Method, system and equipment for detecting manual test execution time

Abstract

The embodiment of the invention relates to the technical field of testing, and discloses a manual test execution time detection method and system and detection device.According to the detection method, after a tester executes a manual test in test software, a plurality of sets of sample data are obtained, then feature data are obtained based on processing of the sample data and grouped, and the manual test execution time is obtained; the method comprises the following steps: firstly, constructing a two-type window filter to screen out idle data in sample data, then constructing a two-type window filter to screen out periodic data in the sample data, and finally, determining manual test execution time based on the sample data after the idle data and the periodic data are filtered out. According to the detection method provided by the embodiment of the invention, the manual test execution time can be automatically detected, and the detection result is more accurate and reliable compared with a manual estimation mode.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of testing technology, and in particular to a method and system for detecting execution time of a manual test, and a detection device. Background Art

[0002] With the development of software technology, various types of software have appeared on the market. Before various types of software are officially put on the application software or application store, testers usually need to conduct multiple tests to detect whether there are any problems with the test software, so as to minimize program errors that occur after the software is installed on the user's terminal.

[0003] In the process of implementing the embodiments of the present invention, the inventors found that when testers currently perform manual testing on application software that needs to be tested, the testers usually count the actual execution time of the manual testing by themselves or estimate it based on experience. The test execution time obtained by this manual estimation method is usually inaccurate and unreliable. Summary of the invention

[0004] The embodiments of the present application provide a method and system for detecting the execution time of a manual test, and a detection device.

[0005] The purpose of the embodiment of the present invention is achieved through the following technical solutions:

[0006] To solve the above technical problems, in the first aspect, an embodiment of the present invention provides a method for detecting the execution time of a manual test, comprising: obtaining several groups of sample data after the tester performs a manual test in the test software; obtaining feature data based on the sample data; grouping the several groups of sample data based on the feature data to filter out idle data in the sample data; constructing a two-type window filter to filter out periodic data in the sample data; and determining the execution time of the manual test based on the sample data after filtering out the idle data and the periodic data.

[0007] In some embodiments, obtaining feature data based on the sample data processing includes: generating feature values ​​based on the sample data, and obtaining the number of code lines covered by the feature values ​​and the number of times the feature values ​​appear.

[0008] In some embodiments, the idle data is coverage data generated by the test software when it is idle; the periodic data is coverage data generated when the test software executes a scheduled task.

[0009] In some embodiments, the grouping of the several groups of sample data based on the feature data to determine the idle data in the sample data includes: dividing the number of code lines covered by the feature value by the number of times the feature value appears to determine the ratio of the occurrence of the idle data; and filtering out the idle data in the sample data based on the ratio.

[0010] In some embodiments, the construction of two types of window filters to filter out periodic data in the sample data includes: setting a type I window filter and a type II window filter in combination with the idle data, wherein the execution period of the type II window filter spans a sampling time; filtering the several groups of sample data through the type I window filter to obtain a first sample; filtering the several groups of sample data through the type II window filter and merging them to obtain a second sample; based on the similarity between the first sample and the second sample, determining the sample data that can be classified as the same category and the number thereof; if the number of samples of the same category of sample data exceeds a preset number, determining the same category of sample data as periodic data.

[0011] In some embodiments, based on the similarity between the first sample and the second sample, determining the sample data that can be classified as the same category and the number thereof includes: obtaining the intersection of the first sample and the second sample; calculating the proportion of the intersection in the first sample to obtain the similarity between the first sample and the second sample; if the similarity is greater than a preset threshold, classifying the first sample and the second sample as sample data of the same category.

[0012] In some embodiments, after the tester performs manual testing in the test software, several groups of sample data are obtained, including: inserting a preset probe into the test software to complete the insertion; after the tester performs manual testing in the test software after the insertion is completed, collecting the coverage data of the test software at preset time intervals to obtain the several groups of sample data.

[0013] In some embodiments, determining the manual test execution time based on the sample data after filtering out the idle data and the periodic data includes: determining the executed code generated by the tester when performing the test in the sample data after filtering out the idle data and the periodic data; and determining the test execution time based on the execution time interval of the executed code.

[0014] To solve the above technical problems, in a second aspect, an embodiment of the present invention provides a detection device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method described in the first aspect above.

[0015] To solve the above technical problems, in the third aspect, an embodiment of the present invention further provides a detection system for manual test execution time, comprising: a test terminal and a detection device as described in the second aspect, the test terminal being communicatively connected to the detection device, and test software being installed in the test terminal.

[0016] Compared with the prior art, the beneficial effects of the present invention are: different from the prior art, the embodiments of the present invention provide a method, system and detection equipment for detecting the execution time of a manual test. The detection method can obtain several groups of sample data after the tester performs a manual test in the test software, and then obtain characteristic data based on the sample data processing and group them to filter out idle data in the sample data, and then construct a two-type window filter to filter out periodic data in the sample data, and finally determine the execution time of the manual test based on the sample data after filtering out the idle data and the periodic data. The detection method provided by the embodiments of the present invention can realize automatic detection of the execution time of the manual test, and its detection result is more accurate and reliable than the manual estimation method. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] One or more embodiments are exemplarily described by pictures in the corresponding drawings, and these exemplified descriptions do not constitute limitations on the embodiments. Elements / modules and steps with the same reference numerals in the drawings are represented as similar elements / modules and steps. Unless otherwise specified, the figures in the drawings do not constitute proportional limitations.

[0018] Figure 1 It is a flow chart of a method for detecting execution time of a manual test provided by an embodiment of the present invention;

[0019] Figure 2 It is an example diagram of several groups of sample data collected according to an embodiment of the present invention;

[0020] Figure 3 yes Figure 1 A schematic diagram of a sub-flow of step S110 in the method for detecting the execution time of a manual test is shown;

[0021] Figure 4 It is an example diagram of feature data obtained by processing a set of sample data provided by an embodiment of the present invention;

[0022] Figure 5 It is an example diagram of feature data obtained by processing several groups of sample data provided by an embodiment of the present invention;

[0023] Figure 6 This is an example diagram of several groups of sample data after grouping provided by an embodiment of the present invention;

[0024] Figure 7 yes Figure 1 A schematic diagram of a sub-flow of step S130 in the method for detecting the execution time of a manual test is shown;

[0025] Figure 8 yes Figure 1 A schematic diagram of a sub-flow of step S140 in the method for detecting the execution time of a manual test is shown;

[0026] Fig. 9 is an example diagram of a type I window filter and a type II window filter provided by an embodiment of the present invention;

[0027] Fig.10 yes Figure 8 A schematic diagram of a sub-flow of step S144 in the method for detecting the execution time of a manual test is shown;

[0028] Fig.11 yes Figure 1 A schematic diagram of a sub-flow of step S150 in the method for detecting the execution time of a manual test is shown;

[0029] Fig.12 It is a structural schematic diagram of a detection system for manual test execution time provided by an embodiment of the present invention;

[0030] Fig.13 is a schematic diagram of the hardware structure of a detection device provided by an embodiment of the present invention;

[0031] Fig.14 It is a structural schematic diagram of a detection device for manual test execution time provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0032] The present invention is described in detail below in conjunction with specific embodiments. The following embodiments will help those skilled in the art to further understand the present invention, but are not intended to limit the present invention in any form. It should be noted that, for those of ordinary skill in the art, several variations and improvements may be made without departing from the concept of the present invention. These all belong to the protection scope of the present invention.

[0033] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0034] It should be noted that, if there is no conflict, the various features in the embodiments of the present invention can be combined with each other, all within the scope of protection of this application. In addition, although the functional modules are divided in the device schematic diagram and the logical order is shown in the flow chart, in some cases, the steps shown or described may be performed in a sequence different from the module division in the device or the flow chart. In addition, the words "first", "second", etc. used herein do not limit the data and execution order, but only distinguish the same items or similar items with basically the same functions and effects.

[0035] Unless otherwise defined, all technical and scientific terms used in this specification have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in this specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

[0036] In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0037] In order to solve the problem of low accuracy and reliability of the current method of manually estimating test execution time in manual testing processes, an embodiment of the present invention provides a method and system for detecting manual test execution time, and a detection device, which can automatically detect the manual test execution time after analysis and screening based on sample data, and its detection result is more accurate and reliable than the manual estimation method.

[0038] Specifically, the embodiments of the present invention are further described below in conjunction with the accompanying drawings.

[0039] The present invention provides a method for detecting the execution time of a manual test. Figure 1 , which shows the process of a method for detecting the execution time of a manual test provided by an embodiment of the present invention, the method for detecting the execution time of a manual test includes but is not limited to the following steps:

[0040] Step S110: After the tester performs manual testing in the test software, several groups of sample data are obtained;

[0041] In the embodiment of the present invention, the tester can perform manual testing on the test software that needs to perform the test work, and collect sample data obtained during the test process, such as Figure 2As shown in FIG. 1 , an example of several groups of sample data collected by an embodiment of the present invention is shown. Figure 3 , which shows Figure 1 A sub-process of step S110 in the method for detecting the execution time of manual testing is shown, wherein after the tester executes the manual test in the test software, several groups of sample data are obtained, including:

[0042] Step S111: inserting a preset probe into the test software to complete the plugging;

[0043] Before the tester performs the test work, the test software needs to meet certain pre-requisites before the test can be performed, so as to collect sample data that can be used to calculate the test execution time. The pre-requisites are as follows: first, the test software needs to be properly instrumented to ensure that the test software inserts probes while maintaining the original logical integrity, that is, inserts specific code segments as feature functions, and then obtains and analyzes the data flow information of the program to achieve the test purpose and complete the execution of the test work.

[0044] Step S112: After the tester performs manual testing in the test software after the instrumentation is completed, the coverage data of the test software is collected at preset time intervals to obtain the plurality of groups of sample data.

[0045] After the instrumentation is completed, the tester can perform manual testing in the test software after the instrumentation is completed, and after the execution instruction is input, collect the coverage data of the test software as sample data at a preset time interval. The preset time interval can be set according to the actual test scenario, for example, Figure 2 In the example shown, coverage data is collected every minute. Figure 2 As shown, the coverage data is the code executed by the test software in the time interval, that is, it includes the file path line number and its coverage status, where "covered" means that a line in a file is executed in the interval time, and "missed" means that it is not executed.

[0046] Step S120: obtaining feature data based on the sample data processing;

[0047] After obtaining the plurality of groups of sample data having the same time interval and obtained at different time points, since the sample data at different time series are Figure 2 The sample data shown contains a lot of information and is not easy to process. Therefore, the sample data can be further abstracted. Specifically, the feature data obtained based on the sample data includes: generating feature values ​​based on the sample data, and obtaining the number of code lines covered by the feature values ​​and the number of times the feature values ​​appear. The feature data of a sample data obtained after abstraction processing is as follows: Figure 4 As shown, it includes the eigenvalues ​​and the number of rows covered by each eigenvalue; and the characteristic data of the several groups of sample data at different time series obtained after further statistics are as follows Figure 5 As shown, it includes the feature value of each time point in the time interval and the number of rows covered by the feature value. The number of rows covered by the feature value refers to the number of code rows covered by the same feature value in the same set of sample data.

[0048] Step S130: grouping the plurality of groups of sample data based on the characteristic data to filter out idle data in the sample data;

[0049] The idle data is the coverage data generated by the test software when it is idle. In the embodiment of the present invention, when the tester is performing manual testing before and after, that is, when there is no one operating, the test software will also generate coverage data when it is not closed. However, these coverage data are not generated by the tester when performing manual testing. Therefore, it is necessary to exclude such idle sample data. Before filtering out such idle data, it is necessary to group the several groups of sample data to obtain the following: Figure 6 The grouped feature data shown in FIG. 1 , where sample data with the same feature value are grouped together, that is, Figure 6 As shown, samples 1, 2, and 3 with eigenvalue 1 are grouped together, samples 5, 9, and 11 with eigenvalue 2 are grouped together, samples 29, 35, 44, and 45 with eigenvalue 3 are grouped together, and sample 8 with eigenvalue 4 is grouped together alone. For details, see Figure 7 , which shows Figure 1 A sub-process of step S130 in the method for detecting the execution time of a manual test, wherein the plurality of groups of sample data are grouped based on the feature data to determine idle data in the sample data, includes:

[0050] Step S131: Divide the number of code lines covered by the feature value by the number of times the feature value appears to determine the occurrence ratio of the idle data;

[0051] Step S132: filtering out idle data in the sample data based on the ratio.

[0052] In the actual testing process, the spatial data is characterized by a large number and a small number of covered code lines, and different applications generate different coverage data when idle. The number of code lines covered by the feature value is divided by the number of times the feature value appears, and the resulting ratio is used to screen the idle data, which can obtain relatively accurate results.

[0053] Step S140: constructing two types of window filters to filter out periodic data in the sample data;

[0054] The periodic data is the coverage data generated when the test software executes the scheduled task. In the embodiment of the present invention, when the tester performs manual testing, the test software usually also executes the scheduled task. The scheduled task may be application-defined or user-set, and its characteristics are periodic, automatic operation, and the period is unknown. Obviously, this type of periodic data is not generated by the tester during manual testing, and it is necessary to filter this type of periodic data by constructing a specific two-type window filter. For details, please refer to Figure 8 , which shows Figure 1 A sub-process of step S140 in the method for detecting the execution time of the manual test, wherein the two-type window filter is constructed to filter out the periodic data in the sample data, includes:

[0055] Step S141: setting a type I window filter and a type II window filter in combination with the idle data, wherein the execution period of the type II window filter spans the sampling time;

[0056] In the embodiment of the present invention, the constructed type I window filter and type II window filter can be as follows: Fig. 9 As shown, the difference between the two lies in whether they span the sampling time, wherein the Type I window filter is set so that the execution cycle does not span the sampling time, and the Type II window filter is set so that the execution cycle spans the sampling time.

[0057] Step S142: filtering the plurality of groups of sample data through the I-type window filter to obtain a first sample;

[0058] like Fig. 9 As shown, the idle data is set as the filtering condition of the Type I window filter and the Type II window filter, and the idle sample data cannot pass through the Type I window filter and the Type II window filter. Therefore, the samples filtered out by the Type I window filter and the Type II window filter are possible periodic samples, and the first sample screened out by the Type I window filter is a possible periodic sample that satisfies both not crossing the sampling time and being non-idle data.

[0059] Step S143: filtering the plurality of groups of sample data through the type II window filter and merging them to obtain a second sample;

[0060] Similar to step S142, the second sample screened out by the type II window filter is a possible periodic sample that satisfies both the crossing of the sampling time and non-idle data, and there is, since the type II window filter is set to cross the sampling time, and since the periodic sample appears periodically, it must be repeated in the type II window filter, so it is necessary to merge the screened possible periodic samples to obtain the second sample. Among them, the possible periodic sample refers to a possible periodic sample, which also contains non-periodic sample data, such as sample data of the executed manual test task.

[0061] Step S144: based on the similarity between the first sample and the second sample, determine the sample data that can be classified into the same category and the number thereof;

[0062] In the embodiment of the present invention, since the periodic sample appears periodically, it must appear repeatedly in the second sample and the first sample. Therefore, based on the similarity between the first sample and the second sample, the sample data that can be classified into the same category and the number thereof can be determined. Fig.10 , which shows Figure 8 A sub-process of step S144 in the method for detecting the execution time of the manual test, wherein the determining of the sample data that can be classified into the same category and the number thereof based on the similarity between the first sample and the second sample includes:

[0063] Step S1441: obtaining the intersection of the first sample and the second sample;

[0064] Specifically, the lines covered by the code of the first sample can be regarded as set A, and the lines covered by the code of the second sample can be regarded as set B. By obtaining the intersection of set A and set B, the same sample data therein can be obtained.

[0065] Step S1442: calculating the proportion of the intersection in the first sample to obtain the similarity between the first sample and the second sample;

[0066] Since the intersection of set A and set B, that is, the same sample data in the first sample and the second sample may not be a periodic sample, it is also necessary to determine whether it is a periodic sample through a ratio. Specifically, the proportion of the intersection of set A and set B in set A can be calculated as the similarity for the next step of judgment.

[0067] Step S1443: If the similarity is greater than a preset threshold, classifying the first sample and the second sample as sample data of the same category.

[0068] If the proportion of the intersection of set A and set B in set A, that is, the similarity, is greater than a preset threshold, then set A and set B are considered similar, and all sample data with similarities greater than the preset threshold are classified into the same group; if the similarity is less than or equal to the preset threshold, then set A and set B are considered not similar. The preset threshold can be set according to actual needs, for example, set to 90%.

[0069] Step S145: If the number of samples of the same type of sample data exceeds a preset number, it is determined that the same type of sample data is periodic data.

[0070] Since periodic data will appear regularly and repeatedly, the number of samples of the sample data classified into the same group can be counted, and it can be determined whether the number of samples of the sample data classified into the same group exceeds a preset number. If so, it can be identified as periodic data, otherwise it can be excluded.

[0071] Step S150: determining the manual test execution time based on the sample data after filtering out the idle data and the periodic data.

[0072] Finally, after filtering out the idle data and periodic data, the execution time of the manual test can be determined based on the remaining sample data. For details, see Fig.11 , which shows Figure 1 A sub-process of step S150 in the method for detecting the execution time of a manual test, wherein the manual test execution time is determined based on the sample data after filtering out the idle data and the periodic data, comprises:

[0073] Step S151: determining the executed code generated by the tester when performing the test in the sample data after filtering out the idle data and the periodic data;

[0074] Step S152: Determine the test execution time based on the execution time interval of the executed code.

[0075] In the embodiment of the present invention, the actual manual test execution time can be finally determined according to the running time of the function code executed in the sample data after the instrumentation and after the idle data and the periodic data are filtered out.

[0076] The embodiment of the present invention also provides a detection system for manual test execution time, see Fig.12 , which shows the structure of a detection system 1 for artificial test execution time provided by an embodiment of the present invention, the detection system 1 for artificial test execution time comprises: a detection device 10 and a test terminal 20, wherein the test terminal 20 is communicatively connected to the detection device 10, and the test terminal 20 has test software installed therein.

[0077] In the embodiment of the present invention, the detection device 10 can execute the detection method of manual test execution time as described in the above embodiment, and realize the testing of the test software installed in the test terminal 20, that is, the test application program, and the detection of manual test execution time.

[0078] The detection device 10 and the test terminal 20 can be any electronic devices, such as a mobile phone, a tablet, a computer, etc. Furthermore, the detection device 10 and the test terminal 20 can also be the same electronic device, which can be selected according to the actual application scenario.

[0079] The present invention also provides a detection device, see Fig.13 , which shows that it is possible to execute Figures 1 to 11 The hardware structure of the detection device of the method for detecting the execution time of the artificial test. The detection device 10 can be Fig.12 The detection device 10 is shown.

[0080] The detection device 10 includes: at least one processor 11; and a memory 12 connected to the at least one processor 11 for communication. Fig.13 The memory 12 stores instructions that can be executed by the at least one processor 11, and the instructions are executed by the at least one processor 11 so that the at least one processor 11 can perform the above Figures 1 to 11 The processor 11 and the memory 12 may be connected via a bus or other means. Fig.13 The example of connecting through bus is taken in the following.

[0081] The memory 12 is a non-volatile computer-readable storage medium that can be used to store non-volatile software programs, non-volatile computer executable programs and modules, such as program instructions / modules corresponding to the method for detecting the execution time of manual testing in the embodiment of the present application. The processor 11 executes various functional applications and data processing of the detection device by running the non-volatile software programs, instructions and modules stored in the memory 12, that is, the method for detecting the execution time of manual testing in the above method embodiment is realized.

[0082] The memory 12 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and application programs required for at least one function; the data storage area may store data created according to the use of the detection device for the execution time of the manual test, etc. In addition, the memory 12 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some embodiments, the memory 12 may optionally include a memory remotely arranged relative to the processor 11, and these remote memories may be connected to the detection device for the execution time of the manual test via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0083] The one or more modules are stored in the memory 12, and when executed by the one or more processors 11, execute the method for detecting the execution time of the manual test in any of the above method embodiments, for example, execute the above described Figures 1 to 11 method steps.

[0084] The above-mentioned product can execute the method provided in the embodiment of the present application, and has the functional modules and beneficial effects corresponding to the execution method. For technical details not fully described in this embodiment, please refer to the method provided in the embodiment of the present application.

[0085] The present application also provides a non-volatile computer-readable storage medium, wherein the computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are executed by one or more processors, for example, to execute the above-described Figures 1 to 11 method steps.

[0086] The present application also provides a computer program product, including a computer program stored on a non-volatile computer-readable storage medium, wherein the computer program includes program instructions. When the program instructions are executed by a computer, the computer executes the method for detecting the execution time of a manual test in any of the above method embodiments, for example, executing the above described Figures 1 to 11 method steps.

[0087] The embodiment of the present invention provides a detection device 200 for the execution time of an artificial test, see Fig.14 , which shows a structural block diagram of a detection device 200 for artificial test execution time provided by an embodiment of the present invention, wherein the detection device 200 for artificial test execution time includes: a sampling unit 210, a processing unit 220, a first screening unit 230, a second screening unit 240 and a determination unit 250.

[0088] The sampling unit 210 is used to obtain several groups of sample data after the tester performs manual testing in the test software. The sampling unit 210 is also used to insert a preset probe into the test software to complete the plugging; after the tester performs manual testing in the test software after the plugging is completed, the coverage data of the test software is collected at preset time intervals to obtain the several groups of sample data.

[0089] The processing unit 220 is used to process the sample data to obtain feature data. The processing unit 220 is also used to generate feature values ​​based on the sample data, and obtain the number of code lines covered by the feature value and the number of times the feature value appears.

[0090] The first screening unit 230 is used to group the plurality of groups of sample data based on the feature data to screen out idle data in the sample data. The idle data is coverage data generated by the test software when it is idle. The first screening unit 230 is also used to divide the number of lines of code covered by the feature value by the number of times the feature value appears to determine the ratio of the idle data; and screen out the idle data in the sample data based on the ratio.

[0091] The second screening unit 240 is used to construct two types of window filters to filter out periodic data in the sample data. The periodic data is the coverage data generated when the test software executes the timing task. The second screening unit 240 is also used to set a type I window filter and a type II window filter in combination with the idle data, wherein the execution cycle of the type II window filter spans the sampling time; the several groups of sample data are screened by the type I window filter to obtain a first sample; the several groups of sample data are screened and merged by the type II window filter to obtain a second sample; based on the similarity between the first sample and the second sample, the sample data and the number thereof that can be classified as the same class are determined; if the number of samples of the same class of sample data exceeds a preset number, the sample data of the same class is determined to be periodic data. The second screening unit 240 is also used to obtain the intersection of the first sample and the second sample; calculate the proportion of the intersection in the first sample to obtain the similarity between the first sample and the second sample; if the similarity is greater than a preset threshold, the first sample and the second sample are classified as sample data of the same class.

[0092] The determination unit 250 is used to determine the manual test execution time based on the sample data after filtering out the idle data and the periodic data. The determination unit 250 is also used to determine the executed code generated by the tester when performing the test in the sample data after filtering out the idle data and the periodic data; and determine the test execution time based on the execution time interval of the executed code.

[0093] A method and system for detecting the execution time of a manual test, as well as a detection device, are provided in an embodiment of the present invention. The detection method can obtain several groups of sample data after the tester performs a manual test in the test software, and then obtain characteristic data based on the sample data and group them to filter out idle data in the sample data, and then construct a two-type window filter to filter out periodic data in the sample data, and finally determine the execution time of the manual test based on the sample data after filtering out the idle data and the periodic data. The detection method provided in the embodiment of the present invention can realize automatic detection of the execution time of the manual test, and its detection result is more accurate and reliable than the manual estimation method.

[0094] It should be noted that the device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, and may be located in one place or distributed over multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0095] Through the description of the above implementation methods, ordinary technicians in this field can clearly understand that each implementation method can be implemented by means of software plus a general hardware platform, and of course, it can also be implemented by hardware. Ordinary technicians in this field can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing related hardware through a computer program, and the program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, the storage medium can be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM), etc.

[0096] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them. Under the concept of the present invention, the technical features in the above embodiments or different embodiments may also be combined, the steps may be implemented in any order, and there are many other changes in different aspects of the present invention as described above, which are not provided in detail for the sake of simplicity. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for detecting execution time of manual testing, characterized in that: include: After the tester performs manual testing in the test software, several sets of sample data are obtained; Obtaining feature data based on the sample data processing; Grouping the plurality of groups of sample data based on the characteristic data to filter out idle data in the sample data; Constructing two types of window filters to filter out periodic data in the sample data; The manual test execution time is determined based on the sample data after filtering out the idle data and the periodic data.

2. The method for detecting the execution time of a manual test according to claim 1, characterized in that: The step of obtaining characteristic data based on the sample data processing includes: Based on the sample data, a feature value is generated, and the number of code lines covered by the feature value and the number of times the feature value appears are obtained.

3. The method for detecting the execution time of a manual test according to claim 2, characterized in that: The idle data is coverage data generated by the test software itself when it is idle; The periodic data is the coverage data generated when the test software executes the scheduled task.

4. The method for detecting the execution time of a manual test according to claim 2, characterized in that: The step of grouping the plurality of groups of sample data based on the feature data to determine idle data in the sample data includes: Divide the number of code lines covered by the feature value by the number of times the feature value appears to determine the ratio of the idle data appearance; Idle data in the sample data is filtered out based on the ratio.

5. The method for detecting the execution time of a manual test according to claim 4, characterized in that: The constructing of two types of window filters to filter out periodic data in the sample data includes: Setting a type I window filter and a type II window filter in combination with the idle data, wherein an execution period of the type II window filter spans a sampling time; Filtering the plurality of groups of sample data through the I-type window filter to obtain a first sample; Filtering the plurality of groups of sample data through the type II window filter and merging them to obtain a second sample; Based on the similarity between the first sample and the second sample, determine the sample data that can be classified into the same category and the number thereof; If the number of samples of the same type of sample data exceeds a preset number, it is determined that the same type of sample data is periodic data.

6. The method for detecting the execution time of a manual test according to claim 5, characterized in that: The determining, based on the similarity between the first sample and the second sample, sample data that can be classified into the same category and the number thereof includes: Obtaining an intersection of the first sample and the second sample; Calculating the proportion of the intersection in the first sample to obtain the similarity between the first sample and the second sample; If the similarity is greater than a preset threshold, the first sample and the second sample are classified as sample data of the same category.

7. The method for detecting the execution time of a manual test according to any one of claims 1 to 6, characterized in that: After the tester performs manual testing in the test software, several groups of sample data are obtained, including: Inserting a preset probe into the test software to complete the instrumentation; After the test personnel perform manual testing in the test software after the instrumentation is completed, the coverage data of the test software is collected at preset time intervals to obtain the several groups of sample data.

8. The method for detecting the execution time of a manual test according to any one of claims 1 to 6, characterized in that: The determining of the manual test execution time based on the sample data after filtering out the idle data and the periodic data includes: Determine the executed code generated by the tester when performing the test in the sample data after filtering out the idle data and the periodic data; Determine the test execution time based on the execution time interval of the executed code.

9. A detection device, characterized in that: include: at least one processor; as well as, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 8.

10. A system for detecting execution time of manual tests, characterized in that: include: A test terminal and a detection device as claimed in claim 9, wherein the test terminal is communicatively connected with the detection device and test software is installed in the test terminal.

Images