Game abnormal problem identification method and related equipment

Abstract

The embodiment of the invention discloses a game exception problem identification method and related equipment, and the method comprises the steps: obtaining a game exception detection request, and constructing a plurality of game test cases based on the game exception detection request; respectively executing the plurality of game test cases through the game test version and the game historical version to obtain a test version execution result and a historical version execution result corresponding to each game test case; comparing the test version execution result with the historical version execution result to obtain a plurality of abnormal test cases; based on the difference characteristics of each abnormal test case, performing clustering operation to obtain a plurality of clustering results; calculating the case complexity of each abnormal test case based on a plurality of case complexity associated factors; and based on the case complexity of each abnormal test case, screening a target abnormal test case from each clustering result and sending the target abnormal test case. According to the method, the game abnormal problem identification efficiency can be improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of computer, in particular to a game abnormal problem identification method and device, electronic equipment and computer readable storage medium. BACKGROUND

[0002] In the process of game version iteration, it is necessary to test the game test version, identify the corresponding game abnormal problems, adjust the game test version, and realize the stable operation of the game test version.

[0003] At present, the identification of game abnormal problems is performed by experienced planners and testers to search, filter and read the difference data of a large number of game use cases, and to determine the game abnormal problems that need to be paid attention to.

[0004] However, the multiplayer online battle arena game has high complexity, the difference data of game use case execution has large magnitude, and the game abnormal problems corresponding to the difference data of game use case execution have many repetitive redundancies, and there are also some individual game abnormal problems that are not easy to detect, so it takes a lot of time to filter the game abnormal problems that need to be paid attention to from the difference data of game use case execution, and there is a risk of omission in the filtering process, resulting in low efficiency of game abnormal problem identification. SUMMARY

[0005] The embodiments of the present application provide a game abnormal problem identification method, device, electronic equipment and computer readable storage medium, which can improve the efficiency of game abnormal problem identification.

[0006] The embodiments of the present application provide a game abnormal problem identification method, which comprises:

[0007] Obtaining a game abnormal detection request, and constructing a plurality of game test cases based on the game abnormal detection request;

[0008] Executing the plurality of game test cases through a game test version to obtain a test version execution result corresponding to each game test case, and executing the plurality of game test cases through a game history version to obtain a history version execution result corresponding to each game test case;

[0009] Comparing the test version execution result and the history version execution result corresponding to the same game test case to obtain a plurality of abnormal test cases with differences between the test version execution result and the history version execution result;

[0010] Based on one or more difference features corresponding to each abnormal test case, performing clustering operation on the plurality of abnormal test cases to obtain a plurality of clustering results;

[0011] calculate a use case complexity corresponding to each of the abnormal test cases based on a plurality of use case complexity correlation factors;

[0012] filter a target abnormal test case from each of the clustering results based on the use case complexity corresponding to each of the abnormal test cases, and send the target abnormal test case, so that a staff member identifies a game abnormal problem based on the target abnormal test case.

[0013] Correspondingly, an embodiment of the present application provides a game abnormal problem identification device, comprising:

[0014] An acquisition unit is configured to acquire a game abnormality detection request and construct a plurality of game test cases based on the game abnormality detection request;

[0015] An execution unit is configured to execute the plurality of game test cases through a game test version to obtain a test version execution result corresponding to each of the game test cases, and execute the plurality of game test cases through a game history version to obtain a history version execution result corresponding to each of the game test cases;

[0016] A comparison unit is configured to compare the test version execution result and the history version execution result corresponding to a same game test case to obtain a plurality of abnormal test cases in which there is a difference between the test version execution result and the history version execution result;

[0017] A clustering unit is configured to perform a clustering operation on the plurality of abnormal test cases based on one or more difference features corresponding to each of the abnormal test cases to obtain a plurality of clustering results;

[0018] A calculation unit is configured to calculate a use case complexity corresponding to each of the abnormal test cases based on a plurality of use case complexity correlation factors;

[0019] A filtering unit is configured to filter a target abnormal test case from each of the clustering results based on the use case complexity corresponding to each of the abnormal test cases, and send the target abnormal test case, so that a staff member identifies a game abnormal problem based on the target abnormal test case.

[0020] In some embodiments, the clustering unit can be specifically configured to perform a deduplication operation on the abnormal test cases with the same difference features based on one or more difference features corresponding to each of the abnormal test cases to obtain deduplicated abnormal test cases, determine a difference feature set corresponding to each of the deduplicated abnormal test cases, and perform a clustering operation on the plurality of deduplicated abnormal test cases by a similarity degree between different difference feature sets to obtain a plurality of clustering results.

[0021] In some embodiments, the clustering unit can be specifically configured to determine one or more core abnormal test cases from the plurality of deduplicated abnormal test cases according to the similarity between different sets of difference features; and perform a clustering operation on the plurality of deduplicated abnormal test cases to obtain a plurality of clustering results, taking each of the core abnormal test cases as a core of a cluster.

[0022] In some embodiments, the clustering unit can be specifically configured to determine a current abnormal test case from the plurality of deduplicated abnormal test cases; obtain a current similarity between a set of difference features corresponding to each of the deduplicated abnormal test cases and a set of difference features corresponding to the current abnormal test case; if the number of deduplicated abnormal test cases with a current similarity less than or equal to a preset similarity exceeds a preset number, determine the current abnormal test case as a core abnormal test case, and label the core abnormal test case with a clustering label; and return to the step of determining a current abnormal test case from the plurality of deduplicated abnormal test cases until all the deduplicated abnormal test cases are traversed.

[0023] In some embodiments, the clustering unit can be specifically configured to determine, as a neighbor abnormal test case corresponding to the core abnormal test case, a deduplicated abnormal test case with a similarity less than or equal to a preset similarity between sets of difference features corresponding to the core abnormal test case; and label the neighbor abnormal test case with the same clustering label as the core abnormal test case.

[0024] In some embodiments, the computing unit can be specifically configured to normalize each of the case complexity correlation factors of the plurality of abnormal test cases to obtain normalized case complexity correlation factors; obtain a target weight of each of the normalized case complexity correlation factors; and calculate a case complexity corresponding to each of the abnormal test cases based on the normalized case complexity correlation factors and the target weight.

[0025] In some embodiments, the computing unit can be specifically configured to obtain an initial weight of each of the normalized case complexity correlation factors; and adjust the initial weight based on the number of occurrences of correlation factor specific information and the total amount of data of the case complexity correlation factors to obtain a target weight.

[0026] In some embodiments, the screening unit can be specifically configured to compare the case complexity corresponding to each of the abnormal test cases in each of the clustering results based on the case complexity to obtain an abnormal test case with the lowest case complexity in the clustering result; and take the abnormal test case with the lowest case complexity in the clustering result as a target abnormal test case.

[0027] In addition, the embodiment of the present application further provides an electronic device, comprising a processor and a memory, the memory stores an application program, and the processor is used to run the application program in the memory to realize the game abnormal problem identification method provided by the embodiment of the present application.

[0028] In addition, the embodiment of the present application further provides a computer readable storage medium, which stores a plurality of instructions, and the instructions are suitable for being loaded by a processor to execute the steps in any one of the game abnormal problem identification methods provided by the embodiment of the present application.

[0029] In addition, the embodiment of the present application further provides a computer program product, which comprises a computer program or instructions, and the computer program or instructions are executed by a processor to realize the steps in the game abnormal problem identification method provided by the embodiment of the present application.

[0030] The embodiment of the present application can obtain a game abnormal detection request, and construct a plurality of game test cases based on the game abnormal detection request; execute the plurality of game test cases through a game test version and a game history version respectively to obtain a test version execution result and a history version execution result corresponding to each game test case; compare the test version execution result and the history version execution result to obtain a plurality of abnormal test cases; calculate a test case complexity of each abnormal test case based on a plurality of test case complexity correlation factors; and select a target abnormal test case from each clustering result based on the test case complexity of each abnormal test case and send the target abnormal test case. Since the abnormal test cases are classified and clustered according to the difference characteristics of each abnormal test case and the game abnormal problem, a plurality of clustering results including a plurality of abnormal test cases are obtained, a large number of repeated and redundant abnormal test cases are simplified and classified, then the test case complexity of each abnormal test case is calculated according to a plurality of test case complexity correlation factors, and a target abnormal test case with the lowest test case complexity is selected from each clustering result according to the test case complexity of each abnormal test case and sent, which can reduce the complexity of the abnormal test cases to be identified as much as possible and reduce the complexity of the game abnormal problem identification, and thus the efficiency of the game abnormal problem identification can be improved. BRIEF DESCRIPTION OF DRAWINGS

[0031] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative effort on the basis of these drawings.

[0032] Figure 1is a scene schematic diagram of a game abnormal problem identification method provided by an embodiment of the application;

[0033] Figure 2-a is a first flow schematic diagram of a game abnormal problem identification method provided by an embodiment of the application;

[0034] Figure 2-b is a difference feature comparison diagram of an abnormal test case provided by an embodiment of the application;

[0035] Figure 2-c is a use case complexity correlation factor diagram provided by an embodiment of the application;

[0036] Figure 2-d is a second flow schematic diagram of a game abnormal problem identification method provided by an embodiment of the application;

[0037] Figure 2-e is a third flow schematic diagram of a game abnormal problem identification method provided by an embodiment of the application;

[0038] Figure 3 is a fourth flow schematic diagram of a game abnormal problem identification method provided by an embodiment of the application;

[0039] Figure 4 is a structure schematic diagram of a game abnormal problem identification apparatus provided by an embodiment of the application;

[0040] Figure 5 is a structure schematic diagram of an electronic device provided by an embodiment of the application. DETAILED DESCRIPTION

[0041] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the present application.

[0042] The embodiments of the present application provide a game abnormal problem identification method and related devices, which can include a game abnormal problem identification apparatus, an electronic device, a computer readable storage medium and a computer program product. The game abnormal problem identification apparatus can be specifically integrated in an electronic device, which can be a terminal or a server or the like.

[0043] It can be understood that the game abnormal problem identification method of the present embodiment can be executed on a terminal, or executed on a server, or executed by the terminal and the server together. The above examples should not be understood as limiting the present application.

[0044] The server can be a stand-alone physical server, a server cluster composed of multiple physical servers, or a distributed system, and can also be a cloud server providing cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery network (CDN), and basic cloud computing services such as big data and artificial intelligence platforms. The terminal can be a smart phone, a tablet computer, a notebook computer, a desktop computer, a smart speaker, a smart watch, and the like, but is not limited thereto. The terminal and the server can be directly or indirectly connected through wired or wireless communication, which is not limited in the present application.

[0045] For example, with reference to Figure 1 , Figure 1 is a scene diagram of a game abnormal problem identification method provided by an embodiment of the present application, as shown in Figure 1 , for example, the game abnormal problem identification device is integrated in an electronic device, the electronic device can be used to obtain a game abnormal detection request, and construct a plurality of game test cases based on the game abnormal detection request; the plurality of game test cases are respectively executed through a game test version and a game history version, to obtain a test version execution result and a history version execution result corresponding to each game test case; the test version execution result and the history version execution result are compared, to obtain a plurality of abnormal test cases; based on the difference features of each abnormal test case, clustering operation is performed, to obtain a plurality of clustering results; based on a plurality of case complexity correlation factors, the case complexity of each abnormal test case is calculated; based on the case complexity of each abnormal test case, a target abnormal test case is selected from each clustering result and sent, thereby improving the efficiency of game abnormal problem identification.

[0046] The following will be described in detail. It should be noted that the order of the following embodiments is not limited as the preferred order of the embodiments.

[0047] This embodiment will be described from the perspective of a game abnormal problem identification device, which can be integrated in an electronic device, which can be a server or a terminal, and the like.

[0048] Please refer to Figure 2-a , Figure 2-a is a first flow diagram of a game abnormal problem identification method provided by an embodiment of the present application, as shown in Figure 2-a , the specific process of the game abnormal problem identification method provided by the present application is as follows:

[0049] 201, a game abnormal detection request is obtained, and a plurality of game test cases are constructed based on the game abnormal detection request.

[0050] In the context of game testing, test cases refer to the comprehensive descriptions of the testing tasks performed on game software, reflecting the test plan, methods, techniques, strategies, and anticipated problems. Game test cases include test objectives, test environment, input data, test steps, expected results, test scripts, etc., ultimately forming a test document. Simply put, a game test case is a set of test inputs, execution conditions, and expected results compiled for a specific goal, used to verify whether a program's functionality meets expected requirements.

[0051] For example, when a multiplayer online tactical competitive game needs to be updated, this application can obtain a game anomaly detection request for the game test version of the game to be updated, and then construct multiple game test cases based on the game anomaly detection request through various methods such as manual construction, automated traversal, and AI exploration.

[0052] Among them, multiplayer online battle arena (MOBA) games typically require players to purchase equipment during battles, and players are usually divided into two teams. Different players on each team control different characters (heroes) and compete against each other on a scattered game map to try to win the game.

[0053] 202. Execute multiple game test cases using the game test version to obtain the execution results of the test version corresponding to each game test case, and execute multiple game test cases using the game historical version to obtain the execution results of the historical version corresponding to each game test case.

[0054] Among them, the game test version is the game version that needs to be tested after the game has been updated, while the game history version is the game version that is already running stably and has no abnormal issues.

[0055] For example, after obtaining the corresponding game test cases for a game test request, the same game test cases are executed using both the game test version and the game historical version. Then, the execution results of the test version obtained by executing multiple game test cases using the game test version and the execution results of the historical version obtained by executing multiple game test cases using the game historical version are obtained, until all game test cases are executed.

[0056] 203. Compare the execution results of the test version corresponding to the same game test case with the execution results of the historical version to identify multiple abnormal test cases where there are differences between the execution results of the test version and the execution results of the historical version.

[0057] Among them, abnormal test cases are game test cases in which the execution results differ after execution of the game test version and the game history version.

[0058] For example, after obtaining the test version execution result and the historical version execution result of all game test cases, the test version execution result and the historical version execution result corresponding to the same game test case are compared to determine a plurality of game test cases with differences between the test version execution result and the historical version execution result, and the plurality of game test cases are taken as abnormal test cases.

[0059] 204、Based on one or more difference features corresponding to each abnormal test case, a clustering operation is performed on the plurality of abnormal test cases to obtain a plurality of clustering results.

[0060] The features can be attribute features of heroes, and the difference features are features with differences between the test version execution process and the historical version execution process of the test case, which can be attribute features such as attack power of a hero or blood volume of a hero.

[0061] The abnormal test case includes a plurality of features, and the plurality of features include one or more difference features.

[0062] Referring to Figure 2-b , Figure 2-b is a difference feature comparison chart of an abnormal test case provided by an embodiment of the present application, as shown in Figure 2-b The difference feature value of the abnormal test case can be camp 1- hero 1- physical attack, which changes with time during the execution of the test case, and is different between the execution of the abnormal test case in the game test version and the execution of the abnormal test case in the game historical version.

[0063] For example, after a large number of game test cases are executed, the difference features of the abnormal test cases generated therefrom exist in a large number of repetitions and redundancies. For example, the attack power of the hero Lu Ban No. 7 in the game of King Glory appears to be abnormal, and then all game test cases involving Lu Ban No. 7 are determined to be abnormal test cases. The difference features of these abnormal test cases all have the attack power of Lu Ban No. 7, resulting in a large number of repetitions and redundancies of the difference features. In order to simplify the abnormal test cases to be positioned, a clustering operation can be performed on the plurality of abnormal test cases based on one or more difference features corresponding to each abnormal test case to obtain a plurality of clustering results.

[0064] Optionally, in an embodiment, the step of “based on one or more difference features corresponding to each abnormal test case, performing a clustering operation on the plurality of abnormal test cases to obtain a plurality of clustering results” includes:

[0065] Based on one or more difference features corresponding to each abnormal test case, a deduplication operation is performed on abnormal test cases with the same difference features to obtain deduplicated abnormal test cases.

[0066] determine a difference feature set corresponding to each of the de-duplicated abnormal test cases;

[0067] perform a clustering operation on the plurality of de-duplicated abnormal test cases according to the similarity between different difference feature sets, to obtain a plurality of clustering results.

[0068] Each of the difference feature sets includes all difference features of the corresponding de-duplicated abnormal test case, and each element in the difference feature set represents a separate difference feature name.

[0069] The similarity between different difference feature sets can be a Jaccard similarity, and a specific calculation formula of the Jaccard similarity is as follows:

[0070]

[0071] Wherein, A and B are sets to be calculated for similarity, |A∩B| represents the number of elements of the intersection of A and B, |A∪B| represents the number of elements of the union of A and B, and J(A, B) is the Jaccard similarity between sets A and B.

[0072] The Jaccard similarity is a commonly used measurement method for measuring the similarity between sample sets. Given two sets A and B, the Jaccard similarity can be obtained by calculating the ratio of their intersection and union. It should be noted that the calculation result of the Jaccard similarity ranges from 0 to 1, and the value closer to 1 indicates that the similarity between the sets is higher, and the value closer to 0 indicates that the similarity between the sets is lower.

[0073] The clustering operation on the plurality of de-duplicated abnormal test cases can be further clustering of the de-duplicated abnormal test cases by using a density-based clustering algorithm strategy.

[0074] The density-based clustering algorithm is different from the partition and hierarchical clustering method, which defines a cluster as the largest set of density-connected points, can divide areas with sufficiently high density into clusters, and can find clusters of arbitrary shape in a noisy spatial database.

[0075] For example, by comparing the difference feature sets corresponding to each abnormal test case, de-duplicating abnormal test cases with completely identical difference features, and retaining only one of the abnormal test cases with completely identical difference features as a de-duplicated abnormal test case, abnormal test cases with the same game abnormal root cause are clustered by the similarity between different difference feature sets of different de-duplicated abnormal test cases, to obtain a plurality of clustering results. The relationship between the difference feature set and the difference feature is as follows:

[0076] Case i= {key0, key1, key2, key3, …}

[0077] wherein Case i represents a difference feature set, and key0, key1, key2, key3, … represent difference feature names corresponding to the difference features.

[0078] Optionally, in an embodiment, the step of “performing clustering operation on the multiple deduplicated abnormal test cases according to the similarity between different difference feature sets to obtain multiple clustering results” comprises:

[0079] determining one or more core abnormal test cases from the multiple deduplicated abnormal test cases according to the similarity between different difference feature sets;

[0080] performing clustering operation on the multiple deduplicated abnormal test cases with each core abnormal test case as a core of a cluster to obtain multiple clustering results.

[0081] Each of the multiple clustering results comprises multiple deduplicated abnormal test cases with the same game abnormal root cause.

[0082] For example, as the test case scenarios of the multiplayer online battle arena game become more complex, many different difference features may be generated for the same game abnormal problem, which may result in a large number of deduplicated abnormal test cases even after deduplication according to the difference features, and many of the deduplicated abnormal test cases are caused by the same game abnormal problem. Therefore, after deduplication of the abnormal test cases according to the difference features, one or more core abnormal test cases are determined from the multiple deduplicated abnormal test cases according to the similarity between different difference feature sets, and then clustering operation is performed on the other multiple deduplicated abnormal test cases with each core abnormal test case as a core of a cluster to obtain multiple clustering results with the same game abnormal root cause.

[0083] Optionally, in an embodiment, the step of “determining one or more core abnormal test cases from the multiple deduplicated abnormal test cases according to the similarity between different difference feature sets” comprises:

[0084] determining a current abnormal test case from the multiple deduplicated abnormal test cases;

[0085] obtaining a current similarity between a difference feature set corresponding to each deduplicated abnormal test case and a difference feature set corresponding to the current abnormal test case;

[0086] If the number of the de-duplicated abnormal test cases with the current similarity degree less than or equal to the preset similarity degree exceeds the preset number, the current abnormal test case is determined as a core abnormal test case, and a clustering label is marked for the core abnormal test case.

[0087] Returning to the step of determining the current abnormal test case from the plurality of de-duplicated abnormal test cases, until all the de-duplicated abnormal test cases are traversed.

[0088] For example, one current abnormal test case is determined from the plurality of de-duplicated abnormal test cases, and the current similarity degree between the difference feature set corresponding to each de-duplicated abnormal test case and the difference feature set corresponding to the current abnormal test case is obtained. When the preset similarity degree is r and the preset number is M, if the number of the de-duplicated abnormal test cases with the current similarity degree less than or equal to r exceeds M, the current abnormal test case is determined as a core abnormal test case, and one clustering label is marked for the core abnormal test case. If the number of the de-duplicated abnormal test cases with the current similarity degree less than or equal to r does not exceed M, the current abnormal test case is determined as a noise case, and then the step of determining the current abnormal test case from the plurality of de-duplicated abnormal test cases is executed again until all the de-duplicated abnormal test cases are traversed, to obtain a plurality of core abnormal test cases and noise cases marked with different clustering labels.

[0089] Optionally, in an embodiment, the step of "performing a clustering operation on the plurality of de-duplicated abnormal test cases with each core abnormal test case as a core of one cluster to obtain a plurality of clustering results" includes:

[0090] The de-duplicated abnormal test case with the similarity degree between the difference feature sets corresponding to the core abnormal test case less than or equal to the preset similarity degree is determined as a neighbor abnormal test case corresponding to the core abnormal test case.

[0091] The neighbor abnormal test case is marked with the same clustering label as the core abnormal test case.

[0092] Each clustering result includes one core abnormal test case and a plurality of neighbor abnormal test cases with the same clustering label.

[0093] For example, after obtaining a plurality of core abnormal test cases marked with different clustering labels, the de-duplicated abnormal test case with the similarity degree between the difference feature sets corresponding to the core abnormal test case less than or equal to the preset similarity degree is determined as a neighbor abnormal test case corresponding to the core abnormal test case, and then the neighbor abnormal test case is marked with the same clustering label as the core abnormal test case, to obtain a plurality of clustering results including one core abnormal test case and a plurality of neighbor abnormal test cases with the same clustering label.

[0094] 205、based on multiple use case complexity correlation factors, calculate the use case complexity corresponding to each abnormal test case.

[0095] Wherein, the use case complexity correlation factors are more, and with the further improvement of the use case scene, the use case complexity correlation factors will also continue to increase. Referring to Figure 2-c , Figure 2-c is the use case complexity correlation factor provided by the embodiment of the application, as shown in Figure 2-c The use case complexity correlation factors of the use case include heroes, equipment and interaction, etc. The use case complexity correlation factors are different, and the complexity of the use case is also different. For example, with the increase of the number of heroes, the use case complexity increases; with the increase of the number of equipment, the use case complexity also increases; with the increase of the equipment characteristics (such as special effect equipment such as golden body and resurrection armor), the use case complexity also increases; with the increase of the number of skill interaction, the use case complexity also increases.

[0096] For example, a clustering result contains different abnormal test cases with different use case complexity correlation factors, and the complexity may be quite different. It is necessary to find the simplest abnormal test case to analyze the problem, so as to further reduce the cost of game abnormal problem identification. According to multiple use case complexity correlation factors of each abnormal test case, the embodiment of the application calculates the use case complexity corresponding to each abnormal test case, and provides data support for subsequent game abnormal problem identification.

[0097] Optionally, in an embodiment, the step of "based on multiple use case complexity correlation factors, calculating the use case complexity corresponding to each abnormal test case" comprises:

[0098] Normalizing each use case complexity correlation factor of the multiple abnormal test cases to obtain a normalized use case complexity correlation factor;

[0099] Obtain the target weight of each normalized use case complexity correlation factor;

[0100] Based on the normalized use case complexity correlation factor and the target weight, the use case complexity corresponding to each abnormal test case is calculated.

[0101] Wherein, the formula for obtaining the normalized use case complexity correlation factor by normalizing each use case complexity correlation factor of the multiple abnormal test cases is as follows:

[0102]

[0103] Wherein, x i ′ is the normalized use case complexity correlation factor, x iis the use case complexity correlation factor, min(x) is the minimum value of the use case complexity correlation factor, and max(x) is the maximum value of the use case complexity correlation factor.

[0104] The formula of the use case complexity correlation factor can effectively reduce the influence of the long-tail distribution data after logarithmic processing, for example, the use case with the most interactions is 1000 times, but most use cases have only 1-10 interactions. If direct normalization processing is performed through the maximum value, most data scores of the factor tend to 0, and the discrimination cannot be obviously reflected. After logarithmic processing, the denominator part of the formula sharply decreases, x i The value of the use case complexity correlation factor affects the value of the normalized use case complexity correlation factor. i ′ The discrimination can be obviously reflected.

[0105] For example, the data of each use case complexity correlation factor is different. In order to combine and calculate different types of correlation factors, the normalized use case complexity correlation factor is obtained by normalizing each use case complexity correlation factor of the multiple abnormal test use cases. Then, the target weight of each normalized use case complexity correlation factor is obtained. Finally, the use case complexity corresponding to each abnormal test use case is calculated according to the normalized use case complexity correlation factor and the target weight.

[0106] The formula of the use case complexity corresponding to each abnormal test use case calculated by the embodiment of the application is as follows:

[0107]

[0108] Wherein, r is the use case complexity of the abnormal test use case, a1, a2, a3, …, a n are the target weights of the correlation factors of the abnormal test use case, x1 ′ , x2 ′ , x ′ 3, …, x ′ n are the normalized use case complexity correlation factors of the abnormal test use case.

[0109] Optionally, in an embodiment, the step of "obtaining the target weight of each normalized use case complexity correlation factor" comprises:

[0110] obtaining the initial weight of each normalized use case complexity correlation factor;

[0111] adjusting the initial weight based on the number of times that the correlation factor specific information appears and the total amount of data of the use case complexity correlation factor to obtain the target weight.

[0112] For example, if the normalized case complexity correlation factors of multiple abnormal test cases are completely consistent, the initial weights of the normalized case complexity correlation factors are the same, the calculated case complexity of the multiple abnormal test cases is the same, if the elements of the case complexity factors of the de-duplicated difference cases are as consistent as possible, that is, the number of occurrences of the correlation factor specific information of the abnormal test cases and the total amount of data of the case complexity correlation factors are close, for example, the hero is always a certain specific unchanged hero or the equipment is always a certain specific unchanged equipment, the complexity of the game abnormal problem identification will be further reduced, therefore, after obtaining the initial weight of each normalized case complexity correlation factor, the initial weight of some case complexity correlation factors can be further intervened according to the number of occurrences of the correlation factor specific information and the total amount of data of the case complexity correlation factors, to obtain a target weight, so as to reflect more general difference characteristics as much as possible.

[0113] The formula for adjusting the initial weight to obtain the target weight is as follows:

[0114]

[0115] Wherein, n is the number of occurrences of the correlation factor specific information, N is the total amount of data of the case complexity correlation factors, a i ′ is the target weight, a i is the initial weight.

[0116] 206、Based on the case complexity corresponding to each abnormal test case, a target abnormal test case is selected from each clustering result, and the target abnormal test case is sent, so that the staff identifies the game abnormal problem based on the target abnormal test case.

[0117] For example, after determining the case complexity of each de-duplicated abnormal test case in each clustering result, in order to reduce the workload of game abnormal problem identification, a target abnormal test case is selected from each clustering result according to the case complexity corresponding to each abnormal test case, and the target abnormal test case is sent, so that the staff identifies the game abnormal problem based on the target abnormal test case.

[0118] Optionally, in an embodiment, the step of "selecting a target abnormal test case from each clustering result based on the case complexity corresponding to each abnormal test case, and sending the target abnormal test case" comprises:

[0119] Comparing the case complexity corresponding to each abnormal test case in each clustering result to obtain the abnormal test case with the lowest case complexity in the clustering result;

[0120] The abnormal test case with the lowest case complexity in the clustering result is taken as the target abnormal test case.

[0121] For example, in order to find as many abnormal test cases with lower complexity as possible and reduce the complexity of game abnormal problem identification, the multiple abnormal test cases in each clustering result are sorted according to the case complexity corresponding to the multiple abnormal test cases in each clustering result, the abnormal test case with the lowest case complexity in the clustering result is obtained, and the abnormal test case with the lowest case complexity in the clustering result is sent as a target abnormal test case so that the staff can identify game abnormal problems based on the target abnormal test case.

[0122] In a possible embodiment, after obtaining the abnormal test case with the lowest case complexity in the clustering result, the abnormal test case with the lowest case complexity in the clustering result and the noise case are sent as target abnormal test cases so that the staff can identify game abnormal problems based on the target abnormal test cases.

[0123] Referring to Figure 2-d , Figure 2-d is a second flowchart of a game abnormal problem identification method provided by an embodiment of the present application, as shown in Figure 2-d The technical solution provided by the present application mainly serves game planners and game testers, and helps relevant users accurately identify existing game abnormal problems in a large amount of use case execution data of a multiplayer online battle arena game. First, a game abnormal detection request is initiated by the user side, and based on the game abnormal detection request, multiple game test cases are constructed in various ways such as manual construction, automatic traversal, and AI exploration. Then, the service side triggers the execution of the test cases, executes the multiple game test cases through a game test version, obtains the test version execution result corresponding to each game test case, and at the same time, executes the multiple game test cases through a game historical version, obtains the historical version execution result corresponding to each game test case, and compares the test version execution result and the historical version execution result corresponding to the same game test case, obtains the case execution difference, obtains multiple abnormal test cases with differences between the test version execution result and the historical version execution result, and then automatically identifies game abnormal problems for these abnormal test cases, quickly filters and simplifies to obtain all different types of abnormal case data, obtains different types of game abnormal problems, and finally the user side identifies these game abnormal problems.

[0124] Referring to Figure 2-e , Figure 2-e is a third flowchart of a game abnormal problem identification method provided by an embodiment of the present application, as shown in Figure 2-eAs shown, when the game abnormal problem automation identification is performed, the identification process is mainly implemented by two modules of typical problem classification and use case complexity scoring. First, according to one or more difference features corresponding to each abnormal test case, the abnormal test cases with the same difference features are subjected to a de-duplication operation to obtain de-duplicated abnormal test cases. Then, a density clustering algorithm is used to cluster the multiple de-duplicated abnormal test cases to obtain multiple clustering results with different game abnormal root causes. Finally, the use case complexity corresponding to the multiple abnormal test cases in each clustering result is obtained, the abnormal test case with the lowest use case complexity in each clustering result is obtained, and the abnormal test case with the lowest use case complexity in each clustering result is taken as a target abnormal test case to facilitate the staff to identify the game abnormal problem.

[0125] Through the game abnormal problem automation identification, the abnormal test cases of all different game abnormal root causes are screened and simplified as much as possible, which helps the relevant personnel to accurately identify the game problems that need to be paid attention to in the difference data of a large number of game test cases, greatly improves the efficiency, and greatly reduces the risk of missing the inspection.

[0126] As can be seen from the above, the embodiments of the present application can obtain a game abnormal detection request, and construct multiple game test cases based on the game abnormal detection request. The multiple game test cases are executed by a game test version and a game history version respectively to obtain a test version execution result and a history version execution result corresponding to each game test case. The test version execution result and the history version execution result are compared to obtain multiple abnormal test cases. Based on the difference features of each abnormal test case, clustering operation is performed to obtain multiple clustering results. Based on multiple use case complexity correlation factors, the use case complexity of each abnormal test case is calculated. Based on the use case complexity of each abnormal test case, a target abnormal test case is selected from each clustering result and sent. Since the embodiments of the present application cluster and classify the abnormal test cases according to the difference features of each abnormal test case and the game abnormal problems, multiple clustering results including multiple abnormal test cases are obtained, a large number of repeated and redundant abnormal test cases are simplified and classified, then the use case complexity of each abnormal test case is calculated according to multiple use case complexity correlation factors, and a target abnormal test case with the lowest use case complexity is selected from each clustering result and sent, which can reduce the complexity of the abnormal test cases to be identified as much as possible, reduce the complexity of the game abnormal problem identification, and therefore improve the efficiency of the game abnormal problem identification.

[0127] The embodiments of the present application provide a game abnormal problem identification method, which refers to Figure 3 , Figure 3 is a fourth flowchart of the game abnormal problem identification method provided by the embodiments of the present application, as shown inFigure 3 As shown, the specific process of the game anomaly problem identification method can be as follows:

[0128] 301, the electronic equipment acquires a game anomaly detection request, and constructs a plurality of game test cases based on the game anomaly detection request.

[0129] 302, the electronic equipment executes a plurality of game test cases through a game test version, obtains a test version execution result corresponding to each game test case, and executes a plurality of game test cases through a game history version, obtains a history version execution result corresponding to each game test case.

[0130] For example, after obtaining the corresponding game test case for the game test request, the same game test case is executed through the game test version and the game history version respectively, and then the test version execution result obtained by executing a plurality of game test cases through the game test version and the history version execution result obtained by executing a plurality of game test cases through the game history version are obtained, until all game test cases are executed.

[0131] 303, the electronic equipment compares the test version execution result and the history version execution result corresponding to the same game test case, and obtains a plurality of abnormal test cases existing between the test version execution result and the history version execution result.

[0132] 304, the electronic equipment performs clustering operation on a plurality of abnormal test cases based on one or more difference characteristics corresponding to each abnormal test case, and obtains a plurality of clustering results.

[0133] For example, in a possible embodiment, the abnormal test cases with the same difference features are de-duplicated according to the difference features corresponding to each abnormal test case, only one of the abnormal test cases with the same difference features is reserved as a de-duplicated abnormal test case, then a current abnormal test case is determined from the plurality of de-duplicated abnormal test cases, and a current similarity degree between the difference feature set corresponding to each de-duplicated abnormal test case and the difference feature set corresponding to the current abnormal test case is obtained. When the preset similarity degree is r and the preset number is M, if the number of de-duplicated abnormal test cases with the current similarity degree less than or equal to r exceeds M, the current abnormal test case is determined as a core abnormal test case, a clustering label is marked for the core abnormal test case, and the de-duplicated abnormal test cases with a similarity degree less than or equal to the preset similarity degree with the difference feature set corresponding to the core abnormal test case are determined as neighbor abnormal test cases corresponding to the core abnormal test case. Then, the neighbor abnormal test cases are marked with the same clustering label as the core abnormal test case. Then, it is judged in turn whether there is a neighbor core abnormal test case in each neighbor abnormal test case corresponding to the core abnormal test case. If there is, the neighbor abnormal test case corresponding to the neighbor core abnormal test case is marked with the same clustering label as the core abnormal test case. Then, it is returned to execute the judgment in turn whether there is a neighbor core abnormal test case in each neighbor abnormal test case corresponding to the core abnormal test case, until the neighbor abnormal test case is not the neighbor core abnormal test case. A current abnormal test case is determined from the de-duplicated abnormal test cases that have not been marked, and it is returned to execute the acquisition of the current similarity degree between the difference feature set corresponding to each de-duplicated abnormal test case and the difference feature set corresponding to the current abnormal test case, until all the de-duplicated abnormal test cases are marked, and a plurality of clustering results marked with different clustering labels are obtained.

[0134] If the number of de-duplicated abnormal test cases with the current similarity degree less than or equal to r does not exceed M, the current abnormal test case is marked as a noise case, a current abnormal test case is determined from the plurality of de-duplicated abnormal test cases that have not been marked, and it is returned to execute the acquisition of the current similarity degree between the difference feature set corresponding to each de-duplicated abnormal test case and the difference feature set corresponding to the current abnormal test case, until all the de-duplicated test cases are marked.

[0135] The neighbor core abnormal test case is both the neighbor abnormal test case and the core abnormal test case.

[0136] 305. The electronic device calculates a use case complexity corresponding to each abnormal test case based on a plurality of use case complexity correlation factors.

[0137] For example, the embodiment of the application normalizes each use case complexity correlation factor of the plurality of abnormal test cases to obtain a normalized use case complexity correlation factor, then obtains an initial weight of the normalized use case complexity correlation factor, and further intervenes in the initial weight of some use case complexity correlation factors according to the number of occurrences of the specific information of the correlation factor and the data total amount of the use case complexity correlation factor to obtain a target weight, so as to reflect more universal difference characteristics as much as possible. Finally, the use case complexity corresponding to each abnormal test case is calculated according to the normalized use case complexity correlation factor and the target weight.

[0138] 306、Based on the use case complexity corresponding to each abnormal test case, a target abnormal test case is selected from each clustering result, and the target abnormal test case is sent so that the staff can identify the game abnormal problem based on the target abnormal test case.

[0139] For example, the plurality of abnormal test cases in each clustering result are sorted according to the use case complexity corresponding to each abnormal test case in the clustering result to obtain an abnormal test case with the lowest use case complexity in the clustering result, and the abnormal test case with the lowest use case complexity in the clustering result is taken as a target abnormal test case, and the target abnormal test case is sent so that the staff can identify the game abnormal problem based on the target abnormal test case.

[0140] As can be seen from the above, the electronic device of the embodiment can obtain a game abnormal detection request, and construct a plurality of game test cases based on the game abnormal detection request. The plurality of game test cases are respectively executed on a game test version and a game historical version to obtain a test version execution result and a historical version execution result corresponding to each game test case. The test version execution result and the historical version execution result are compared to obtain a plurality of abnormal test cases. Based on the difference characteristics of each abnormal test case, clustering operation is performed to obtain a plurality of clustering results. Based on a plurality of use case complexity correlation factors, the use case complexity of each abnormal test case is calculated. Based on the use case complexity of each abnormal test case, a target abnormal test case is selected from each clustering result and sent. Since the abnormal test cases are clustered and classified according to the difference characteristics of each abnormal test case and the game abnormal problem in the application, a plurality of clustering results including a plurality of abnormal test cases are obtained, a large number of repeated and redundant abnormal test cases are simplified and classified, then the use case complexity of each abnormal test case is calculated according to a plurality of use case complexity correlation factors, and a target abnormal test case with the lowest use case complexity is selected from each clustering result and sent, which can reduce the complexity of the abnormal test case to be identified as much as possible and reduce the complexity of the game abnormal problem identification, so that the efficiency of the game abnormal problem identification can be improved.

[0141] To better implement the above method, the embodiments of the present application further provide a game abnormal problem identification device, which can be integrated in an electronic device, which can be a server, a terminal or the like. The terminal can include a tablet computer, a notebook computer, a personal computer (PC), a wearable device, a virtual reality device or other smart devices that can identify game abnormal problems.

[0142] For example, as shown in Figure 4 The game abnormal problem identification device can include an acquisition unit 401, an execution unit 402, a comparison unit 403, a clustering unit 404, a calculation unit 405 and a screening unit 406, as follows:

[0143] (1) The acquisition unit 401;

[0144] The acquisition unit 401 is configured to acquire a game abnormality detection request and construct a plurality of game test cases based on the game abnormality detection request.

[0145] (2) The execution unit 402;

[0146] The execution unit 402 is configured to execute the plurality of game test cases through a game test version to obtain a test version execution result corresponding to each game test case, and execute the plurality of game test cases through a game history version to obtain a history version execution result corresponding to each game test case.

[0147] (3) The comparison unit 403;

[0148] The comparison unit 403 is configured to compare the test version execution result and the history version execution result corresponding to the same game test case to obtain a plurality of abnormal test cases with differences between the test version execution result and the history version execution result.

[0149] (4) The clustering unit 404;

[0150] The clustering unit 404 is configured to perform a clustering operation on the plurality of abnormal test cases based on one or more difference features corresponding to each abnormal test case to obtain a plurality of clustering results.

[0151] Optionally, in some embodiments of the present application, the clustering unit can include a deduplication subunit, a first determination subunit and a first clustering subunit, as follows:

[0152] The deduplication subunit is configured to perform a deduplication operation on abnormal test cases with the same difference features based on one or more difference features corresponding to each abnormal test case to obtain deduplicated abnormal test cases.

[0153] The first determining sub-unit is configured to determine a difference feature set corresponding to each of the de-duplicated abnormal test cases.

[0154] The first clustering sub-unit is configured to perform clustering operation on the plurality of de-duplicated abnormal test cases according to the similarity between different difference feature sets, to obtain a plurality of clustering results.

[0155] Optionally, in some embodiments of the present application, the first clustering sub-unit can include a second determining sub-unit and a second clustering sub-unit, as follows:

[0156] The second determining sub-unit is configured to determine one or more core abnormal test cases from the plurality of de-duplicated abnormal test cases according to the similarity between different difference feature sets.

[0157] The second clustering sub-unit is configured to perform clustering operation on the plurality of de-duplicated abnormal test cases by taking each core abnormal test case as a core of a cluster, to obtain a plurality of clustering results.

[0158] Optionally, in some embodiments of the present application, the second determining sub-unit can include a third determining sub-unit, a first obtaining sub-unit, a third determining sub-unit and a returning sub-unit, as follows:

[0159] The third determining sub-unit is configured to determine a current abnormal test case from the plurality of de-duplicated abnormal test cases.

[0160] The first obtaining sub-unit is configured to obtain a current similarity between a difference feature set corresponding to each of the de-duplicated abnormal test cases and a difference feature set corresponding to the current abnormal test case.

[0161] The third determining sub-unit is configured to determine the current abnormal test case as a core abnormal test case and label the core abnormal test case with a clustering label, if the number of de-duplicated abnormal test cases with a similarity less than or equal to a preset similarity is greater than a preset number.

[0162] The returning sub-unit is configured to return to performing the step of determining a current abnormal test case from the plurality of de-duplicated abnormal test cases, until all the de-duplicated abnormal test cases are traversed.

[0163] Optionally, in some embodiments of the present application, the second clustering sub-unit can include a fourth determining sub-unit and a labeling sub-unit, as follows:

[0164] The fourth determining sub-unit is configured to determine a de-duplicated abnormal test case with a similarity less than or equal to a preset similarity between difference feature sets corresponding to a core abnormal test case as a neighbor abnormal test case of the core abnormal test case.

[0165] The marking sub-unit is configured to mark the neighbor abnormal test case with the same cluster label as the core abnormal test case.

[0166] (5) The computing unit 405;

[0167] The computing unit 405 is configured to calculate the use case complexity corresponding to each abnormal test case based on the plurality of use case complexity correlation factors.

[0168] Optionally, in some embodiments of the present application, the computing unit can include a second obtaining sub-unit and a computing sub-unit, as follows:

[0169] The second obtaining sub-unit is configured to obtain the target weight of each normalized use case complexity correlation factor.

[0170] The computing sub-unit is configured to calculate the use case complexity corresponding to each abnormal test case based on the normalized use case complexity correlation factor and the target weight.

[0171] Optionally, in some embodiments of the present application, the second obtaining sub-unit can include a third obtaining sub-unit and an adjusting sub-unit, as follows:

[0172] The third obtaining sub-unit is configured to obtain the initial weight of each normalized use case complexity correlation factor.

[0173] The adjusting sub-unit is configured to adjust the initial weight based on the number of times the correlation factor specific information appears and the total amount of data of the use case complexity correlation factor to obtain the target weight.

[0174] (6) The screening unit 406;

[0175] The screening unit 406 is configured to screen a target abnormal test case from each cluster result based on the use case complexity corresponding to each abnormal test case, and send the target abnormal test case, so that the staff can identify the game abnormal problem based on the target abnormal test case.

[0176] Optionally, in some embodiments of the present application, the screening unit can include a third obtaining sub-unit and an adjusting sub-unit, as follows:

[0177] The comparison sub-unit is configured to compare the use case complexity corresponding to the plurality of abnormal test cases in each cluster result to obtain the abnormal test case with the lowest use case complexity in the cluster result.

[0178] The fifth determining sub-unit is configured to take the abnormal test case with the lowest use case complexity in the cluster result as the target abnormal test case.

[0179] From the above, the embodiment can obtain the game anomaly detection request by the acquisition unit 401, and construct a plurality of game test cases based on the game anomaly detection request; through the execution unit 402, a plurality of game test cases are executed through the game test version, the test version execution result corresponding to each game test case is obtained, and a plurality of game test cases are executed through the game history version, the history version execution result corresponding to each game test case is obtained; through the comparison unit 403, the test version execution result and the history version execution result corresponding to the same game test case are compared, a plurality of anomaly test cases existing between the test version execution result and the history version execution result are obtained; through the clustering unit 404, based on one or more difference features corresponding to each anomaly test case, a plurality of anomaly test cases are clustered to obtain a plurality of clustering results; through the calculation unit 405, based on a plurality of case complexity correlation factors, the case complexity corresponding to each anomaly test case is calculated; through the screening unit 406, based on the case complexity corresponding to each anomaly test case, a target anomaly test case is screened from each clustering result, and the target anomaly test case is sent, so that the staff identifies the game anomaly problem based on the target anomaly test case. Since the anomaly test case is clustered and classified according to the difference features of each anomaly test case and the game anomaly problem in the present application, a plurality of clustering results including a plurality of anomaly test cases are obtained, a large number of repeated and redundant anomaly test cases are simplified and classified, then the case complexity of each anomaly test case is calculated according to a plurality of case complexity correlation factors, and a target anomaly test case with the lowest case complexity is screened from each clustering result according to the case complexity of each anomaly test case, and is sent, which can reduce the complexity of the anomaly test case to be identified as much as possible, and reduce the complexity of the game anomaly problem identification, so that the efficiency of the game anomaly problem identification can be improved.

[0180] The embodiment of the present application also provides an electronic device, as shown in the figure, which shows the structural schematic diagram of the electronic device related to the embodiment of the present application, in particular: Figure 5

[0181] The electronic device can include a processor 501 with one or more processing cores, a memory 502 with one or more computer readable storage media, a power supply 503, and an input unit 504, and the like. Those skilled in the art can understand that the structure of the electronic device shown in the figure does not constitute a limitation on the electronic device, and can include more or fewer components than the figure, or combine certain components, or different component arrangements. Among them: Figure 5

[0182] ​​The processor 501 is the control center of the electronic device, connects all parts of the electronic device through various interfaces and lines, and performs various functions of the electronic device and processes data by running or executing software programs and / or modules stored in the memory 502 and calling data stored in the memory 502. Optionally, the processor 501 can include one or more processing cores; preferably, the processor 501 can integrate an application processor and a modem processor, wherein the application processor mainly processes the operating system, user interface, and application program, etc., and the modem processor mainly processes wireless communication. It can be understood that the above-mentioned modem processor can also not be integrated into the processor 501.

[0183] The memory 502 can be used to store software programs and modules, and the processor 501 executes various functions and data processing by running the software programs and modules stored in the memory 502. The memory 502 can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application program required by a function (such as a sound playing function, an image playing function, etc.), etc.; the data storage area can store data created according to the use of the electronic device, etc. In addition, the memory 502 can include a high-speed random access memory, and can also include a non-volatile memory, such as at least one magnetic disk storage device, a flash memory device, or other volatile solid-state memory device. Accordingly, the memory 502 can also include a memory controller to provide access for the processor 501 to the memory 502.

[0184] The electronic device also includes a power supply 503 for supplying power to various components, and preferably the power supply 503 can be logically connected to the processor 501 through a power management system, so as to realize functions such as management of charging, discharging, and power consumption management through the power management system. The power supply 503 can also include one or more than one direct current or alternating current power supply, a recharging system, a power failure detection circuit, a power converter or inverter, a power state indicator, etc. Any component.

[0185] The electronic device can also include an input unit 504, which can be used to receive input digital or character information, and generate keyboard, mouse, joystick, optical or trackball signal inputs related to user settings and function control.

[0186] Although not shown, the electronic device can also include a display unit, etc., which will not be described here. Specifically, in the present embodiment, the processor 501 in the electronic device will load the executable file corresponding to the process of one or more than one application program into the memory 502 according to the following instructions, and run the application program stored in the memory 502 by the processor 501, so as to realize various functions, as follows:

[0187] The game exception detection request is acquired, and a plurality of game test cases are constructed based on the game exception detection request; the plurality of game test cases are executed through a game test version and a game history version respectively to obtain a test version execution result and a history version execution result corresponding to each game test case; the test version execution result and the history version execution result are compared to obtain a plurality of exception test cases; clustering is performed based on a difference feature of each exception test case to obtain a plurality of clustering results; a use case complexity of each exception test case is calculated based on a plurality of use case complexity correlation factors; and a target exception test case is selected from each clustering result based on the use case complexity of each exception test case and is sent.

[0188] For example, the electronic device acquires a game exception detection request, and a plurality of game test cases are constructed based on the game exception detection request; the plurality of game test cases are executed through a game test version to obtain a test version execution result corresponding to each game test case, and the plurality of game test cases are executed through a game history version to obtain a history version execution result corresponding to each game test case; the test version execution result and the history version execution result corresponding to the same game test case are compared to obtain a plurality of exception test cases in which there is a difference between the test version execution result and the history version execution result; a plurality of exception test cases are clustered based on one or more difference features corresponding to each exception test case to obtain a plurality of clustering results; a use case complexity corresponding to each exception test case is calculated based on a plurality of use case complexity correlation factors; a target exception test case is selected from each clustering result based on the use case complexity corresponding to each exception test case, and the target exception test case is sent, so that a staff member identifies a game exception problem based on the target exception test case.

[0189] The specific implementation of each operation can refer to the foregoing embodiments, which will not be repeated here.

[0190] From the above, the embodiment of the application can obtain a game exception detection request, and construct a plurality of game test cases based on the game exception detection request; execute the plurality of game test cases through a game test version and a game history version respectively to obtain a test version execution result and a history version execution result corresponding to each game test case; compare the test version execution result and the history version execution result to obtain a plurality of exception test cases; perform clustering operation based on the difference features of each exception test case to obtain a plurality of clustering results; calculate the test case complexity of each exception test case based on a plurality of test case complexity correlation factors; and select a target exception test case from each clustering result based on the test case complexity of each exception test case and send the target exception test case. Since the application clusters and classifies the exception test cases according to the difference features of each exception test case and the game exception problems, a plurality of clustering results including a plurality of exception test cases are obtained, a large number of repeated and redundant exception test cases are simplified and classified, then the test case complexity of each exception test case is calculated based on a plurality of test case complexity correlation factors, and a target exception test case with the lowest test case complexity is selected from each clustering result based on the test case complexity of each exception test case and sent, which can reduce the complexity of the to-be-identified exception test case as much as possible and reduce the complexity of the game exception problem identification, thereby improving the efficiency of the game exception problem identification.

[0191] Those skilled in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructions or controlled by instructions related to hardware, which can be stored in a computer readable storage medium and loaded and executed by a processor.

[0192] To this end, the embodiment of the application provides a computer readable storage medium, which stores a plurality of instructions capable of being loaded by a processor to execute the steps in any one of the game exception problem identification methods provided by the embodiments of the application. For example, the instructions can execute the following steps:

[0193] Obtain a game exception detection request, and construct a plurality of game test cases based on the game exception detection request; execute the plurality of game test cases through a game test version and a game history version respectively to obtain a test version execution result and a history version execution result corresponding to each game test case; compare the test version execution result and the history version execution result to obtain a plurality of exception test cases; perform clustering operation based on the difference features of each exception test case to obtain a plurality of clustering results; calculate the test case complexity of each exception test case based on a plurality of test case complexity correlation factors; and select a target exception test case from each clustering result based on the test case complexity of each exception test case and send the target exception test case.

[0194] For example, the electronic device obtains a game anomaly detection request, and constructs a plurality of game test cases based on the game anomaly detection request; executes the plurality of game test cases through a game test version, obtains a test version execution result corresponding to each game test case, and executes the plurality of game test cases through a game history version, to obtain a history version execution result corresponding to each game test case; compares the test version execution result and the history version execution result corresponding to the same game test case, to obtain a plurality of abnormal test cases in which there is a difference between the test version execution result and the history version execution result; performs a clustering operation on the plurality of abnormal test cases based on one or more difference features corresponding to each abnormal test case, to obtain a plurality of clustering results; calculates a test case complexity corresponding to each abnormal test case based on a plurality of test case complexity correlation factors; selects a target abnormal test case from each clustering result based on the test case complexity corresponding to each abnormal test case, and sends the target abnormal test case, so that a staff member identifies a game anomaly problem based on the target abnormal test case.

[0195] The specific implementation of each operation can refer to the foregoing embodiments, and will not be described here again.

[0196] The computer readable storage medium can include a read-only memory (ROM), a random access memory (RAM), a magnetic disk, an optical disk, and the like.

[0197] Due to the instructions stored in the computer readable storage medium, the steps of any one of the game anomaly problem identification methods provided in the embodiments of the application can be executed, and thus the beneficial effects of any one of the game anomaly problem identification methods provided in the embodiments of the application can be achieved. Details are described in the foregoing embodiments, and will not be described here again.

[0198] According to an aspect of the present application, a computer program product or computer program is provided, which includes computer instructions stored in a computer readable storage medium. The processor of the computer device reads the computer instructions from the computer readable storage medium, and the processor executes the computer instructions, so that the computer device executes the method provided in the various optional implementations of the game anomaly problem identification aspect described above.

[0199] The game abnormality problem identification method, device, electronic equipment and computer readable storage medium provided by the embodiment of the present application are described in detail above, the principle and implementation mode of the present application are described in this paper by applying specific examples, and the above embodiment is only used to help understand the method of the present application and its core idea; at the same time, for those skilled in the art, according to the idea of the present application, the specific implementation mode and application range will be changed, and the above description should not be understood as the limitation of the present application.

Claims

1. A method for identifying game anomalies, characterized in that, include: Obtain game anomaly detection requests and construct multiple game test cases based on the game anomaly detection requests; By executing multiple game test cases using the game test version, the execution result of the test version corresponding to each game test case is obtained. By executing multiple game test cases using the game historical version, the execution result of the historical version corresponding to each game test case is obtained. By comparing the execution results of the test version corresponding to the same game test case with the execution results of the historical version, multiple abnormal test cases in which there are differences between the execution results of the test version and the execution results of the historical version are obtained; Based on one or more differential features corresponding to each of the abnormal test cases, a clustering operation is performed on multiple abnormal test cases to obtain multiple clustering results; Based on multiple factors related to test case complexity, the test case complexity corresponding to each of the abnormal test cases is calculated. Based on the test case complexity corresponding to each of the above abnormal test cases, a target abnormal test case is selected from each of the clustering results and sent to the staff so that they can identify game abnormal issues based on the target abnormal test case.

2. The game anomaly identification method according to claim 1, characterized in that, The method involves clustering multiple abnormal test cases based on one or more differential features corresponding to each abnormal test case, resulting in multiple clustering results, including: Based on one or more difference features corresponding to each of the abnormal test cases, the abnormal test cases with the same difference features are deduplicated to obtain deduplicated abnormal test cases. Determine the set of difference features corresponding to each deduplicated abnormal test case; By assessing the similarity between different sets of differential features, clustering operations are performed on multiple deduplicated abnormal test cases to obtain multiple clustering results.

3. The game anomaly identification method according to claim 2, characterized in that, The method involves clustering multiple deduplicated abnormal test cases based on the similarity between different sets of differential features, resulting in multiple clustering results, including: One or more core abnormal test cases are determined from multiple deduplicated abnormal test cases by the degree of similarity between different sets of said difference features. Using each of the core exception test cases as the core of a cluster, clustering operations are performed on multiple deduplicated exception test cases to obtain multiple clustering results.

4. The game anomaly identification method according to claim 3, characterized in that, The step of determining one or more core abnormal test cases from multiple deduplicated abnormal test cases based on the similarity between different sets of differential features includes: The current abnormal test case is determined from the multiple deduplicated abnormal test cases; Obtain the current similarity between the set of differences in features corresponding to each deduplicated abnormal test case and the set of differences in features corresponding to the current abnormal test case; If the number of deduplicated abnormal test cases with a current similarity level less than or equal to a preset similarity level exceeds a preset number, the current abnormal test case is identified as a core abnormal test case, and a clustering label is assigned to the core abnormal test case. Return to the step of determining the current abnormal test case from the multiple deduplicated abnormal test cases, until all deduplicated abnormal test cases have been traversed.

5. The game anomaly identification method according to claim 3, characterized in that, The process involves using each of the core abnormal test cases as the core of a cluster, and performing clustering operations on multiple deduplicated abnormal test cases to obtain multiple clustering results, including: The deduplicated abnormal test cases whose similarity to the set of differences of the core abnormal test cases is less than or equal to a preset similarity are determined as the neighbor abnormal test cases corresponding to the core abnormal test cases. The neighbor anomaly test cases are labeled with the same clustering labels as the core anomaly test cases.

6. The game anomaly identification method according to claim 1, characterized in that, The calculation of the test case complexity for each abnormal test case based on multiple test case complexity correlation factors includes: Normalize the complexity correlation factors of each of the multiple abnormal test cases to obtain normalized test case complexity correlation factors; Obtain the target weight of each of the normalized use case complexity-related factors; Based on the normalized test case complexity correlation factors and the target weight, the test case complexity corresponding to each abnormal test case is calculated.

7. The game anomaly identification method according to claim 6, characterized in that, The step of obtaining the target weight of each of the normalized use case complexity correlation factors includes: Obtain the initial weights of each of the normalized use case complexity-related factors; The initial weights are adjusted based on the frequency of occurrence of specific information of the related factors and the total amount of data of the related factors in the use case complexity, to obtain the target weights.

8. The game anomaly identification method according to claim 1, characterized in that, The step of selecting a target abnormal test case from each clustering result based on the test case complexity corresponding to each of the abnormal test cases includes: Based on the test case complexity corresponding to multiple abnormal test cases in each clustering result, the abnormal test case with the lowest test case complexity in the clustering results is obtained by comparison. The abnormal test case with the lowest test case complexity in the clustering results is selected as the target abnormal test case.

9. A device for identifying game anomalies, characterized in that, include: The acquisition unit is used to acquire game anomaly detection requests and build multiple game test cases based on these requests. The execution unit is used to execute multiple game test cases using the game test version, obtain the execution result of the test version corresponding to each game test case, and execute multiple game test cases using the game historical version, obtain the execution result of the historical version corresponding to each game test case; The comparison unit is used to compare the execution results of the test version and the execution results of the historical version corresponding to the same game test case, and to obtain multiple abnormal test cases where there are differences between the execution results of the test version and the execution results of the historical version. Clustering unit is used to perform clustering operations on multiple abnormal test cases based on one or more differential features corresponding to each abnormal test case, and obtain multiple clustering results; The calculation unit is used to calculate the test case complexity corresponding to each abnormal test case based on multiple test case complexity correlation factors; The filtering unit is used to select a target abnormal test case from each cluster result based on the test case complexity corresponding to each abnormal test case, and send the target abnormal test case so that staff can identify abnormal issues in the game based on the target abnormal test case.

10. An electronic device, characterized in that, It includes a processor and a memory, the memory storing an application program, and the processor running the application program within the memory to perform the steps in the game anomaly identification method according to any one of claims 1 to 8.

11. A computer program product, comprising a computer program / instructions, characterized in that, When the computer program / instruction is executed by the processor, it implements the steps in the game anomaly identification method according to any one of claims 1 to 8.

12. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a plurality of instructions adapted for loading by a processor to perform the steps of the game anomaly identification method according to any one of claims 1 to 8.

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