi-mode game balance testing method according to claim 1 or 2, characterized in that: After performing a balance test on each test task based on the test simulation algorithm set by the balance test platform and obtaining the test results, the method further includes: Analyze the test results to obtain a balance analysis result corresponding to the current game project; Determining whether the current game item satisfies a preset balance condition based on the balance analysis result; When it is determined that the current game item does not meet the balance condition, determining a key balance factor corresponding to the current game item according to the balance analysis result; generating an optimization suggestion based on the key balance factor; the optimization suggestion includes a first optimization suggestion and / or a second optimization suggestion; According to the optimization suggestion, the optimization object corresponding to the optimization suggestion is optimized; the optimization object includes the current game project and / or the balance testing platform.

5. The multi-mode game balance testing method according to claim 4, characterized in that: Generating optimization suggestions based on the key balance factors includes: Determining a factor type of the key balance factor; the factor type includes a game design factor type and / or a test design factor type; When the factor type of the key balance factor includes the game design factor type, generating a first optimization suggestion for the current game project based on the key balance factor; the first optimization suggestion is used to optimize the game design corresponding to the current game project; When the factor type of the key balance factor includes the test design factor type, a second optimization suggestion for the balance test platform is generated based on the key balance factor; the second optimization suggestion is used to optimize the data generation algorithm corresponding to the balance test platform; the data generation algorithm includes a combination of one or more of a test data generation algorithm, an adversarial data generation algorithm and a battle matching algorithm.

6. The multi-mode game balance testing method according to claim 5, characterized in that: Generating a first optimization suggestion for the current game project based on the key balance factor includes: Determining the combat power difference corresponding to each group of the test tasks based on the key balance factors; Filtering the test tasks whose combat power differences are greater than or equal to a preset combat power difference from the combat power differences corresponding to all the test tasks as the first reference tasks; Analyzing the test data included in all the first reference tasks to obtain design defect information corresponding to the current game project; the design defect information includes one or more combinations of character design defect information, team design defect information, equipment design defect information, enemy design defect information, and level design defect information; A first optimization suggestion for the current game project is generated based on the design defect information.

7. A multi-mode game balance testing device, characterized in that: The device is applied to a balance test platform, and comprises: A determination module is used to determine the test requirements corresponding to the current game project; the test requirements include a test game mode, test mode setting parameters, and a test object; a generating module, configured to generate, based on the test requirements, a plurality of sets of test data that meet the test requirements; wherein, when the test requirements include at least two test game modes, all the test data include at least one set of test data for each test game mode; The generating module is further configured to generate adversarial data corresponding to each set of the test data based on the data generating algorithm set by the balance testing platform and in accordance with the test requirements; A testing module, configured to perform a balance test on each test task based on a test simulation algorithm set by the balance testing platform to obtain a test result; each test task includes a set of test data and adversarial data corresponding to the test data; Wherein, the test game mode includes a PVP mode and / or a PVE mode; when the test game mode includes the PVP mode, the test mode setting parameters include test battle parameters, and the test battle parameters include a test battle map and / or a test battle duration; when the test game mode includes the PVE mode, the test mode setting parameters include test level parameters, and the test level parameters include a test level type and / or a test level difficulty; the test object includes a combination of one or more of a target character, a target lineup, a target value, a target level, and a target difficulty; Each set of test data includes a test lineup; the test lineup includes at least one test character; each set of test data also includes test character parameters corresponding to each test character in the test lineup; the test character parameters include a combination of one or more of the test character level, test character equipment, and test character skills; For each set of test data, when the test game mode corresponding to the test data is the PVP mode, the adversarial data corresponding to the test data includes the adversarial lineup and the adversarial character parameters corresponding to each adversarial character in the adversarial lineup; when the test game mode corresponding to the test data is the PVE mode, the adversarial data corresponding to the test data includes the level enemy lineup and the enemy parameters corresponding to each level enemy in the level enemy lineup.

8. A multi-mode game balance testing device, characterized in that: The device comprises: a memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the multi-mode game balance testing method according to any one of claims 1 to 6.

9. A computer storage medium, characterized in that The computer storage medium stores computer instructions, which, when called, are used to execute the multi-mode game balance testing method according to any one of claims 1 to 6.

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