Method and device for quantifying similarity of tester's thinking

Abstract

The present invention discloses a method and device for quantifying the similarity of tester thinking, the method comprising: classifying the first-round defects measured by each tester in the first round, and calculating the defect degree coefficient of the first-round defects after classification; calculating the initial test thinking classification metric value of each tester under the corresponding test thinking category; calculating the revised test thinking classification metric value of each tester under the corresponding test thinking category; calculating the test thinking classification metric value of each tester under the corresponding test thinking category, and calculating the standardized test thinking classification metric value of each tester under the corresponding test thinking category; calculating the test thinking similarity value between each tester. The present invention can effectively clarify the differences in test technology of testers, and thus effectively solves the problems of improving the thinking ability of testers, combining different testers into teams, and cultivating test teams, thereby achieving the effect of improving the probability of project defect discovery and testing efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of computer software testing, and in particular to a method and device for quantifying the similarity of thinking of testers. Background Art

[0002] In the prior art, when evaluating the similarity of testers' thinking based on test defects, the test thinking of each tester is generally analyzed separately. However, the elements of this analysis method are relatively complex and not easy to be quantified in the actual analysis process. Therefore, the original analysis method cannot quantify the similarity of testers' test thinking, cannot effectively clarify the differences in testers' test technology, and cannot solve the problems of improving testers' thinking ability, combining different testers into teams, and training test teams, thereby affecting the probability of defect discovery and test efficiency.

[0003] It should be noted that this section is intended to provide a background or context to the embodiments of the present disclosure as recited in the claims. No admission is made that the description herein is prior art by virtue of its inclusion in this section. Summary of the invention

[0004] The embodiment of the present invention provides a method and device for quantifying the similarity of testers' thinking, so as to solve the problems in the prior art of separately analyzing the test thinking of each tester, such as complex analysis factors and inability to quantify the similarity of the test thinking of testers.

[0005] In a first aspect, an embodiment of the present invention provides a method for quantifying the similarity of thinking of testers, comprising:

[0006] According to the preset test thinking categories, the first-round defects detected by each tester in the first round are classified, and the defect degree coefficients of the classified first-round defects are calculated;

[0007] By summing up the defect degree coefficients of the first round of defects under each test thinking category, the initial test thinking classification measurement value of each tester under the corresponding test thinking category is calculated;

[0008] According to the re-round defects detected by each tester in the re-round, the modified test thinking classification measurement value of each tester under the corresponding test thinking category is calculated;

[0009] According to the initial test thinking classification metric value and the revised test thinking classification metric value, the test thinking classification metric value of each tester under the corresponding test thinking category is calculated, and the standardized test thinking classification metric value of each tester under the corresponding test thinking category is calculated;

[0010] According to the standardized test thinking classification metric value, the test thinking similarity value between each tester is calculated through the Euclidean distance model.

[0011] As a preferred embodiment of the first aspect of the present invention, the defect degree coefficient of the first round of defects after classification is calculated, including:

[0012] Obtain the severity values ​​and user impact values ​​of the first round of defects after classification;

[0013] The defect degree coefficient of the first-round defects after classification is calculated by multiplying the severity value of the first-round defects after classification and the user use impact value.

[0014] As a preferred embodiment of the first aspect of the present invention, the method of calculating the corrected test thinking classification metric value of each tester under the corresponding test thinking category by correcting each test thinking classification metric value includes:

[0015] According to the preset test thinking categories, the re-round defects detected by each tester are classified, and the defect degree coefficient of the classified re-round defects is calculated;

[0016] By summing up the defect degree coefficients of the re-round defects under each test thinking category, the modified test thinking classification metric value of each tester under the corresponding test thinking category is calculated.

[0017] As a preferred embodiment of the first aspect of the present invention, the defect degree coefficient of the classified re-round defects is calculated, including:

[0018] Obtain the difficulty coefficient of the re-round defect, as well as the severity value and user use impact value of the classified re-round defect;

[0019] The defect degree coefficient of the classified re-round defect is calculated by multiplying the re-round defect round difficulty coefficient and the severity value and user use impact value of the classified re-round defect.

[0020] As a preferred embodiment of the first aspect of the present invention, the test thinking classification metric value of each tester under the corresponding test thinking category is calculated according to the initial test thinking classification metric value and the revised test thinking classification metric value, and the standardized test thinking classification metric value of each tester under the corresponding test thinking category is calculated, including:

[0021] Calculate the test thinking classification metric value of each tester under the corresponding test thinking category by summing the initial test thinking classification metric value and the revised test thinking classification metric value;

[0022] By calculating the ratio of each test thinking classification metric value to the sum of the test thinking classification metric values ​​under all test thinking categories, the standardized test thinking classification metric value of each tester under the corresponding test thinking category is calculated.

[0023] In a second aspect, an embodiment of the present invention provides a device for quantifying the similarity of a tester's thinking, including:

[0024] A defect degree coefficient calculation unit is used to classify the first-round defects detected by each tester in the first round according to a preset test thinking category, and calculate the defect degree coefficient of the classified first-round defects;

[0025] An initial test thinking classification metric value calculation unit, used to calculate the initial test thinking classification metric value of each tester under the corresponding test thinking category by summing the defect degree coefficients of the first round of defects under each test thinking category;

[0026] A modified test thinking classification metric value calculation unit is used to calculate the modified test thinking classification metric value of each tester under the corresponding test thinking category according to the re-round defects measured by each tester in the re-round;

[0027] A standardized test thinking classification metric value calculation unit, used to calculate the test thinking classification metric value of each tester under the corresponding test thinking category according to the initial test thinking classification metric value and the revised test thinking classification metric value, and calculate the standardized test thinking classification metric value of each tester under the corresponding test thinking category;

[0028] The test thinking similarity value calculation unit is used to calculate the test thinking similarity value between each tester through the Euclidean distance model according to the standardized test thinking classification measurement value.

[0029] As a preferred embodiment of the second aspect of the present invention, the modified test thinking classification metric value calculation unit is specifically used for:

[0030] According to the preset test thinking categories, the re-round defects detected by each tester are classified, and the defect degree coefficient of the classified re-round defects is calculated;

[0031] By summing up the defect degree coefficients of the re-round defects under each test thinking category, the modified test thinking classification metric value of each tester under the corresponding test thinking category is calculated.

[0032] As a preferred embodiment of the second aspect of the present invention, the standardized test thinking classification metric value calculation unit is specifically used for:

[0033] Calculate the test thinking classification metric value of each tester under the corresponding test thinking category by summing the initial test thinking classification metric value and the revised test thinking classification metric value;

[0034] By calculating the ratio of each test thinking classification metric value to the sum of the test thinking classification metric values ​​under all test thinking categories, the standardized test thinking classification metric value of each tester under the corresponding test thinking category is calculated.

[0035] In a third aspect, an embodiment of the present invention provides an electronic device, comprising a processor and a memory, wherein the memory stores execution instructions, and the processor reads the execution instructions in the memory to execute the steps in the method for quantifying the similarity of tester thinking as described in any one of the first aspect and its preferred methods.

[0036] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, wherein the storage medium stores computer execution instructions, and the computer execution instructions are used to execute the steps in the method for quantifying the similarity of tester's test thinking as described in any one of the first aspect and its preferred embodiment.

[0037] The method and device for quantifying the similarity of testers' thinking provided by the embodiments of the present invention classify the testers' thinking according to the first-round defects measured by each tester in the first round, calculate the defect degree coefficient of the classified first-round defects, and then calculate the initial test thinking classification measurement value of each tester in the corresponding test thinking category by summing the defect degree coefficients of the first-round defects; then, the initial test thinking classification measurement value is corrected according to the second-round defects measured by each tester in the second round to obtain the corrected test thinking classification measurement value, and then the test thinking classification measurement value is calculated according to the initial test thinking classification measurement value and the corrected test thinking classification measurement value, and the standardized test thinking classification measurement value is calculated accordingly; finally, according to the standardized test thinking classification measurement value, the test thinking similarity value between each tester can be calculated through the Euclidean distance model, so as to obtain the group of people with similar thinking of the testers in the project.

[0038] The present invention quantifies the similarities of the test thinking of each tester, which can effectively clarify the differences in tester testing techniques. Therefore, it effectively solves the problems of improving the thinking ability of testers, combining different testers into teams, and cultivating test teams, thereby achieving the effect of improving the probability of project defect discovery and testing efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0040] Figure 1 A flowchart of a method for quantifying the similarity of thinking of testers provided in an embodiment of the present invention;

[0041] Figure 2 A schematic diagram of the structure of a device for quantifying the similarity of thinking of testers provided by an embodiment of the present invention;

[0042] Figure 3 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0043] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0044] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0045] Figure 1 The present invention exemplarily shows an implementation flow chart of a method for quantifying the similarity of tester thinking provided by an embodiment of the present invention. The method can quantify the similarity of tester testing thinking and can effectively clarify the differences in tester testing techniques. Therefore, the problems of improving tester thinking ability, combining different testers into teams, and training test teams are effectively solved, thereby achieving the effect of improving the probability of project defect discovery and testing efficiency.

[0046] Reference Figure 1 As shown, the method mainly includes the following steps:

[0047] Step 101: Classify the first-round defects detected by each tester in the first round according to the preset test thinking category, and calculate the defect degree coefficient of the classified first-round defects;

[0048] Step 102, by summing up the defect degree coefficients of the first round of defects under each test thinking category, calculate the initial test thinking classification measurement value of each tester under the corresponding test thinking category;

[0049] Step 103: Calculate the corrected test thinking classification metric value of each tester under the corresponding test thinking category according to the re-round defects detected by each tester in the re-round;

[0050] Step 104: Calculate the test thinking classification metric value of each tester under the corresponding test thinking category according to the initial test thinking classification metric value and the revised test thinking classification metric value, and calculate the standardized test thinking classification metric value of each tester under the corresponding test thinking category;

[0051] Step 105: Calculate the test thinking similarity values ​​between testers through the Euclidean distance model according to the standardized test thinking classification measurement value.

[0052] In step 101, before testing a test project, a source and type analysis is first performed from the aspects of technology, business, domain knowledge, characteristics (test methods, functions, usability, performance, etc.), so as to obtain a preset test thinking category for defects in the test project. The category is freely classified according to the type of software project involved and the software industry field.

[0053] For example, in this embodiment, after analysis, the preset test thinking categories finally determined include database type a, external interface type b, installation type c, interface type d, function type e, and performance type f.

[0054] Further, according to the preset test thinking categories, the first round of defects detected by each tester in a certain software testing project are classified. In this technical solution, there is no limit on the number of testers. In this embodiment, a total of three testers are set, namely X, Y and Z.

[0055] For example, according to the preset test thinking categories, the first round of defects detected by tester X are classified as follows:

[0056] There are three first-round defects for database type a, namely Xa1, Xa2, and Xa3;

[0057] There are two first-round defects with external interface type b, namely Xb1 and Xb2;

[0058] There are 1 first-round defects with installation type c, namely Xc1;

[0059] There are 1 first-round defects with interface type d, namely Xd1;

[0060] There are three first-round defects with functional type e, namely Xe1, Xe2, and Xe3;

[0061] There is 1 defect with performance type f, namely Xf1.

[0062] The classification of the first round of defects detected by tester X is shown in Table 1 below:

[0063] Table 1

[0064]

[0065] According to the preset test thinking categories, the first round of defects detected by tester Y are classified as follows:

[0066] There is one defect with database type a, namely Ya1;

[0067] There is one defect with external interface type b, namely Yb1;

[0068] There are two defects of installation type c, namely Yc1 and Yc2;

[0069] There are five defects of interface type d, namely Yd1, Yd2, Yd3, Yd4, and Yd5;

[0070] There are two defects of functional type e, namely Ye1 and Ye2;

[0071] There is 1 defect with performance type f, namely Yf1.

[0072] The classification of the first round of defects detected by tester Y is shown in Table 2 below:

[0073] Table 2

[0074]

[0075] According to the preset test thinking categories, the first round of defects detected by tester Z are classified as follows:

[0076] There are five defects of database type a, namely Za1, Za2, Za3, Za4, and Za5;

[0077] There are two defects of external interface type b, namely Zb1 and Zb2;

[0078] There are two defects of installation type c, namely Zc1 and Zc2;

[0079] There are 1 defect with interface type d, namely Zd1;

[0080] There are 4 defects of functional type e, namely Ze1, Ze2, Ze3, and Ze4;

[0081] There are two defects of performance type f, namely Zf1 and Zf2.

[0082] The classification of the first round of defects detected by tester Z is shown in Table 3 below:

[0083] Table 3

[0084]

[0085]

[0086] After classifying the first-round defects detected by each tester in the first round, the defect degree coefficient of the classified first-round defects is further calculated to quantify the severity of the defect.

[0087] In this calculation process, the HU defect assessment model is first established. Specifically, in practical applications, the HU defect assessment model consists of two defect assessment factors, namely the severity H of each defect data in the system and the user use impact U.

[0088] In an optional embodiment provided in the present application, the step of calculating the defect degree coefficient of the first round of defects after classification in step 101 can be specifically implemented according to the following steps:

[0089] 1011. Obtain severity values ​​and user impact values ​​of the first round of defects after classification.

[0090] In this step, according to the HU defect assessment model, the severity value H and the user use impact value U of the first round of defects after classification are first obtained. In the embodiment of the present invention, the principle of defect assessment factor scoring is: the severity value H and the user use impact value U have the same weight, and the two factors are scored according to specific standards, and the scoring range of each factor is 1 point to 5 points. The details are as follows:

[0091] Severity value H (1 to 5 points): Score each defect data according to its severity in the system. If the system encounters serious errors such as failure to implement key functions, system crash, data loss, memory overflow, etc., which directly cause the system or key functions to be unusable, 5 points can be directly assigned.

[0092] User usage impact value U (1 point to 5 points): Score each defect data according to the frequency of use by users. When serious errors such as key user functions being unavailable occur, 5 points can be directly given.

[0093] For example, the specific scoring criteria are shown in Table 4 below:

[0094] Table 4

[0095]

[0096]

[0097] According to the scoring criteria in Table 4 above, for each tester, the severity value and user impact value of the first round of defects after classification are obtained.

[0098] 1012. Calculate the defect degree coefficient of the first round of defects after classification by multiplying the severity value of the first round of defects after classification and the user use impact value.

[0099] In this step, each first-round defect data needs to be scored according to the defect severity value H and the user use impact value U. Preferably, in the embodiment of the present invention, the severity value H and the user use impact value U of each first-round defect are multiplied to obtain the defect degree coefficient B of each first-round defect measured by each tester.

[0100] The defect degree coefficient is determined by the following formula:

[0101] Defect degree coefficient B = severity value H × user use impact value U. That is:

[0102] B=H×U.

[0103] For example, Table 5 provides the scoring results of the severity value H and the user use impact value U of each first-round defect data. According to the scoring criteria provided in Table 4, the defect degree coefficient B of each first-round defect can be determined. For example, when H is equal to 1 and U is equal to 1, H×U is the minimum value of 1; when H is equal to 5 and U is equal to 5, H×U is the maximum value of 25.

[0104] Table 5

[0105] Severity value H (1 to 5) User usage impact value U (1 to 5) Defect degree coefficient B = H × U 5 5 25 4 4 16 3 3 9 2 2 4 1 1 1

[0106] Furthermore, according to the severity value and user usage impact value of each first-round defect, the defect degree coefficient of the classified first-round defect is calculated by multiplying the severity value and user usage impact value of the classified first-round defect.

[0107] Exemplarily, the calculated defect degree coefficients of the first round of defects after classification by each tester X, Y, and Z are shown in Tables 6, 7, and 8 below.

[0108] Table 6

[0109]

[0110] Table 7

[0111]

[0112] Table 8

[0113]

[0114]

[0115] In step 102, based on the defect degree coefficients of the first round of defects of each tester under each test thinking category calculated in the above steps, the initial test thinking classification measurement value of each tester under the corresponding test thinking category is calculated by adding the defect degree coefficients of the first round of defects under each test thinking category.

[0116] Exemplarily, the calculated initial test thinking classification metric values ​​of each tester X, Y, and Z under the corresponding test thinking category are shown in the following Tables 9, 10, and 11.

[0117] Table 9

[0118]

[0119] Table 10

[0120]

[0121]

[0122] Table 11

[0123]

[0124] Furthermore, the initial test thinking classification measurement values ​​of testers X, Y, and Z under the corresponding test thinking categories are respectively counted, as shown in Table 12 below.

[0125] Table 12

[0126]

[0127] In step 103, after determining the initial test thinking classification metric value of each tester under the corresponding test thinking category, since the quantitative calculation of the test thinking similarity of the testers based on the initial test thinking classification metric value alone will result in a large deviation, it is necessary to use the defect degree coefficient obtained by multiple rounds of testing to obtain the revised test thinking classification metric value of each tester under the corresponding test thinking category. In the specific implementation process, the specific number of rounds of testing to be performed needs to be determined based on the specific execution results, and is generally performed until no defects are found.

[0128] Exemplarily, in this embodiment, three rounds of testing were conducted for this test item, and the execution results of the first and second rounds were both found to have defects, and the execution result of the third round was no defects.

[0129] In an optional embodiment provided in the present application, step 103 may be specifically implemented according to the following steps:

[0130] 1031. According to the preset test thinking categories, the re-round defects detected by each tester in the re-round are classified, and the defect degree coefficient of the classified re-round defects is calculated.

[0131] In this step, the re-round defects detected by each tester in the test project are classified according to the preset test thinking categories. Since no defects were found in the execution results of the third round in this embodiment, the re-round refers to the second round, and the re-round defects refer to the defects detected in the second round.

[0132] After classifying the re-round defects detected by each tester in the re-round, the defect degree coefficients of the classified re-round defects are further calculated.

[0133] The defect degree coefficient of the classified re-round defects calculated in step 1031 can be specifically implemented according to the following steps:

[0134] 10311. Obtain the difficulty coefficient of the re-round defect, as well as the severity value and user use impact value of the classified re-round defect.

[0135] In this step, since defect discovery generally becomes more difficult with the increase in the number of test rounds, and according to the test situation, the scope of test cases will be adjusted when the actual defect test is carried out in the next round, including the screening of the test cases in the previous round and the addition and modification of new test cases, the first-round defect round difficulty coefficient dbpc1 is defined as 1 in this embodiment, and the subsequent defect round difficulty coefficient adopts the defect ratio of the test case execution in the next round, which is compared with the defect ratio of the test case execution in the previous round to define the defect round difficulty coefficient of the subsequent round.

[0136] Second round defect round difficulty coefficient dbpc n Determined by the following formula:

[0137]

[0138] Right now,

[0139]

[0140] For example, the previous round of use case execution defect ratio, bpc1 = the number of defects in the previous round / the number of use case executions in the previous round, the next round of use case execution defect ratio, bpc2 = the number of defects in the next round / the number of use case executions in the next round, so the defect round difficulty coefficient of the next round is If no new defects are found in the third round, the difficulty coefficient of the defect round will not be adjusted and will remain the value of the previous round.

[0141] For example, in the first round of defect testing, the number of test cases = 100, the number of defects found = 10, the first round of test case execution defect ratio bpc1 = 10 / 100 = 0.1, and the first round of defect round difficulty coefficient is defined as 1; in the second round of defect testing, the number of screened test cases = 70, the number of newly added test cases = 10, and the number of second round test case executions is 80; the number of second round defects = 8, and the second round of test case execution defect ratio is Then the difficulty coefficient of the next defect round is:

[0142]

[0143] After obtaining the difficulty coefficient of the re-round defect, the severity value and user use impact value of the classified re-round defect are obtained according to the method described in the above step 101.

[0144] 10312. The defect degree coefficient of the classified re-round defects is calculated by multiplying the re-round defect difficulty coefficient and the severity value and user use impact value of the classified re-round defects.

[0145] In this step, according to the re-round defect round difficulty coefficient and the severity value and user usage impact value of each re-round defect, the defect degree coefficient of the classified re-round defect is calculated by multiplying the re-round defect round difficulty coefficient and the severity value and user usage impact value of the classified re-round defect.

[0146] 1032. By summing up the defect degree coefficients of the re-round defects under each test thinking category, the modified test thinking classification measurement value of each tester under the corresponding test thinking category is calculated.

[0147] In this step, according to the defect degree coefficient of the re-round defects of each tester under each test thinking category calculated in the above step, the defect degree coefficient of the re-round defects under each test thinking category is added to obtain the corrected test thinking classification measurement value of each tester under the corresponding test thinking category.

[0148] Exemplarily, the calculated modified test thinking classification metric values ​​of each tester X, Y, and Z under the corresponding test thinking category are shown in Tables 13, 14, and 15 below.

[0149] Table 13

[0150]

[0151] Table 14

[0152]

[0153] Table 15

[0154]

[0155]

[0156] Furthermore, the modified test thinking classification metric values ​​of testers X, Y, and Z under the corresponding test thinking categories are statistically analyzed, as shown in Table 16 below.

[0157] Table 16

[0158]

[0159] In step 104, the test thinking classification metric value of each tester in the corresponding test thinking category is calculated based on the initial test thinking classification metric value and the modified test thinking classification metric value calculated above. Then, the test thinking classification metric value is standardized to calculate the standardized test thinking classification metric value of each tester in the corresponding test thinking category.

[0160] In an optional embodiment provided in the present application, step 104 may be specifically implemented according to the following steps:

[0161] 1041. The test thinking classification metric value of each tester in the corresponding test thinking category is calculated by summing the initial test thinking classification metric value and the revised test thinking classification metric value.

[0162] In this step, the initial test thinking classification metric value and the modified test thinking classification metric value calculated above are added together to calculate the test thinking classification metric value of each tester under the corresponding test thinking category. That is, the test thinking classification metric value tm = initial test thinking classification metric value ftm + modified test thinking classification metric value rtm.

[0163] tm=ftm+rtm.

[0164] Exemplarily, the calculated test thinking classification measurement values ​​of each tester X, Y, and Z under the corresponding test thinking category are shown in Tables 17, 18, and 19 below.

[0165] Table 17

[0166]

[0167] Table 18

[0168]

[0169] Table 19

[0170]

[0171] 1042. By calculating the ratio of each test thinking classification metric value to the sum of the test thinking classification metric values ​​under all test thinking categories, the standardized test thinking classification metric value of each tester under the corresponding test thinking category is calculated.

[0172] In this step, the test thinking classification metric value of each tester under the corresponding test thinking category is standardized, that is, each test thinking classification metric value is divided by the sum of the test thinking classification metric values ​​under all test thinking categories to calculate the standardized test thinking classification metric value of each tester under the corresponding test thinking category.

[0173] Standardized Test Thinking Classification Measure Right now:

[0174]

[0175] Exemplarily, the calculated standardized test thinking classification measurement values ​​of each tester X, Y, and Z under the corresponding test thinking category are shown in Tables 20, 21, and 22 below.

[0176] Table 20

[0177]

[0178] Table 21

[0179]

[0180] Table 22

[0181]

[0182] In step 105, the standardized test thinking classification metric values ​​calculated according to the above steps are calculated through the Euclidean distance model to obtain the test thinking similarity values ​​between the testers. According to the test thinking similarity values, the test thinking similarity between the two testers can be intuitively judged. The larger the test thinking similarity value, the greater the test thinking similarity between the two testers.

[0183] Exemplarily, the test thinking similarity value Wxy between testers X and Y is calculated, and the specific process is as follows:

[0184]

[0185] Calculate the test thinking similarity value Wxz between testers X and Z. The specific process is as follows:

[0186]

[0187] According to the basic definition of the Euclidean distance model, the greater the similarity distance of the test thinking of testers, the smaller the similarity of the test thinking of testers; conversely, the smaller the test thinking distance of testers, the greater the similarity of the test thinking of testers.

[0188] From the above calculation results, we can know that the similarity distance of test thinking between tester X and tester Y is 34.40, while the similarity distance of test thinking between tester X and tester Z is 7.36, which means that the similarity of test thinking between tester X and tester Z is higher.

[0189] Through the above methods, we understand the similarities of the testers' testing thinking, and also clarify the differences in the testers' testing techniques. First, we can conduct targeted pair testing for testers with different testing thinking to optimize the overall testing skill level of the team or customize the test team personnel for different test projects, so as to achieve the purpose of optimizing personnel allocation and improving the probability of defect discovery; secondly, for testers whose overall testing ability is weak or weak in a certain aspect, we can customize the technical improvement plan, so as to comprehensively and specifically improve the tester's testing ability, which is conducive to improving the testing efficiency.

[0190] It should be noted that, for the sake of simplicity, the embodiments of the above method are described as a series of action combinations, but those skilled in the art should know that the present invention is not limited by the described action sequence. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions involved are not necessarily required by the present invention.

[0191] To summarize, the method for quantifying the similarity of testers' thinking provided by the embodiment of the present invention classifies the testers' thinking according to the first-round defects measured by each tester in the first round, calculates the defect degree coefficient of the classified first-round defects, and then calculates the initial test thinking classification measurement value of each tester in the corresponding test thinking category by summing the defect degree coefficients of the first-round defects; then, the initial test thinking classification measurement value is corrected according to the second-round defects measured by each tester in the second round to obtain the corrected test thinking classification measurement value, and then the test thinking classification measurement value is calculated according to the initial test thinking classification measurement value and the corrected test thinking classification measurement value, and the standardized test thinking classification measurement value is calculated accordingly; finally, according to the standardized test thinking classification measurement value, the test thinking similarity value between each tester can be calculated through the Euclidean distance model, thereby obtaining the group of people with similar thinking of the testers in the project.

[0192] The present invention quantifies the similarities of the test thinking of each tester, which can effectively clarify the differences in tester testing techniques. Therefore, it effectively solves the problems of improving the thinking ability of testers, combining different testers into teams, and cultivating test teams, thereby achieving the effect of improving the probability of project defect discovery and testing efficiency.

[0193] Based on the same inventive concept, Figure 2 An apparatus for quantifying the similarity of tester thinking provided by an embodiment of the present invention is exemplarily shown. Since the principle of the apparatus for solving technical problems is similar to that of a method for quantifying the similarity of tester thinking, the specific implementation manner of the apparatus can refer to the specific implementation manner of the method, and repeated parts will not be repeated.

[0194] Reference Figure 2 As shown, the device mainly includes the following units:

[0195] The defect degree coefficient calculation unit 21 is used to classify the first-round defects measured by each tester in the first round according to the preset test thinking category, and calculate the defect degree coefficient of the classified first-round defects;

[0196] The initial test thinking classification metric value calculation unit 22 is used to calculate the initial test thinking classification metric value of each tester under the corresponding test thinking category by summing the defect degree coefficients of the first round of defects under each test thinking category;

[0197] The modified test thinking classification metric value calculation unit 23 is used to calculate the modified test thinking classification metric value of each tester under the corresponding test thinking category according to the re-round defects measured by each tester in the re-round;

[0198] A standardized test thinking classification metric value calculation unit 24 is used to calculate the test thinking classification metric value of each tester under the corresponding test thinking category according to the initial test thinking classification metric value and the revised test thinking classification metric value, and calculate the standardized test thinking classification metric value of each tester under the corresponding test thinking category;

[0199] The test thinking similarity value calculation unit 25 is used to calculate the test thinking similarity value between each tester through the Euclidean distance model according to the standardized test thinking classification measurement value.

[0200] It should be noted here that the above-mentioned defect degree coefficient calculation unit 21, initial test thinking classification metric value calculation unit 22, revised test thinking classification metric value calculation unit 23, standardized test thinking classification metric value calculation unit 24 and test thinking similarity value calculation unit 25 correspond to steps 101 to 105 in the above-mentioned method embodiment, and the five units are the same as the instances and application scenarios implemented by the corresponding steps, but are not limited to the contents disclosed in the above-mentioned method embodiment.

[0201] Preferably, the defect degree coefficient calculation unit 21 is specifically used for:

[0202] Obtain the severity values ​​and user impact values ​​of the first round of defects after classification;

[0203] The defect degree coefficient of the first-round defects after classification is calculated by multiplying the severity value of the first-round defects after classification and the user use impact value.

[0204] Preferably, the modified test thinking classification metric value calculation unit 23 is specifically used for:

[0205] According to the preset test thinking categories, the re-round defects detected by each tester are classified, and the defect degree coefficient of the classified re-round defects is calculated;

[0206] By summing up the defect degree coefficients of the re-round defects under each test thinking category, the modified test thinking classification metric value of each tester under the corresponding test thinking category is calculated.

[0207] Preferably, the modified test thinking classification metric value calculation unit 23 is further specifically used for:

[0208] Obtain the difficulty coefficient of the re-round defect, as well as the severity value and user use impact value of the classified re-round defect;

[0209] The defect degree coefficient of the classified re-round defect is calculated by multiplying the re-round defect round difficulty coefficient and the severity value and user use impact value of the classified re-round defect.

[0210] Preferably, the standardized test thinking classification metric value calculation unit 24 is specifically used for:

[0211] Calculate the test thinking classification metric value of each tester under the corresponding test thinking category by summing the initial test thinking classification metric value and the revised test thinking classification metric value;

[0212] By calculating the ratio of each test thinking classification metric value to the sum of the test thinking classification metric values ​​under all test thinking categories, the standardized test thinking classification metric value of each tester under the corresponding test thinking category is calculated.

[0213] It should be noted that the device for quantifying the similarity of tester's thinking provided in the embodiment of the present invention and the method for quantifying the similarity of tester's thinking described in the aforementioned embodiment belong to the same technical concept, and its specific implementation process can refer to the description of the method steps in the aforementioned embodiment, which will not be repeated here.

[0214] It should be understood that the units included in the above device for quantifying the similarity of the thinking of testers are only logical divisions based on the functions implemented by the device. In practical applications, the above units can be superimposed or split. And the functions implemented by the device for quantifying the similarity of the thinking of testers provided in this embodiment correspond one to one with the method for quantifying the similarity of the thinking of testers provided in the above embodiment. The more detailed processing flow implemented by the device has been described in detail in the above method embodiment and will not be described in detail here.

[0215] To summarize, the device for quantifying the similarity of tester thinking provided by the embodiment of the present invention classifies the testers' thinking according to the first-round defects measured by each tester in the first round, calculates the defect degree coefficient of the classified first-round defects, and then calculates the initial test thinking classification measurement value of each tester under the corresponding test thinking category by summing the defect degree coefficients of the first-round defects; then, the initial test thinking classification measurement value is corrected according to the second-round defects measured by each tester in the second round to obtain the corrected test thinking classification measurement value, and then the test thinking classification measurement value is calculated according to the initial test thinking classification measurement value and the corrected test thinking classification measurement value, and the standardized test thinking classification measurement value is calculated accordingly; finally, according to the standardized test thinking classification measurement value, the test thinking similarity value between each tester can be calculated through the Euclidean distance model, thereby obtaining the group of people with similar thinking of the testers in the project.

[0216] The present invention quantifies the similarities of the test thinking of each tester, which can effectively clarify the differences in tester testing techniques. Therefore, it effectively solves the problems of improving the thinking ability of testers, combining different testers into teams, and cultivating test teams, thereby achieving the effect of improving the probability of project defect discovery and testing efficiency.

[0217] Based on the same inventive concept, Figure 3 As shown, an embodiment of the present invention provides an electronic device, which mainly includes a processor 301 and a memory 302, wherein the memory 302 also stores an execution instruction. The processor 301 reads the execution instruction in the memory 302 to execute the steps described in any embodiment of the above-mentioned tester thinking similarity quantification method. Alternatively, the processor 301 reads the execution instruction in the memory 302 to implement the functions of each unit in any embodiment of the above-mentioned tester thinking similarity quantification device.

[0218] Figure 3 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention is shown in FIG. Figure 3 As shown, the device includes a processor 301 , a memory 302 and a transceiver 303 ; wherein the processor 301 , the memory 302 and the transceiver 303 communicate with each other via a bus 304 .

[0219] The bus 304 mentioned above can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, only one arrow line is used in the figure, but it does not mean that there is only one bus or one type of bus.

[0220] The memory may include a random access memory (RAM) or a non-volatile memory (NVM), such as at least one disk memory. Optionally, the memory may also be at least one storage device located away from the aforementioned processor.

[0221] The above-mentioned processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working process of each component described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0222] To summarize, the electronic device provided by the embodiment of the present invention classifies the thinking of the testers according to the first-round defects measured by each tester in the first round, calculates the defect degree coefficient of the classified first-round defects, and then calculates the initial test thinking classification measurement value of each tester in the corresponding test thinking category by summing the defect degree coefficients of the first-round defects; then, the initial test thinking classification measurement value is corrected according to the second-round defects measured by each tester in the second round to obtain the corrected test thinking classification measurement value, and then the test thinking classification measurement value is calculated according to the initial test thinking classification measurement value and the corrected test thinking classification measurement value, and the standardized test thinking classification measurement value is calculated accordingly; finally, according to the standardized test thinking classification measurement value, the test thinking similarity value between each tester can be calculated through the Euclidean distance model, thereby obtaining the group of people with similar thinking of the testers in the project.

[0223] The present invention quantifies the similarities of the test thinking of each tester, which can effectively clarify the differences in tester testing techniques. Therefore, it effectively solves the problems of improving the thinking ability of testers, combining different testers into teams, and cultivating test teams, thereby achieving the effect of improving the probability of project defect discovery and testing efficiency.

[0224] In addition, an embodiment of the present invention further provides a computer-readable storage medium, comprising computer-executable instructions, wherein the computer-executable instructions are used to execute the steps described in the embodiment of the above-mentioned method for quantifying the similarity of the thinking of testers. Alternatively, the computer-executable instructions are used to execute the functions of each unit in the embodiment of the above-mentioned device for quantifying the similarity of the thinking of testers.

[0225] The computer storage medium of the embodiment of the present invention may adopt any combination of one or more computer-readable media. The computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared or semiconductor system, device or device, or any combination of the above. More specific examples (non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable CD-ROM, an optical storage device, a magnetic storage device, or any suitable combination of the above. The computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, device or device.

[0226] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, which carry computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to: electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0227] The program code contained on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, optical cable, radio frequency (RF), etc., or any suitable combination of the above.

[0228] In addition, the computer program code for performing the operation of the present invention can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages, such as Java, Smalltalk, C++, and also conventional procedural programming languages, such as "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as an independent software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (e.g., using an Internet service provider to connect through the Internet).

[0229] Those of ordinary skill in the art will appreciate that the modules, units and / or method steps of the various embodiments described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0230] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0231] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0232] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0233] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for quantifying the similarity of tester thinking, characterized in that: include: According to the preset test thinking categories, the first-round defects detected by each tester in the first round are classified, and the defect degree coefficient of the classified first-round defects is calculated; wherein the preset test thinking categories include database type, external interface type, installation type, interface type, function type, and performance type; By summing up the defect degree coefficients of the first round of defects under each test thinking category, the initial test thinking classification measurement value of each tester under the corresponding test thinking category is calculated; According to the re-round defects detected by each tester in the re-round, the modified test thinking classification measurement value of each tester under the corresponding test thinking category is calculated; According to the initial test thinking classification metric value and the revised test thinking classification metric value, the test thinking classification metric value of each tester under the corresponding test thinking category is calculated, and the standardized test thinking classification metric value of each tester under the corresponding test thinking category is calculated; According to the standardized test thinking classification metric value, the test thinking similarity value between each tester is calculated through the Euclidean distance model.

2. The method according to claim 1, characterized in that The defect degree coefficient of the first round of defects after calculation and classification includes: Obtain the severity values ​​and user impact values ​​of the first round of defects after classification; The defect degree coefficient of the first-round defects after classification is calculated by multiplying the severity value of the first-round defects after classification and the user use impact value.

3. The method according to claim 1, characterized in that The method of calculating the corrected test thinking classification metric value of each tester under the corresponding test thinking category by correcting each test thinking classification metric value includes: According to the preset test thinking categories, the re-round defects detected by each tester are classified, and the defect degree coefficient of the classified re-round defects is calculated; By summing up the defect degree coefficients of the re-round defects under each test thinking category, the modified test thinking classification metric value of each tester under the corresponding test thinking category is calculated.

4. The method according to claim 3, characterized in that The defect degree coefficient of the re-round defects after calculation and classification includes: Obtain the difficulty coefficient of the re-round defect, as well as the severity value and user use impact value of the classified re-round defect; The defect degree coefficient of the classified re-round defect is calculated by multiplying the re-round defect round difficulty coefficient and the severity value and user use impact value of the classified re-round defect.

5. The method according to any one of claims 1 to 4, characterized in that The step of calculating the test thinking classification metric value of each tester under the corresponding test thinking category according to the initial test thinking classification metric value and the modified test thinking classification metric value, and calculating the standardized test thinking classification metric value of each tester under the corresponding test thinking category includes: Calculate the test thinking classification metric value of each tester under the corresponding test thinking category by summing the initial test thinking classification metric value and the revised test thinking classification metric value; By calculating the ratio of each test thinking classification metric value to the sum of the test thinking classification metric values ​​under all test thinking categories, the standardized test thinking classification metric value of each tester under the corresponding test thinking category is calculated.

6. A device for quantifying the similarity of a person's thinking, characterized in that: include: A defect degree coefficient calculation unit is used to classify the first-round defects detected by each tester in the first round according to a preset test thinking category, and calculate the defect degree coefficient of the classified first-round defects; wherein the preset test thinking category includes database type, external interface type, installation type, interface type, function type, and performance type; An initial test thinking classification metric value calculation unit, used to calculate the initial test thinking classification metric value of each tester under the corresponding test thinking category by summing the defect degree coefficients of the first round of defects under each test thinking category; A modified test thinking classification metric value calculation unit is used to calculate the modified test thinking classification metric value of each tester under the corresponding test thinking category according to the re-round defects measured by each tester in the re-round; A standardized test thinking classification metric value calculation unit, used to calculate the test thinking classification metric value of each tester under the corresponding test thinking category according to the initial test thinking classification metric value and the revised test thinking classification metric value, and calculate the standardized test thinking classification metric value of each tester under the corresponding test thinking category; The test thinking similarity value calculation unit is used to calculate the test thinking similarity value between each tester through the Euclidean distance model according to the standardized test thinking classification measurement value.

7. The device according to claim 6, characterized in that The modified test thinking classification metric value calculation unit is specifically used for: According to the preset test thinking categories, the re-round defects detected by each tester are classified, and the defect degree coefficient of the classified re-round defects is calculated; By summing up the defect degree coefficients of the re-round defects under each test thinking category, the modified test thinking classification metric value of each tester under the corresponding test thinking category is calculated.

8. The device according to claim 6 or 7, characterized in that The standardized test thinking classification measurement value calculation unit is specifically used for: Calculate the test thinking classification metric value of each tester under the corresponding test thinking category by summing the initial test thinking classification metric value and the revised test thinking classification metric value; By calculating the ratio of each test thinking classification metric value to the sum of the test thinking classification metric values ​​under all test thinking categories, the standardized test thinking classification metric value of each tester under the corresponding test thinking category is calculated.

9. An electronic device, characterized in that: It comprises a processor and a memory, wherein the memory stores execution instructions, and the processor reads the execution instructions in the memory to execute the steps in the method for quantifying the similarity of test personnel's test thinking as described in any one of claims 1 to 5.

10. A computer-readable storage medium, characterized in that: The storage medium stores computer-executable instructions, and the computer-executable instructions are used to execute the steps in the method for quantifying the similarity of tester thinking as described in any one of claims 1 to 5.

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