Traffic playback screening method, device and equipment and readable storage medium
By cleaning and comparing the test traffic similarly, filtering out traffic with obvious personalized characteristics as playback requests, solving the problem of insufficient test coverage scenarios in the existing technology, and achieving more comprehensive test scenario coverage.
Patent Information
- Application Number
- CN202510699851.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-05-28
- Publication Date
- 2025-07-11
AI Technical Summary
In the existing traffic playback technology, there are fewer test coverage scenarios, and it is difficult to effectively filter out appropriate traffic for playback to improve test coverage.
By determining multiple test traffic of the target test service, the personalized features of each test traffic are highlighted for cleaning, the enumerable eigenvalue vector of enumerable requests is obtained, and the cleaned test traffic is similarly compared with the enumerable eigenvalue vector, and the traffic that does not belong to the enumerable request is selected as a playback request.
The coverage of test scenarios is improved, especially the coverage of special scenarios, boundary scenarios or abnormal scenarios, and the comprehensiveness of the test is enhanced.
Smart Images
Figure CN120301787A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of testing technologies, and more specifically, to a traffic playback screening method, apparatus, device, and readable storage medium. Background Art
[0002] In the field of software development, testing work is crucial. Whenever new devices are put into development or existing devices are facing updates, testing is required to evaluate whether the code logic can run properly.
[0003] Currently, in the field of testing, traffic recording technology and traffic playback technology are often used to verify the code correctness of the test object. By playing back a large number of requests, generally more application scenarios can be covered. However, there are a large number of duplicate or similar requests in the actual testing process, so that whether a small or large number of requests are played back, the covered scenarios do not differ significantly. Based on this, how to select appropriate traffic from the massive traffic for playback to improve the test coverage scenarios has become a key problem to be solved urgently. Summary of the Invention
[0004] In view of this, this application provides a traffic playback screening method, apparatus, device, and readable storage medium, which are used to solve the disadvantage of less test coverage scenarios in the existing traffic playback technology.
[0005] In order to achieve the above purpose, the following solutions are proposed:
[0006] A traffic playback screening method includes:
[0007] Determine multiple test traffic corresponding to the target test service;
[0008] For the purpose of highlighting the personalized characteristics of each test traffic, clean each test traffic;
[0009] Obtain an enumeration feature value vector for identifying the enumerable requests of the target test service;
[0010] Perform a similarity comparison between each cleaned test traffic and the enumeration feature value vector, and screen out all the cleaned test traffic that does not belong to the enumerable requests as playback requests for playback.
[0011] Optionally, the cleaning each test traffic for the purpose of highlighting the personalized characteristics of each test traffic includes:
[0012] Determine each preset low-value feature type;
[0013] Remove all parameter values in each test traffic that match each low-value feature type, and the test traffic obtained after removal is the cleaned test traffic.
[0014] Optionally, obtaining the enumerable feature value vector for identifying the enumerable requests of the target test service includes:
[0015] Obtaining multiple service requests of the target test service;
[0016] Cleaning each service request for the purpose of highlighting the personalized features of each service request;
[0017] Based on the values corresponding to each key in each of the cleaned service requests, identifying target keys belonging to the enumerable type;
[0018] Generating an enumerable feature value vector based on the target values corresponding to each target key.
[0019] Optionally, the identifying target keys belonging to the enumerable type based on the values corresponding to each key in each of the cleaned service requests includes:
[0020] Identifying the values corresponding to each key in each of the cleaned service requests, and counting the number of different values corresponding to the same key;
[0021] Obtaining a quantity range for characterizing the enumeration degree of the key;
[0022] Determining the keys whose corresponding quantities meet the quantity range as target keys belonging to the enumerable type.
[0023] Optionally, the obtaining a quantity range for characterizing the enumeration degree of the key includes:
[0024] Generating the quantity range with the goal of improving the scenario coverage rate of each playback request.
[0025] Optionally, the generating an enumerable feature value vector based on the target values corresponding to each target key includes:
[0026] Calculating the MD5 value of the target value corresponding to each target key, and the MD5 values corresponding to each target key form the enumerable feature value vector.
[0027] Optionally, the comparing the similarity between each cleaned test traffic and the enumerable feature value vector, and screening out all the cleaned test traffic that does not belong to the enumerable requests as playback requests for playback includes:
[0028] Calculating the MD5 information of each cleaned test traffic, calculating the similarity between the MD5 information of each test traffic and each MD5 value in the enumerable feature value vector, and using all the test traffic with similarities less than the preset similarity condition as playback requests for playback.
[0029] A traffic playback screening device, comprising:
[0030] A determination module, configured to determine a plurality of test traffic corresponding to a target test service;
[0031] A cleaning module, configured to clean each test traffic for the purpose of highlighting the personalized characteristics of each test traffic;
[0032] An acquisition module, configured to acquire an enumerated feature value vector for identifying enumerable requests of the target test service;
[0033] A screening module, configured to perform a similarity comparison between each cleaned test traffic and the enumerated feature value vector, and screen out all the cleaned test traffic that does not belong to the enumerable requests as playback requests for playback.
[0034] A traffic playback screening device, comprising a memory and a processor;
[0035] The memory is configured to store a program;
[0036] The processor is configured to execute the program to implement each step of the above traffic playback screening method.
[0037] A readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, each step of the above traffic playback screening method is implemented.
[0038] As can be seen from the above technical solution, the traffic playback screening method provided by this application can determine multiple test traffic corresponding to the target test service; for the purpose of highlighting the personalized characteristics of each test traffic, each test traffic is cleaned; based on this, this application can remove redundant information and interference information in the test traffic through cleaning, making the unique characteristics of the test traffic obvious, facilitating subsequent targeted similarity comparison and reducing the amount of data for similarity comparison, and accelerating the process of similarity comparison; then, an enumerated feature value vector for identifying the enumerable requests of the target test service can be obtained; the enumerable requests are made clear through the enumerated feature value vector, facilitating subsequent screening; considering that enumerable requests often represent relatively conventional and common test scenarios, while traffic that does not belong to enumerable requests may correspond to some special scenarios, boundary scenarios or abnormal scenarios. Therefore, this application can perform a similarity comparison between each cleaned test traffic and the enumerated feature value vector, and screen all the cleaned test traffic that does not belong to enumerable requests as playback requests for playback; based on this, this application can cover scenarios that are easily missed in conventional tests by screening test traffic that may correspond to special scenarios, boundary scenarios or abnormal scenarios for playback, thereby improving the overall test scenario coverage rate. It can be seen that this application can improve the probability of corresponding to special scenarios, boundary scenarios or abnormal scenarios in the playback requests by combining the enumerated feature vectors, and further improve the test scenario coverage rate. BRIEF DESCRIPTION OF THE DRAWINGS
[0039] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only the embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained according to the provided drawings without creative efforts.
[0040] Figure 1 Flowchart of a traffic playback screening method disclosed in an embodiment of the present application;
[0041] Figure 2 Block diagram of the structure of a traffic playback screening device disclosed in an embodiment of the present application;
[0042] Figure 3 Hardware block diagram of a traffic playback screening device disclosed in an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS
[0043] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts belong to the scope of protection of the present application.
[0044] An embodiment of the present application provides a traffic playback screening method. This traffic playback screening method can be applied to various test systems or playback systems, and can also be applied to various computer terminals or intelligent terminals. Its execution subject can be the processor or server of a computer terminal or intelligent terminal.
[0045] Next, in combination with Figure 1 a detailed introduction to the traffic playback screening method of the present application will be given, including the following steps:
[0046] Step S1: Determine multiple test traffic corresponding to the target test service.
[0047] Specifically, the target test service can be any one or more services of any test subject that needs to be tested.
[0048] Among them, the test subject can have multiple different types.
[0049] For example, the test subject can be a software module that needs to be tested, a newly launched applet, or software that needs to be iteratively updated.
[0050] Recall keywords can be generated according to the test subject and the target test service;
[0051] According to the recall keywords, multiple test traffic can be determined.
[0052] Among them, multiple ways can be used to implement determining multiple test traffic according to the recall keywords.
[0053] For example, multiple test traffic can be determined from the log data according to the recall keywords.
[0054] Multiple test traffic can also be determined from the database and / or the third database according to the recall keywords.
[0055] The target test service can include one or more different types of services.
[0056] For example, it can be the product search service of an e-commerce platform, the user login service of an e-commerce platform, or the user payment service, etc.
[0057] Step S2: Clean each test traffic for the purpose of highlighting the personalized features of each test traffic.
[0058] Specifically, the parameter values corresponding to the low-value feature types in each test traffic can be cleaned.
[0059] In order to highlight the personalized features of each test traffic, each test traffic can be cleaned in various ways.
[0060] For example, a cleaning model can be constructed, which is trained for the purpose of highlighting the personalized features of each test traffic.
[0061] Each test traffic can be preprocessed;
[0062] Among them, the preprocessing includes missing value filling and duplicate value deletion, etc.
[0063] The cleaning model can be used to clean each preprocessed test traffic.
[0064] Another example is that the common parameter types to be removed can be determined in advance;
[0065] In each test traffic, the feature values matching the common parameter types are removed to obtain the cleaned test traffic.
[0066] Step S3: Obtain the enumerated feature value vector for identifying the enumerable requests of the target test service.
[0067] Specifically, the business requests of multiple target test services can be parsed to filter out the enumerated feature value vectors for identifying the enumerable requests of the target test service.
[0068] Among them, the enumerable requests can have finite and enumerable corresponding parameter values or corresponding attribute values.
[0069] For example, in the e-commerce search service, the searched product categories such as clothing, electronic products, food, etc.
[0070] Step S4: Compare each cleaned test traffic with the enumerated feature value vector, and filter out all the cleaned test traffic that does not belong to the enumerable requests as replay requests for replay.
[0071] Specifically, in various ways, the similarity comparison between each cleaned test traffic and the enumerated feature value vector can be realized.
[0072] For example, the Euclidean distance expression can be used to measure the straight-line distance between each cleaned test traffic and the enumerated feature value vector.
[0073] The Manhattan distance expression can also be used to calculate the sum of the absolute values of the differences between each cleaned test traffic and the enumerated eigenvalue vector in each dimension.
[0074] The cosine similarity expression can also be used to calculate the similarity between each cleaned test traffic and the enumerated eigenvalue vector in each dimension.
[0075] A machine learning method can also be used to calculate the similarity between each cleaned test traffic and the enumerated eigenvalue vector in each dimension.
[0076] Among them, the machine learning method can be the K-nearest neighbor algorithm, support vector machine (SVM), decision tree, etc.
[0077] As can be seen from the above technical solutions, the traffic playback screening method provided by this application can determine multiple test traffics corresponding to the target test service; considering that different test traffics can correspond to the same scenario or similar scenarios, in order to remove the same scenario or similar scenarios as much as possible, this application aims to highlight the personalized features of each test traffic and clean each test traffic; based on this, this application can remove redundant information and interference information in the test traffic through cleaning, making the unique features of the test traffic obvious and the personalized features of each test traffic obvious, thereby facilitating subsequent targeted similarity comparison and reducing the amount of data for similarity comparison, and accelerating the process of similarity comparison; then, an enumerated eigenvalue vector for identifying the enumerable requests of the target test service can be obtained; the various enumerable requests are made clear through the enumerated eigenvalue vector, which is convenient for subsequent screening; considering that enumerable requests often represent relatively regular, common, and test scenarios that can be represented by examples, while the traffic that does not belong to enumerable requests may correspond to some special scenarios, boundary scenarios, or abnormal scenarios. Therefore, this application can perform a similarity comparison between each cleaned test traffic and the enumerated eigenvalue vector, and screen all the cleaned test traffics that do not belong to enumerable requests as playback requests for playback; based on this, this application can cover the scenarios that are easily missed in regular tests by screening the test traffics that may correspond to special scenarios, boundary scenarios, or abnormal scenarios for playback, thereby improving the overall test scenario coverage rate. It can be seen that this application can improve the probability of corresponding to special scenarios, boundary scenarios, or abnormal scenarios in the playback requests by combining the enumerated feature vectors, and further improve the test scenario coverage rate.
[0078] In some embodiments of this application, the process of step S2, which aims to highlight the personalized features of each test traffic and clean each test traffic, is described in detail as follows:
[0079] S20. Determine each preset low-value feature type.
[0080] Specifically, low-value feature vectors can be extracted from multiple service requests of the target test service;
[0081] Determine the parameter type corresponding to each low-value feature vector, and use the parameter types corresponding to the respective low-value feature vectors as respective low-value feature types.
[0082] The low-value feature types can be feature types with relatively low reference values, such as device number, user identifier, user login key, user login platform, user login version number, recall channel, parameter name, number of included parameters, etc.
[0083] S21. Remove all parameter values in each test traffic that match the respective low-value feature types. After removal, the cleaned test traffic is obtained.
[0084] Specifically, in multiple ways, all parameter values in each test traffic that match the respective low-value feature types can be removed.
[0085] For example, a regular expression can be constructed according to the low-value feature type;
[0086] Use the positive expression to extract all parameter values in each test traffic that match the respective low-value feature types.
[0087] Another example is that a string can be constructed according to the low-value feature type;
[0088] Use the string to remove all parameter values in each test traffic that match the respective low-value feature types.
[0089] It can be seen from the above technical solution that this embodiment provides an optional method for cleaning each test traffic with the aim of highlighting the personalized features of each test traffic. Through the above method, each test traffic can be cleaned in combination with the low-value feature types, thereby better highlighting the personalized features in each test traffic.
[0090] In some embodiments of the present application, the process of step S3, obtaining an enumerated feature value vector for identifying an enumerable request of the target test service, is described in detail as follows:
[0091] S30. Obtain multiple service requests of the target test service.
[0092] Specifically, a large number of service requests corresponding to the target test service can be recalled according to keywords matching the target test service.
[0093] S31. Clean each service request with the aim of highlighting the personalized features of each service request.
[0094] Specifically, all parameter values in each business request that match each low - value feature type can be removed, and the business request after cleaning is obtained after the removal.
[0095] For example, all parameter values that match each low - value feature type can be extracted from each business request through regular expressions or strings, so as to remove all parameter values that match each low - value feature type from each business request.
[0096] S32. Based on the value corresponding to each key in each cleaned business request, identify the target keys belonging to the enumerable type.
[0097] Specifically, each cleaned business request can be parsed to determine the value corresponding to each key included in each business request.
[0098] Count the value corresponding to each key in each business request, and identify the key whose corresponding value is enumerable as the target key.
[0099] S33. Based on the target value corresponding to each target key, generate an enumerated feature value vector.
[0100] Specifically, in order to compress the data volume, an identifier of the target value corresponding to each target key can be generated, and each identifier is combined to form an enumerated feature value vector.
[0101] Among them, there are various ways to implement the generation of the identifier of the target value corresponding to each target key.
[0102] For example, an identifier of the target value corresponding to each target key can be generated through a signature algorithm.
[0103] Among them, the signature algorithm can be any one of HMAC (Hash - based Message Authentication Code), RSA signature algorithm, DSA (Digital Signature Algorithm), ECDSA (Elliptic Curve Digital Signature Algorithm), Lamport signature algorithm, and SPHINCS+ signature algorithm.
[0104] It is also possible to combine with the Snowflake algorithm to generate the identifier of the target value corresponding to each target key.
[0105] It is also possible to combine a hashing algorithm to generate an identifier for the target value corresponding to each target key.
[0106] Among them, the hashing algorithm can be any one of Secure Hash Algorithm, Cryptographic Hash Function Algorithm, SHA-3, SHA-512, BLAKE2, and Whirlpool.
[0107] It is also possible to combine a message digest algorithm to generate an identifier for the target value corresponding to each target key.
[0108] From the above technical solutions, it can be seen that this embodiment provides an optional way to obtain an enumerable feature value vector for identifying the target test service. Through the above method, keys belonging to the enumerable type can be identified from the business requests that have been cleaned and correspond to the target test service, and the enumerable feature value vector can be generated using the values corresponding to the enumerable keys.
[0109] In some embodiments of the present application, the process of step S32, which is to identify the target keys belonging to the enumerable type based on the values corresponding to each key in each of the cleaned business requests, is described in detail as follows:
[0110] S320: Identify the values corresponding to each key in each of the cleaned business requests, and count the number of different values corresponding to the same key.
[0111] Specifically, the keys included in each of the cleaned business requests can be identified.
[0112] Determine the value corresponding to each key.
[0113] The same values corresponding to the same key can be de-duplicated, and the number of values corresponding to each key after de-duplication can be determined.
[0114] S321: Obtain the quantity range used to characterize the key enumeration degree.
[0115] Specifically, considering that keys with too many corresponding values have too strong randomness and it is difficult to construct an enumerable feature vector, a maximum value n2 can be set to remove keys with too strong randomness.
[0116] Considering that keys with too few corresponding values have too high fixity and there is no need to construct an enumerable feature vector, a minimum value n1 can be set to remove keys with relatively fixed parameter values.
[0117] Based on this, the quantity range can be [n1, n2].
[0118] S322. Determine the target keys that meet the quantity range and belong to the enumerable type.
[0119] Specifically, the value quantity of each key can be compared with the quantity range, and each key that meets the quantity range is used as each target key.
[0120] It can be seen from the above technical solutions that this embodiment provides an optional method for identifying target keys belonging to the enumerable type based on the value corresponding to each key in each business request after cleaning. Through the above method, the keys can be further screened by the quantity range, and the target keys with weak fixity and enumerable parameter values are retained.
[0121] In some embodiments of the present application, the process of step S321, obtaining the quantity range for characterizing the enumeration degree of keys, is described in detail as follows:
[0122] S3210. Generate the quantity range with the goal of improving the scenario coverage rate of each playback request.
[0123] Specifically, an evaluation model is generated with the goal of improving the scenario coverage rate of each playback request.
[0124] Based on the evaluation model, the quantity range is adjusted.
[0125] It can be seen from the above technical solutions that this embodiment provides an optional method for obtaining the quantity range for characterizing the enumeration degree of keys. Through the above method, the reliability of the present application can be further improved.
[0126] In some embodiments of the present application, the process of step S33, generating an enumeration feature value vector based on the target value corresponding to each target key, is described in detail as follows:
[0127] S330. Calculate the MD5 value of the target value corresponding to each target key, and the MD5 values corresponding to each target key form the enumeration feature value vector.
[0128] Specifically, the target value corresponding to each target key can be extracted, and combined with the fifth-generation secure hash algorithm, the MD5 value of each target value of each target key is generated.
[0129] Combine each MD5 value to form an enumeration feature value vector.
[0130] As can be seen from the above technical solution, this embodiment provides an optional way to generate an enumerated eigenvalue vector. Through the above method, different target values corresponding to the same target key can be converted into MD5 values, compressing the data volume of each target value.
[0131] In some embodiments of the present application, the process of step S4, comparing each cleaned test traffic with the enumerated eigenvalue vector and screening all the cleaned test traffic that does not belong to the enumerable requests as replay requests for replay is described in detail as follows:
[0132] S40. Calculate the MD5 information of each cleaned test traffic, calculate the similarity between the MD5 information of each test traffic and each MD5 value in the enumerated eigenvalue vector, and use all the test traffic with similarities less than the preset similarity condition as replay requests for replay.
[0133] Specifically, the MD5 information of each cleaned test traffic can be generated in combination with the fifth-generation secure hash algorithm.
[0134] The similarity between the MD5 information of each test traffic and each MD5 value in the enumerated eigenvalue vector can be calculated.
[0135] When there is any similarity exceeding the preset similarity threshold, the corresponding test traffic is regarded as an enumerable test traffic.
[0136] For all test traffic with similarities less than the similarity threshold, they are used as replay requests that do not belong to the enumerable requests for replay.
[0137] As can be seen from the above technical solution, this embodiment provides an optional way to screen the cleaned test traffic that does not belong to the enumerable requests. Through the above method, the reliability and accuracy of the present application can be further improved.
[0138] Next, Figure 2 The traffic replay screening device provided by the present application will be introduced in detail below. The traffic replay screening device provided below can be compared with the traffic replay screening method provided above.
[0139] See Figure 2 It can be found that the traffic replay screening device may include:
[0140] Determination module 10, configured to determine multiple test traffics corresponding to a target test service;
[0141] Cleaning module 20, configured to clean each test traffic for the purpose of highlighting the personalized characteristics of each test traffic;
[0142] An acquisition module 30, configured to acquire an enumerated feature value vector for identifying enumerable requests of the target test service;
[0143] A screening module 40, configured to perform a similarity comparison between each cleaned test traffic and the enumerated feature value vector, and screen out all the cleaned test traffic that does not belong to the enumerable requests as replay requests for replay.
[0144] Further, the cleaning module 20 may include:
[0145] A first cleaning unit, configured to determine various preset low-value feature types;
[0146] A second cleaning unit, configured to remove all parameter values in each test traffic that match the various low-value feature types, and obtain the cleaned test traffic after removal.
[0147] Further, the acquisition module 30 may include:
[0148] A service request acquisition unit, configured to acquire multiple service requests of the target test service;
[0149] A service request cleaning unit, configured to clean each service request for the purpose of highlighting the personalized features of each service request;
[0150] A target key identification unit, configured to identify target keys belonging to the enumerable type based on the values corresponding to each key in each cleaned service request;
[0151] An enumerated feature value vector generation unit, configured to generate an enumerated feature value vector based on the target values corresponding to each target key.
[0152] Further, the target key identification unit may include:
[0153] A quantity statistics subunit, configured to identify the values corresponding to each key in each cleaned service request, and count the number of different values corresponding to the same key;
[0154] A quantity range acquisition subunit, configured to acquire a quantity range for characterizing the enumeration degree of keys;
[0155] A target key screening subunit, configured to determine the keys whose corresponding quantities meet the quantity range as target keys belonging to the enumerable type.
[0156] Further, the quantity range acquisition subunit may include:
[0157] A quantity range generation component, configured to generate the quantity range with the goal of improving the scenario coverage rate of each replay request.
[0158] Furthermore, the enumerated eigenvalue vector generation unit may include:
[0159] The MD5 value calculation subunit is configured to calculate the MD5 value of the target value corresponding to each target key, and the MD5 values corresponding to each target key form the enumerated eigenvalue vector.
[0160] Furthermore, the screening module 40 may include:
[0161] The MD5 information calculation unit is configured to calculate the MD5 information of each cleaned test traffic, calculate the similarity between the MD5 information of each test traffic and each MD5 value in the enumerated eigenvalue vector, and use the test traffic with all similarities less than the preset similarity condition as a playback request for playback.
[0162] The traffic playback screening device provided by the embodiments of the present application can be applied to traffic playback screening devices, such as PC terminals, cloud platforms, servers, server clusters, etc. Optionally, Figure 3 shows a hardware structure block diagram of the traffic playback screening device. Referring to Figure 3 , the hardware structure of the traffic playback screening device may include: at least one processor 1, at least one communication interface 2, at least one memory 3, and at least one communication bus 4;
[0163] In the embodiments of the present application, the number of the processor 1, the communication interface 2, the memory 3, and the communication bus 4 is at least one, and the processor 1, the communication interface 2, and the memory 3 complete communication with each other through the communication bus 4;
[0164] The processor 1 may be a central processing unit CPU, or a specific integrated circuit ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of the present invention, etc.;
[0165] The memory 3 may include a high-speed RAM memory, and may also include a non-volatile memory, such as at least one disk memory;
[0166] Wherein, the memory stores a program, and the processor can call the program stored in the memory, and the program is used for:
[0167] Determine multiple test traffics corresponding to the target test service;
[0168] Clean each test traffic for the purpose of highlighting the personalized characteristics of each test traffic;
[0169] Obtain an enumerated feature value vector for identifying enumerable requests of the target test service;
[0170] Perform a similarity comparison between each cleaned test traffic and the enumerated feature value vector, and filter out all the cleaned test traffic that does not belong to the enumerable requests as replay requests for replay.
[0171] Optionally, the refinement function and expansion function of the program can be referred to the above description.
[0172] The embodiment of the present application also provides a readable storage medium, which can store a program suitable for execution by a processor, and the program is used for:
[0173] Determine multiple test traffic corresponding to the target test service;
[0174] For the purpose of highlighting the personalized features of each test traffic, clean each test traffic;
[0175] Obtain an enumerated feature value vector for identifying enumerable requests of the target test service;
[0176] Perform a similarity comparison between each cleaned test traffic and the enumerated feature value vector, and filter out all the cleaned test traffic that does not belong to the enumerable requests as replay requests for replay.
[0177] Optionally, the refinement function and expansion function of the program can be referred to the above description.
[0178] Finally, it should also be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variation thereof is intended to cover a non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "including a..." does not exclude the existence of additional identical elements in the process, method, article or device including the element.
[0179] The various embodiments in this specification are described in a progressive manner, and the key points of each embodiment are the differences from other embodiments. The same and similar parts between the various embodiments can be referred to each other.
[0180] The foregoing description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. The various embodiments of the present application can be combined with each other. Therefore, the present application will not be limited to the embodiments shown herein, but rather is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims
1. A traffic playback screening method, characterized in that Including: Determine multiple test traffic corresponding to the target test service; For the purpose of highlighting the personalized characteristics of each test traffic, clean each test traffic; Obtain an enumeration feature value vector for identifying enumerable requests of the target test service; Perform a similarity comparison between each cleaned test traffic and the enumeration feature value vector, and screen out all the cleaned test traffic that does not belong to enumerable requests as replay requests for replay.
2. The traffic playback screening method according to claim 1, wherein The step of cleaning each test traffic for the purpose of highlighting the personalized characteristics of each test traffic includes: Determine each preset low-value feature type; Remove all parameter values in each test traffic that match each low-value feature type, and the cleaned test traffic is obtained after removal.
3. The traffic playback screening method according to claim 1, wherein The step of obtaining an enumeration feature value vector for identifying enumerable requests of the target test service includes: Obtain multiple service requests of the target test service; For the purpose of highlighting the personalized characteristics of each service request, clean each service request; Based on the value corresponding to each key in each cleaned service request, identify the target keys belonging to the enumerable type; Generate an enumeration feature value vector based on the target value corresponding to each target key.
4. The traffic playback screening method according to claim 3, wherein The step of identifying the target keys belonging to the enumerable type based on the value corresponding to each key in each cleaned service request includes: Identify the value corresponding to each key in each cleaned service request, and count the number of different values corresponding to the same key; Obtain a quantity range for characterizing the enumeration degree of the key; Determine the keys whose corresponding quantity meets the quantity range as the target keys belonging to the enumerable type.
5. The traffic playback screening method according to claim 4, wherein The step of obtaining a quantity range for characterizing the enumeration degree of the key includes: Generate the quantity range with the goal of improving the scenario coverage rate of each replay request.
6. The traffic playback screening method according to claim 3, wherein The step of generating an enumeration feature value vector based on the target value corresponding to each target key includes: Calculate the MD5 value of the target value corresponding to each target key, and the MD5 values corresponding to each target key form the enumeration feature value vector.
7. The traffic playback screening method according to claim 6, wherein The step of performing a similarity comparison between each cleaned test traffic and the enumeration feature value vector, and screening out all the cleaned test traffic that does not belong to enumerable requests as replay requests for replay includes: Calculate the MD5 information of each cleaned test traffic, calculate the similarity between the MD5 information of each test traffic and each MD5 value in the enumeration feature value vector, and use all test traffic with similarities less than the preset similarity condition as replay requests for replay.
8. A traffic playback screening device, characterized in that, Including: A determination module for determining multiple test traffic corresponding to the target test service; A cleaning module for cleaning each test traffic for the purpose of highlighting the personalized characteristics of each test traffic; An acquisition module for obtaining an enumeration feature value vector for identifying enumerable requests of the target test service; A screening module, configured to perform a similarity comparison between each of the cleaned test traffic and the enumerated eigenvalue vector, and screen out the cleaned test traffic that does not belong to the enumerable requests as replay requests for replay.
9. A traffic playback screening device, characterized in that It includes a memory and a processor; The memory is used to store programs; The processor is configured to execute the program to implement each step of the traffic replay screening method according to any one of claims 1-7.
10. A readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements each step of the traffic replay screening method according to any one of claims 1-7.