A test data processing method and device

Abstract

The present application discloses a test data processing method and device thereof, the method comprising: extracting a serial test case set with test conflicts from an acquired test case set; selecting a preset first test data item from a serial test data set on which the serial test case set depends; extracting a first case set that depends only on the first test data item from the serial test case set; and converting the first case set into a leaf case set of parallel testing by extracting the first test data item from the first case set. By adopting the present application, a test case set of serial testing can be converted into a test case set of parallel testing, which shortens the regression time and improves the regression efficiency.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to a test data processing method and device thereof. Background Art

[0002] Regression testing refers to retesting after modifying old test cases to confirm that the modification has not introduced new errors or caused errors in other program codes. In existing regression testing methods, usually after introducing multiple test cases, these test cases are used sequentially (serially) to test the test system. As the number of test cases increases, this method faces the problem of long regression time and low regression efficiency. Summary of the invention

[0003] The embodiments of the present application provide a test data processing method and a device thereof, which are used to at least solve the above technical problems.

[0004] An embodiment of the present application also provides a test data processing method, which includes: extracting a serial test case set with test conflicts from an acquired test case set; selecting a preset first test data item from a serial test data set on which the serial test case set depends; extracting a first test case set that only depends on the first test data item from the serial test case set; and converting the first test case set into a leaf test case set for parallel testing by extracting the first test data item from the first test case set.

[0005] An embodiment of the present application further provides a test data processing device, the device comprising: a processor; and a memory arranged to store computer executable instructions, wherein the executable instructions, when executed, enable the processor to perform the above method.

[0006] An embodiment of the present application also provides a computer-readable storage medium having computer instructions stored thereon, wherein the above method is implemented when the instructions are executed.

[0007] At least one of the above technical solutions adopted in the embodiments of the present application can achieve the following beneficial effects:

[0008] By adopting the present application, a test case set for serial testing can be converted into a test case set for parallel testing by screening out a test case set that only contains a single test data item from a test case set and extracting only dependent test data items from the test case set, thereby shortening the regression time and improving the regression efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0010] Figure 1 is an application scenario showing a regression test according to an exemplary embodiment of the present application;

[0011] Figure 2 is a flow chart illustrating a test data processing method according to an exemplary embodiment of the present application;

[0012] Figure 3 is a processing diagram illustrating a test case set of an exemplary embodiment of the present application;

[0013] Figure 4 is a block diagram illustrating a test data processing apparatus according to an exemplary embodiment of the present application. DETAILED DESCRIPTION

[0014] In order to make the purpose, technical solution and advantages of the present application clearer, the technical solution of the present application will be clearly and completely described below in combination with the specific embodiments of the present application and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present application.

[0015] The technical solutions provided by various embodiments of the present application are described in detail below in conjunction with the accompanying drawings.

[0016] Figure 1 FIG. 2 shows an application scenario of regression testing according to an exemplary embodiment of the present application. Figure 1 As shown, in daily life, users can install various applications on electronic terminals and use these applications to meet various needs of users. For example, they can use shopping applications to buy goods online, or use payment applications to pay for various goods. Figure 1 In the embodiment, when a user starts an application on an electronic terminal, the application can send a data request to the computer system 10 through a network connection, and then respond to the request using the business system corresponding to the application on the computer system 10, and then send the response data to the electronic terminal.

[0017] In order to ensure the business system running on the computer system 10, a computer system 20 separated from the computer system 10 is usually used to test the business system. For example, before the business system provides new services, the business system can be tested using a test system loaded on the computer system 20. After the test passes, the computer system 10 can provide new services to users.

[0018] In the present application, the computer system 20 can be used to perform regression testing on the business system, that is, to perform testing again after the business system performs business modifications. Figure 1 As shown, the test data processing method of the present application can be used to organize and aggregate existing test cases, convert serially executed test cases into test cases that can be processed in parallel and obtain more streamlined regression test cases, thereby improving regression efficiency.

[0019] The following will refer to Figure 2 To better describe the regression testing method performed by the computer system 20, Figure 2 A flowchart of a regression testing method according to an exemplary embodiment of the present application is shown.

[0020] In step S210, a serial test case set having a test conflict is determined from the test case set.

[0021] In this application, the business system under test can be referred to as the system under test, and a test case refers to a set of test inputs, execution conditions, and expected results written to test a specific goal, which is used to verify whether a specific software requirement is met. Therefore, a test case can be understood as a code that depends on test data and includes test expectations and return results. The test database that is relied on in the process of using the test case to test the system under test can be called a test data set. In order to facilitate the distinction of descriptions, each test data in the test data set can be described as a test data item.

[0022] In the present application, in order to facilitate various operations on the test case set, the test data items can be stored in the form of key-value pairs (key, value), wherein key refers to the keyword, and value refers to the value corresponding to the keyword. The test data set stored in (key, value) can be organized, indexed and stored in the form of key-value pairs. For example, when it is desired to call test data, the key can be obtained first, and then the corresponding value can be obtained using methods such as map.keySet(key) or hm.keySet(key). Any data type that can be stored in a (key, value) structure can be applied to the present application, for example, tailir, tdbm, kvfile. Based on this, if the test data item is not stored in a (key, value) structure, it can be converted to (key, value). For example, for text-type test data, the key can be determined as null, and the value can be determined as text.

[0023] The data conflict indicated in this application refers to the conflict between the test data items that different test cases rely on. The conflict in this application refers to the generated test case calling the key value or value in the same test data item at the same time when calling the test data item. For example, the test data item of test case 1 is (key1, value1), and the test data item of test case 2 is (key2, value2), then there is no conflict between the two test cases described, and the two test cases can be executed in parallel, but if the test data item of test case 1 is (key1, value1), and the test data item of test case 2 is (key1, value2), or, the test data item of test case 1 is (key1, value1), and the test data item of test case 2 is (key2, value1), or, the test data item of test case 1 is (key1, value1), and the test data item of test case 2 is (key1, value1), then the data of the two test cases will affect each other when calling the test data item, causing one of the test cases to fail, and then the entire regression process fails, and these two test cases have a test conflict.

[0024] In an embodiment, test cases with completely different test data items are filtered out from the test data set that the test case set depends on, and the test case set that the test data still existing in the test case set depends on is determined as a serial test data set. In other words, test data items with different key and value values ​​can be filtered out from the test case set, and the remaining test case set is a serial test case set.

[0025] In step S220, a preset first test data item is selected from the serial test data set that the serial test case set depends on. Specifically, multiple test data items that the serial test data set depends on can be determined. In other words, all (key, value) that the serial test data set depends on are determined. Subsequently, a first test data item is selected from the multiple test data items, wherein the first test data item refers to a test data item that is processed first in order.

[0026] In this implementation, the preset first test data item may be a test data item manually selected by a technician according to needs. As an example, the technician selects a test data item that is earlier in the timing sequence as the first test data item based on the timing sequence of each test data item to be called in the system under test. In another example, the technician may select a test data item for a newly added function of the system under test as the first test data item.

[0027] Optionally, the acquired test data items may be sorted according to preset rules to generate a test data item list, and the prediction rules may be based on the number of test cases on which each test data item depends, the importance level of each test case item, or a feature weight determined by the number and importance level of the test cases on which each test data item depends.

[0028] It should be noted that the importance level can be determined according to the importance of the test parameters corresponding to the test data item, which can be set by a technician in implementation. For example, if the test data item is a test feature for testing security, its feature weight can be set to high (e.g., 500); if the test data item is a test feature for evaluating a class, its feature weight can be set to low (e.g., 2); for another example, if the test data item is a test feature for priority testing, its feature weight can be set to high (e.g., 500); if the test data item is a test feature for later testing, its feature weight can be set to low (e.g., 2). Taking feature weight as an example, in implementation, the feature weight of each test case item can be determined. For ease of understanding, the test data item obtained in step S220 can determine the feature weight as shown in Table 1.

[0029] Test data items Number of test cases Importance Feature weights Test data item 1 5 100 500 Test data item 2 3 1 3 Test data item 3 2 1 2 Test data item 4 30 1 30

[0030] Table 1

[0031] Subsequently, the test case items may be sorted according to the size of the feature weights to generate a list of test data items. As shown in Table 1, the test data items may be arranged in the order of test data item 1, test data item 4, test data item 2, and test data item 3.

[0032] Finally, the first test data item is determined according to the order of priority in the test data item list mentioned above. For example, the test data item ranking first in the test data item list can be determined as the first test data item. In addition, the test data item ranking first in the ranking order of the number of dependent test cases or the ranking order of importance level can also be determined as the first test data item.

[0033] In the present application, the test case set that the user expects to process can be preferentially processed by presetting the first test data item, thereby meeting the user's customized needs for regression testing.

[0034] In step S230, a first test case set that depends only on the first test data item is extracted from the serial test case set. In short, the serial test data set can be classified according to the first test data item, that is, the serial test data set is classified into a first test case set that depends only on the first test data item and a first intermediate layer test case set other than the first test case set, wherein the first intermediate layer test case set can depend on a test case set other than the first test case set in the serial test case set, and further, the test data items in the first intermediate layer test case set at least depend on the first test data item.

[0035] In short, in order to convert the test case set of serial test into the test case set of parallel test, it is necessary to convert these test case sets into leaf case sets that can be executed in parallel. To this end, through the above processing, the test case set containing only a single test data item has been extracted from the test case set, and how to convert it into a leaf case set for parallel processing can be processed according to the following steps.

[0036] In step S240, the first use case set is converted into a leaf use case set for parallel testing by extracting the first test data item from the first use case set. The leaf use case set mentioned here indicates a set of use cases whose test cases do not depend on any test data item, so the leaf use case set can be tested in parallel. As an example, when the test data item in the first use case set only depends on d1 (key1, value1), the test data item d1 can be extracted from the first use case set, so that the first use case set can be transformed into a set of use cases that does not depend on any test data item.

[0037] In summary, the test data processing method according to the exemplary embodiment of the present application can convert a test case set of serial tests into a test case set of parallel tests by screening out a test case set containing only a single test data item from the test case set and extracting only dependent test data items from the test case set, thereby shortening the regression time and improving the regression efficiency.

[0038] In implementation, the extraction operation can be performed on each serial test case in the serial test case set, that is, the first intermediate layer use case set other than the first use case set can also be extracted. After the extraction process is performed on the first intermediate layer use case set, the extracted first intermediate layer use case set can be obtained.

[0039] As an example, a clustering operation is also performed on the extracted first intermediate layer use case set to generate a first level use case set.

[0040] Specifically, it can be determined whether the test data set on which the first intermediate layer use case set after extraction depends includes text-type test data items. As an example, according to the above description, the text-type data has been converted to (null, text vector), wherein the text vector can be a vector converted using an existing word vector representation method. Therefore, it can be determined that the test data set corresponding to the first intermediate layer use case set after processing includes text-type test data items by searching for a key of null.

[0041] If it is determined that the test data items of the text type are included, clustering processing can be performed on the first intermediate layer use case set, wherein the clustering processing can adopt an existing clustering algorithm. In short, word segmentation can be performed by constructing word weights using TF-IDF (term frequency–inverse document frequency), and word frequencies and weights can be counted. Subsequently, the weighted Euclidean distance is calculated based on the word frequency and weight, and word vectors with closer distances (satisfying the conditions) are classified into one category.

[0042] This clustering method can be used to filter and delete identical test cases in a test case set. For example, for a tree index type test case, two test cases with different orders and spaces can be identified as identical test cases by using the above clustering process, thereby deleting duplicate test cases. In other words, for the first intermediate layer test case set as a non-leaf test case set, text similarity processing can be used to delete duplicate test cases.

[0043] Optionally, all test data items are extracted from the test case set that the first level test case set depends on, and text test data items whose data type is text are extracted from all the test data items. These text test data items are used to determine the test case set that depends only on a single text test data item.

[0044] Subsequently, the first-level use case set is classified into a sub-use case set that depends only on a single text data item among the respective text data items and a remaining use case set that does not depend only on the single text data item.

[0045] For each subset of use cases, it can be determined whether the distance between the text data item corresponding to the subset of use cases and the first test data item meets the preset requirements. In the present application, the text data items can all be represented by text vectors to represent the value in the test data item. Therefore, various algorithms for calculating data space distance can be used to determine the distance between the two text data items. The algorithms for space distance include but are not limited to Euclidean distance, square of Euclidean distance, Mannhattan distance, and Cherchev distance.

[0046] The preset condition mentioned here can be a threshold set by the technician based on experience or scenario requirements. If the distance between the two is less than the set threshold, the second use case set can be converted into a leaf use case set for parallel testing by extracting the second text data item from the second use case set.

[0047] As another example, the method may further classify the first-level use case set into a plurality of sub-use case sets that depend only on a single text data item among the respective text data items and a remaining use case set that does not depend only on the single text data item.

[0048] Subsequently, it can be determined whether the distance between the text data items on which the plurality of sub-use case sets depend respectively and the first test data item meets the preset condition, wherein the preset condition mentioned here can be a threshold value set by the technician according to experience or scenario needs. If it meets the preset condition, the text data item is extracted from the sub-use case set, and the sub-use case set is converted into a leaf case for parallel testing. It can be seen that the method can perform various clustering operations on the text data items, convert the test case set into more test case sets that can be tested in parallel, thereby further improving the test efficiency.

[0049] In addition, the sub-use case sets that do not meet the preset requirements and the remaining use case sets are determined as the first-level use case sets. The first-level use case sets can be repeatedly operated in the above manner, including: for the first-level use case set, a second use case set that only depends on the second test data item and a second intermediate layer data set that does not only depend on the second test data item can be extracted; by extracting the second test data item from the second use case set, the second use case set is converted into a leaf use case set for parallel testing.

[0050] In addition, text clustering and stratification can be continued for the second layer of intermediate data sets until the serial test data set is divided into a plurality of leaf test case sets that can be executed in parallel and a multi-level data set generated according to the text data items. According to the above operation, the test case set of the serial test is finally converted into the test case set of the parallel test as much as possible, thereby reducing the number of test cases as much as possible and saving test time.

[0051] In order to better understand the present application, the following reference will be made to Figure 3 Describe the processing of the test case suite. Figure 3 is a processing diagram illustrating a test case set of an exemplary embodiment of the present application.

[0052] For ease of description, each test case can be described as follows Figure 3 is expressed as (test data, test request, test expectation). In addition, although Figure 3 The test case suite in relies on only 3 test cases, but this is only illustrative and not limiting.

[0053] like Figure 3 As shown, after obtaining the test case set, all test data items d1, d2 and d3 can be determined from the test case set on which the test case set depends, and then the test case set can be classified into a first case set that only depends on the test data item d1 and a first intermediate layer case set that not only depends on the test data item d1 in the order of the list.

[0054] Then, the test data item d1 can be extracted from the first use case set, thereby converting the first use case set into a leaf use case set that does not depend on any test data item. Figure 3 Some of the test case sets in the test case set have been implemented for parallel processing, which has reduced the test time compared to the existing technology.

[0055] Then, test data item d1 can be extracted for the first intermediate layer use case set. Figure 3 It is not shown in the figure, but preferably, after extracting d1, clustering can be performed on the first intermediate layer use case set to generate the first level use case set. In this way, the test cases that are actually the same can be removed.

[0056] like Figure 3 As shown, the first-level use case set can be divided into a second use case set and a second intermediate layer use case set according to the test data item d2. And when the test data item d2 is a text data item, the subsequent operation is performed, that is, whether the distance between the test data items d1 and d2 meets the preset condition is determined. If so, the test data item d2 can be extracted from the second use case set, thereby converting the second use case set into a leaf use case set that does not depend on any test data item. Finally, after extracting the test data item d2 from the second intermediate layer use case set, the second-level use case set is generated.

[0057] It can be seen that the second-level test case set can be aggregated by calculating the distance between the text data items and the test data items in the first-level test case set, thereby reducing the number of test cases as much as possible and saving testing time.

[0058] Due to the limitation of the number of test cases shown, the processing of the test case set has been completed. In practice, the test case set is very large, so it can be divided into Figure 3 The second-level use case set is recursively processed in this way until all the use case sets in the test case set are completed.

[0059] In order to more clearly understand the inventive concept of the exemplary embodiments of the present application, the following will refer to Figure 4 A block diagram of a test data processing device describing an exemplary embodiment of the present application. A person of ordinary skill in the art will understand that: Figure 4The device in the embodiment only shows the components related to the exemplary embodiment, and the device also includes Figure 4 Common components other than those shown in .

[0060] Figure 4 A block diagram of a test data processing device according to an exemplary embodiment of the present application is shown. The time reminder device refers to a device that can perform test data processing according to an exemplary embodiment of the present application. Figure 4 At the hardware level, the device includes a processor, an internal bus and a computer-readable storage medium, wherein the computer-readable storage medium includes a volatile memory and a non-volatile memory. The processor reads the corresponding computer program from the non-volatile memory and then runs it. Of course, in addition to the software implementation, this application does not exclude other implementation methods, such as logic devices or a combination of software and hardware, etc., that is to say, the execution subject of the following processing flow is not limited to each logic unit, but can also be hardware or logic devices.

[0061] Specifically, the processor performs the following operations: extracting a serial test case set with test conflicts from the acquired test case set; selecting a preset first test data item from the serial test data set on which the serial test case set depends; extracting a first test case set that only depends on the first test data item from the serial test case set; and converting the first test case set into a leaf test case set for parallel testing by extracting the first test data item from the first test case set.

[0062] Optionally, the processor extracts a serial test case set with test conflicts from the test case set in the execution step, including: filtering out test cases with completely different dependent test data items from a test data set on which the test case set depends, and determining the remaining test case set in the test case set as the serial test case set.

[0063] Optionally, the processor selects a preset first test data item from the serial test data set on which the serial test case set depends during the execution step, including: extracting multiple test data items on which each serial test case depends from the serial test data set on which the serial test case set depends; and selecting a first test data item from the multiple test data items according to a preset rule.

[0064] Optionally, the processor selecting the first test data item from the multiple test data items according to a preset rule during the execution step includes: sorting the multiple test data items according to the preset rule to generate a test data item list; and selecting the test data item ranked first in the test data item list as the first test data item.

[0065] Optionally, the processor, in executing the step of sorting the multiple test data items according to the preset rule to generate a test data item list, includes: determining a feature weight of each test data item in the multiple test data items; and sorting them according to the size of the feature weights to generate a test data item list.

[0066] Optionally, the feature weight is determined by the number of test cases on which each test data item depends and the importance level in a specific dimension.

[0067] Optionally, after executing the step of extracting a first use case set that only depends on the first test data item from the serial test case set, the processor further includes: extracting a first intermediate layer use case set that not only depends on the first test data item from the serial test data set.

[0068] Optionally, the processor further executes the step of: when the first test data item is a text data item, performing clustering processing on the first intermediate layer use case set, and obtaining the first intermediate layer use case set after clustering processing as the first level use case set.

[0069] Optionally, the processor executes the step of performing clustering processing on the first intermediate layer use case set when the first test data item is a text data item to obtain the first intermediate layer use case set after clustering processing as the first level use case set, including: when the first test data item is a text data item, extracting the text data item from the first intermediate layer use case set to obtain the extracted first intermediate layer use case set; extracting at least one text data item whose data type is text from the test data set corresponding to the extracted first intermediate layer use case set; and using the at least one text data item, performing clustering processing on the extracted first intermediate layer use case set to generate a first level use case set.

[0070] The processor executes the step of performing clustering processing on the extracted first intermediate layer use case set using the at least one text data item to generate a first level use case set, including: classifying the extracted first intermediate layer use case set into multiple sub-use case sets that only depend on a single text data item in the at least one text data item and the remaining use case sets that not only depend on the at least one text data item; for each sub-use case set in the multiple sub-use case sets, determining whether the distance between the respectively dependent text data items and the first test data item meets the preset requirements; extracting the corresponding text data items from the sub-use case sets that meet the preset requirements, and converting the sub-use case set into a leaf use case set for parallel testing.

[0071] The processor also executes the steps of: determining the sub-use case set that does not meet the preset requirements and the remaining use case set as the first-level use case set; for the first-level use case set, extracting the second use case set that only depends on the second test data item and the second intermediate layer test set that not only depends on the second test data item; by extracting the second test data item from the second use case set, converting the second use case set into a leaf use case set for parallel testing.

[0072] In summary, according to the test data processing device of the exemplary embodiment of the present application, the test case set of the serial test can be converted into the case set of the parallel test, which shortens the regression time and improves the regression efficiency. Furthermore, by filtering out the completely different test data items in the test case set to obtain the serial test data set, this method can reduce the data preparation time and thus shorten the data regression time. Furthermore, the feature weights of the test data items can be used to select the processing order of the test case set, so that the test case set that the user expects to process can be preferentially processed to meet the user's customized needs for regression testing. Furthermore, duplicate test cases can be deleted by processing the text similarity in the non-leaf case set. Furthermore, the test case sets of each layer can be aggregated by calculating the distance between the text data items and the test data items in the test case set of each layer, so that the number of test cases can be reduced as much as possible and the test time can be saved. Furthermore, the method can perform various clustering operations on the text data items to better meet business needs.

[0073] It should be noted that the execution subject of each step of the method provided in Example 1 can be the same device, or the method can be executed by different devices. For example, the execution subject of step 21 and step 22 can be device 1, and the execution subject of step 23 can be device 2; for another example, the execution subject of step 21 can be device 1, and the execution subject of step 22 and step 23 can be device 2; and so on.

[0074] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0075] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0076] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0077] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0078] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0079] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0080] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0081] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements not only depends on those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0082] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0083] The above is only the embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A test data processing method, wherein: include: Screening out test cases with completely different dependent test data items from the test data set to which the test case set depends, and determining the remaining test case sets in the test case set as serial test case sets with test conflicts; Extracting a plurality of test data items that each serial test case depends on from a serial test data set that the serial test case set depends on; Determine a feature weight of each test data item in the plurality of test data items, wherein the feature weight is determined by the number of test cases on which each test data item depends and the importance level of each test case; sort the test data items according to the feature weights to generate a list of test data items; select the test data item ranked first in the list of test data items as the first test data item; Extracting a first test case set that only depends on the first test data item from the serial test case set; By extracting the first test data item from the first use case set, the first use case set is converted into a leaf use case set for parallel testing.

2. The method of claim 1, wherein: After extracting the first test case set that depends only on the first test data item from the serial test case set, the following further comprises: A first intermediate layer use case set that is not only dependent on the first test data item is extracted from the serial test data set.

3. The method of claim 2, wherein: Also includes: In the case where the first test data item is a text data item, clustering processing is performed on the first intermediate layer use case set, and the first intermediate layer use case set after clustering processing is obtained as the first level use case set.

4. The method of claim 3, wherein: When the first test data item is a text data item, clustering is performed on the first intermediate layer use case set to obtain the first intermediate layer use case set after clustering as the first level use case set, including: In the case where the first test data item is a text data item, extracting the text data item from the first intermediate layer use case set to obtain the extracted first intermediate layer use case set; Extracting at least one text data item whose data type is text from the test data set corresponding to the extracted first intermediate layer use case set; The at least one text data item is used to perform clustering processing on the extracted first intermediate layer use case set to generate a first level use case set.

5. The method of claim 4, wherein: Using the at least one text data item to perform clustering processing on the extracted first intermediate layer use case set to generate a first level use case set includes: Classifying the extracted first intermediate layer use case set into a plurality of sub-use case sets that depend only on a single text data item in the at least one text data item and a remaining use case set that does not depend only on the at least one text data item; For each of the plurality of subset use case sets, determining whether a distance between the text data item on which the respective dependency is applied and the first test data item meets a preset requirement; Corresponding text data items are extracted from the sub-use case set that meets the preset requirements, and the sub-use case set is converted into a leaf use case set for parallel testing.

6. The method of claim 5, wherein: Also includes: Determine the sub-use case sets that do not meet the preset requirements and the remaining use case sets as first-level use case sets; For the first level use case set, extract a second use case set that depends only on the second test data item and a second intermediate layer test set that does not depend only on the second test data item; The second use case set is converted into a leaf use case set for parallel testing by extracting the second test data item from the second use case set.

7. A test data processing device, wherein: include: processor; as well as A memory arranged to store computer executable instructions which, when executed, cause the processor to perform the method of any one of claims 1 to 6.

8. A computer-readable storage medium having computer instructions stored thereon, wherein: When the instructions are executed, the method of any one of claims 1 to 6 is implemented.

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