CN115858342A - Processing method and device for testing fullness degree and server - Google Patents
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Abstract
The application provides a processing method, a device and a server for testing sufficiency, which relate to the Internet technology, and the method comprises the following steps: acquiring a prediction request aiming at an object to be tested; wherein the prediction request comprises test information of the object to be tested. Performing prediction processing on the test information according to a preset regression model to obtain a predicted value; the preset regression model is obtained by training according to a plurality of historical test information and actual result information data pairs. And if the predicted value characterization test is determined to be sufficient, stopping the test processing of the object to be tested. According to the method, the test fullness degree of the application is determined through the predicted value and the operation is stopped in time, so that insufficient testing caused by too early stopping of the test and low input-output ratio and fatigue test caused by too late stopping of the test are avoided, and the technical problem that the difficulty in determining the test fullness degree of the application is high is solved.
Description
Technical Field
The present application relates to internet technologies, and in particular, to a processing method and apparatus for testing fullness level, and a server.
Background
At present, in order to ensure the quality of the application, the application needs to be tested before being put into use.
In the prior art, a tester often determines whether to stop applying the test according to subjective judgment and experience.
However, in the prior art, whether the application test is stopped or not is determined by the testers through subjective judgment and experience of the testers, and the subjective judgment depends on the experience of the testers, so that the judgment standards are different from person to person, and a uniform standard cannot be formed.
Disclosure of Invention
The application provides a processing method and device for testing fullness and a server, which are used for solving the technical problem that the difficulty in determining the testing fullness of an application is high.
In a first aspect, the present application provides a processing method for testing fullness, including:
in the process of testing an object to be tested, acquiring a prediction request of the object to be tested; wherein the prediction request comprises test information of the object to be tested;
performing prediction processing on the test information according to a preset regression model to obtain a predicted value; the preset regression model is obtained by training according to a plurality of historical test information and actual result information data pairs;
and if the predicted value characterization test is determined to be sufficient, stopping the test processing of the object to be tested.
Further, the test information is subjected to prediction processing according to a preset regression model to obtain a predicted value, which is specifically used for:
and carrying out prediction processing on the test information according to a prediction equation in a preset regression model to obtain a predicted value.
Further, the prediction equation is:
wherein x is 1 An average test case defect discovery number x of the objects to be tested in the test information 2 The average daily defect newly-added condition, x, of the objects to be tested in the test information 3 Testing the system in the test information for a proportion of severe and/or fatal defects, x 4 The ratio of the number of defects of serious and fatal grade in the test information to the total number of defects, x k For other information in the test information, w 1 、w 2 、w 3 、w 4 、w k 、v 1 、v 2 、v 3 、v 4 、v k Are all preset parameters.
Further, the method further comprises:
acquiring a historical data set, wherein the historical data set comprises a plurality of historical test information and actual result information data pairs;
training an initial model according to the historical test information and actual result information data pair based on a polynomial regression algorithm to obtain a regression model; the regression model is used for predicting the test fullness of the object to be tested according to the test information of the object to be tested.
In a second aspect, the present application provides a processing apparatus for testing fullness, comprising:
the device comprises a first obtaining unit, a second obtaining unit and a prediction unit, wherein the first obtaining unit is used for obtaining a prediction request of an object to be tested in the process of testing the object to be tested; wherein the prediction request comprises test information of the object to be tested;
the prediction unit is used for carrying out prediction processing on the test information according to a preset regression model to obtain a predicted value; the preset regression model is obtained by training according to a plurality of historical test information and actual result information data pairs;
and the stopping unit is used for stopping the test processing of the object to be tested if the predicted value characterization test is determined to be sufficient.
Further, the prediction unit is specifically configured to:
and carrying out prediction processing on the test information according to a prediction equation in a preset regression model to obtain a predicted value.
Further, the prediction equation is:
wherein x is 1 An average test case defect discovery number x of the objects to be tested in the test information 2 The average daily defect newly-added condition, x, of the objects to be tested in the test information 3 Testing the system in the test information for a proportion of severe and/or fatal defects, x 4 Is the ratio of the number of serious and fatal defects in the test information to the total number of defects, x k For other information in the test information, w 1 、w 2 、w 3 、w 4 、w k 、v 1 、v 2 、v 3 、v 4 、v k Are all preset parameters.
Further, the apparatus further comprises:
the second acquisition unit is used for acquiring a historical data set, wherein the historical data set comprises a plurality of historical test information and actual result information data pairs;
the training unit is used for training the initial model according to the historical test information and actual result information data pair based on a polynomial regression algorithm to obtain a regression model; the regression model is used for predicting the testing fullness of the object to be tested according to the testing information of the object to be tested.
In a third aspect, the present application provides a server, comprising a memory and a processor, wherein the memory stores a computer program operable on the processor, and the processor implements the method of the first aspect when executing the computer program.
In a fourth aspect, the present application provides a computer-readable storage medium having stored therein computer-executable instructions for implementing the method of the first aspect when executed by a processor.
In a fifth aspect, the present application provides a computer program product comprising a computer program which, when executed by a processor, implements the method of the first aspect.
According to the processing method, device and server for testing the sufficiency degree, a prediction request of an object to be tested is obtained in the process of testing the object to be tested; wherein the prediction request comprises test information of the object to be tested. Performing prediction processing on the test information according to a preset regression model to obtain a predicted value; the preset regression model is obtained by training according to a plurality of historical test information and actual result information data pairs. And if the predicted value characterization test is determined to be sufficient, stopping the test processing of the object to be tested. In the scheme, in the process of testing an object to be tested, a prediction request of the object to be tested is obtained, wherein the prediction request comprises test information of the object to be tested. Because the preset regression model is obtained by training according to a plurality of historical test information and actual result information data pairs, the test information is input into the preset regression model, and the output predicted value can be predicted. And judging whether the predicted value is sufficient in characterization test, and stopping the test processing of the object to be tested if the predicted value is determined to be sufficient in characterization test. Therefore, in the process of testing the object to be tested, the full degree of the applied test is determined through the predicted value and the operation is stopped in time, so that insufficient test caused by too early stopping of the test and low input-output ratio and fatigue test caused by too late stopping of the test are avoided, and the technical problem that the difficulty in determining the full degree of the applied test is high is solved.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and together with the description, serve to explain the principles of the disclosure.
Fig. 1 is a schematic flowchart of a processing method for testing fullness provided in an embodiment of the present application;
fig. 2 is a schematic flowchart of another processing method for testing fullness provided in an embodiment of the present application;
fig. 3 is a schematic structural diagram of a processing apparatus for testing fullness provided in an embodiment of the present application;
fig. 4 is a schematic structural diagram of another processing apparatus for testing a fullness level according to an embodiment of the present disclosure;
fig. 5 is a schematic structural diagram of a server according to an embodiment of the present application.
With the foregoing drawings in mind, certain embodiments of the disclosure have been shown and described in more detail below. These drawings and written description are not intended to limit the scope of the disclosed concepts in any way, but rather to illustrate the concepts of the disclosure to those skilled in the art by reference to specific embodiments.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the exemplary embodiments below are not intended to represent all implementations consistent with the present disclosure.
In one example, to ensure the quality of an application, the application needs to be tested before it is put into use. In the prior art, a tester often determines whether to stop applying the test according to subjective judgment and experience. However, in the prior art, whether the application test is stopped or not is determined by the testers through subjective judgment and experience of the testers, and the subjective judgment depends on the experience of the testers, so that the judgment standards are different from person to person, and a uniform standard cannot be formed.
The application provides a processing method, a processing device and a server for testing fullness, and aims to solve the technical problems in the prior art.
The following describes the technical solutions of the present application and how to solve the above technical problems with specific embodiments. The following several specific embodiments may be combined with each other, and details of the same or similar concepts or processes may not be repeated in some embodiments. Embodiments of the present application will be described below with reference to the accompanying drawings.
Fig. 1 is a schematic flowchart of a processing method for testing fullness provided in an embodiment of the present application, and as shown in fig. 1, the method includes:
The execution subject of the present embodiment may be a server, for example. Firstly, in the process of testing an object to be tested, a prediction request for the object to be tested is obtained, wherein the prediction request comprises test information of the object to be tested, and the test information represents test data of the object to be tested, for example, the test information comprises an average test case defect discovery number of the object to be tested, an average daily defect newly-increased condition of the object to be tested, a system test serious and/or fatal defect proportion, a proportion of a defect number at a serious and fatal level to a total defect number, and the like. Specifically, the average test case defect discovery number represents the number of defects of code discovery problems actually executed per unit of test case, the average daily defect newly-added condition represents the time dimension summary average created defect number per day, the system test serious and fatal defect proportion represents the proportion of the number of defects at the serious and fatal level to the total number of defects, and the system test defect density represents the application dimension system defect number/application function point number.
102, performing prediction processing on the test information according to a preset regression model to obtain a predicted value; the preset regression model is obtained by training according to a plurality of historical test information and actual result information data pairs.
Illustratively, the preset regression model is trained according to a plurality of historical test information and actual result information data pairs. When the regression model is trained, improvement is carried out on the basis of the traditional linear regression algorithm, polynomial regression is added to improve the prediction accuracy, the problem of linear model under-fitting is solved, and the regression model is perfectly applied to the determination of the test fullness degree scene. Therefore, the server inputs the test information into a preset regression model to perform prediction processing, so as to obtain a predicted value. The test result information represents the sufficiency of the test, for example, if the test result information is 1, the representation test is sufficient, and if the test result information is 0, the representation test is insufficient.
And 103, if the predicted value characterization test is determined to be sufficient, stopping the test processing of the object to be tested.
Illustratively, if the server determines that the predictive value characterization test is sufficient, the testing process for the subject to be tested is stopped.
In the embodiment of the application, a prediction request of an object to be tested is obtained in the process of testing the object to be tested; wherein the prediction request comprises test information of the object to be tested. Performing prediction processing on the test information according to a preset regression model to obtain a predicted value; the preset regression model is obtained by training according to a plurality of historical test information and actual result information data pairs. And if the predicted value characterization test is determined to be sufficient, stopping the test processing of the object to be tested. In the scheme, a prediction request of an object to be tested is obtained in the process of testing the object to be tested, wherein the prediction request comprises test information of the object to be tested. Because the preset regression model is obtained by training according to a plurality of historical test information and actual result information data pairs, the test information is input into the preset regression model, and the predicted value can be predicted and output. And judging whether the predicted value is fully characterized and tested, and if the predicted value is fully characterized and tested, stopping testing the object to be tested. Therefore, in the process of testing the object to be tested, the full degree of the applied test is determined through the predicted value and the operation is stopped in time, so that insufficient test caused by too early stopping of the test and low input-output ratio and fatigue test caused by too late stopping of the test are avoided, and the technical problem that the difficulty in determining the full degree of the applied test is high is solved.
Fig. 2 is a schematic flowchart of another processing method for testing fullness provided in an embodiment of the present application, and as shown in fig. 2, the method includes:
Illustratively, a server obtains a historical data set, the data class of which is derived from applications that have been put on production in the past, the historical data set comprising a plurality of historical test information and actual result information data pairs. And the value of the actual result information y is 0 or 1, the y value in the historical data set is normalized, abnormal application does not occur after the production, the y value is 1, the abnormal application occurs after the production, and the y value is 0. The test information comprises the average test case defect discovery number of the object to be tested x1 Average daily defect addition situation x of object to be tested 2 System test critical and/or fatal defect ratio x 3 The ratio of the number of defects of serious and fatal grade to the total number of defects x 4 。
202, training an initial model according to historical test information and actual result information data pairs based on a polynomial regression algorithm to obtain a regression model; the regression model is used for predicting the testing fullness of the object to be tested according to the testing information of the object to be tested.
Illustratively, the problem of evaluating test sufficiency solved by the present application is a relatively complex problem, the reference factors are many and the root predicted values are not absolute linear relations, so the present application uses a polynomial regression analysis algorithm to analyze, and the polynomial regression can be generally expressed as the following formula (1):
wherein,
for prediction, x is test information, x includes x 1 、x 2 、x 3 、x 4 …x k ，
wx＝w 1 x 1 +w 2 x 2 +w 3 x 3 +w 4 x 4 +...+w k x 5 ，
Further, the polynomial regression is expressed as the following formula (2):
after the above formula (2) is obtained, the parameters W1, W2, W3, W4, v1, v2, v3, v4, b in the formula (2) are required to be solved, and first, a loss function expression is quantized and the initial model is trained by combining data in the historical data set. The loss function (3) is defined as follows:
wherein,
to predict value, y i For true, i.e. predictive value>
With the true value y i The average square distance between the two data sets is generally called mean square error (MAE) in statistics, n represents the test information and actual result information data pairs of n groups of histories in the history data set, i represents the ith group (1 ≦ i ≦ n) in the n groups of data pairs, y i It represents the ith y. Substituting the previous formula (2) into the loss function and solving the parameter w 1 、w 2 、w 3 、w 4 、v 1 、v 2 、v 3 、v 4 B as an independent variable of the loss function L, the following formula (4) can be obtained:
the existing task is to determine w when solving for the minimum L 1 、w 2 、w 3 、w 4 、v 1 、v 2 、v 3 、v 4 B, here we solve using least squares, in statistics, called least squares parameter estimation of regression models, we can put L (w, v, b) separately for w 1 、w 2 、w 3 、x 4 、v 1 、v 2 、v 3 、v 4 B, deriving to obtain w 1 The following formula (5):
to obtain w 2 The following formula (6):
to obtain w 3 The following formula (7):
to obtain w 4 The following formula (8):
to obtain v 1 The following formula (9):
to obtain v 2 The following formula (10):
to obtain v 3 The following formula (11):
to obtain v 4 The following formula (12):
the following formula (13) for b is obtained:
let the above equations (5), (9) and (13) all be 0, w can be obtained 1 、w 2 、w 3 、w 4 、v 1 、v 2 、v 3 、v 4 B, in particular, obtaining w 1 Equation (14) for the optimal solution of (a) is as follows:
to obtain w 2 Equation (15) for the optimal solution of (1) is as follows:
to obtain w 3 Equation (16) for the optimal solution of (a) is as follows:
to obtain w 4 Equation (17) for the optimal solution of (a) is as follows:
to obtain v 1 Equation (18) for the optimal solution of (a) is as follows:
to obtain v 2 Equation (18) for the optimal solution of (a) is as follows:
to obtain v 3 Equation (18) for the optimal solution of (a) is as follows:
to obtain v 4 Equation (18) for the optimal solution of (a) is as follows:
equation (19) for obtaining the optimal solution for b is as follows:
therefore, w is obtained from the above equations 1 、w 2 、w 3 、w 4 、v 1 、v 2 、v 3 、v 4 And b, obtaining a final prediction equation of the polynomial regression model, and further training to obtain the regression model.
For example, this step may refer to step 101 in fig. 1, and is not described again.
In one example, the prediction equation is:
wherein x is 1 Average test case defect discovery number, x, of the objects to be tested in the test information 2 Is the average daily defect increase of the object to be tested in the test information, x 3 Testing the system in the test information for severe and/or fatal defect proportion, x 4 The ratio of the number of defects of serious and fatal grade in the test information to the total number of defects, x k For testing other information in the information, w 1 、w 2 、w 3 、w 4 、w k 、v 1 、v 2 、v 3 、v 4 、v k Are all preset parameters.
Illustratively, the server may test x in the information 1 、x 2 、x 3 、x 4 Inputting the test information into a prediction equation in a preset regression model, and performing prediction processing on the test information to obtain a predicted value
And step 205, if the predicted value characterization test is determined to be sufficient, stopping the test processing of the object to be tested.
For example, if the predicted value characterizing test is determined to be sufficient, the server may stop the testing process for the subject to be tested.
In the embodiment of the application, a historical data set is obtained, wherein the historical data set comprises a plurality of historical test information and actual result information data pairs. Training the initial model according to historical test information and actual result information data pairs based on a polynomial regression algorithm to obtain a regression model; the regression model is used for predicting the testing fullness of the object to be tested according to the testing information of the object to be tested. Acquiring a prediction request of an object to be tested in the process of testing the object to be tested; wherein the prediction request comprises test information of the object to be tested. According to a prediction equation in a preset regression model, performing prediction processing on the test information to obtain a predicted value; the preset regression model is obtained by training according to a plurality of historical test information and actual result information data pairs. And if the predicted value characterization test is determined to be sufficient, stopping the test processing of the object to be tested. Therefore, the full degree of testing of application is determined through the predicted value and the operation is stopped in time, so that insufficient testing caused by too early stopping of testing and low input-output ratio and fatigue testing caused by too late stopping of testing are avoided, and the technical problem that the difficulty in determining the full degree of testing of application is high is solved.
Fig. 3 is a schematic structural diagram of a processing apparatus for testing a fullness level according to an embodiment of the present disclosure, as shown in fig. 3, the apparatus includes:
a first obtaining unit 31, configured to obtain a prediction request of an object to be tested in a process of performing a test on the object to be tested; wherein the prediction request comprises test information of the object to be tested.
The prediction unit 32 is configured to perform prediction processing on the test information according to a preset regression model to obtain a predicted value; the preset regression model is obtained by training according to a plurality of historical test information and actual result information data pairs.
And a stopping unit 33, configured to stop the test processing on the object to be tested if it is determined that the predicted value characterization test is sufficient.
The apparatus of this embodiment may execute the technical solution in the method, and the specific implementation process and the technical principle are the same, which are not described herein again.
Fig. 4 is a schematic structural diagram of another processing apparatus for testing fullness provided in an embodiment of the present application, and based on the embodiment shown in fig. 3, as shown in fig. 4, the prediction unit 32 is specifically configured to:
and carrying out prediction processing on the test information according to a prediction equation in a preset regression model to obtain a predicted value.
In one example, the prediction equation is:
wherein x is 1 The average test case defect discovery number of the objects to be tested in the test information, x is the average daily defect newly-added condition of the objects to be tested in the test information, and x 3 Testing the system in the test information for severe and/or fatal defect proportion, x 4 The ratio of the number of defects of a serious and fatal grade to the total number of defects in the test information, x k For testing other information in the information, w 1 、w 2 、w 3 、w 4 、w k 、v 1 、v 2 、v 3 、v 4 、v k Are all preset parameters.
In one example, the apparatus further comprises:
a second obtaining unit 41, configured to obtain a historical data set, where the historical data set includes a plurality of historical test information and actual result information data pairs.
The training unit 42 is configured to train the initial model according to the historical test information and actual result information data pairs based on a polynomial regression algorithm to obtain a regression model; the regression model is used for predicting the testing fullness of the object to be tested according to the testing information of the object to be tested.
The apparatus of this embodiment may execute the technical solution in the method, and the specific implementation process and the technical principle are the same, which are not described herein again.
Fig. 5 is a schematic structural diagram of a server provided in an embodiment of the present application, and as shown in fig. 5, the server includes: memory 51, processor 52.
The memory 51 has stored therein a computer program that is executable on the processor 52.
The processor 52 is configured to perform the methods provided in the embodiments described above.
The server also comprises a receiver 53 and a transmitter 54. The receiver 53 is used for receiving commands and data transmitted from an external device, and the transmitter 54 is used for transmitting commands and data to an external device.
Embodiments of the present application also provide a non-transitory computer-readable storage medium, where instructions in the storage medium, when executed by a processor of a server, enable the server to perform the method provided by the above embodiments.
An embodiment of the present application further provides a computer program product, where the computer program product includes: a computer program, the computer program being stored in a readable storage medium, from which the computer program can be read by at least one processor of the server, execution of the computer program by the at least one processor causing the server to carry out the solution provided by any of the embodiments described above.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
It will be understood that the present disclosure is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.
Claims (10)
1. A process for testing sufficiency, comprising:
in the process of testing an object to be tested, acquiring a prediction request of the object to be tested; wherein the prediction request comprises test information of the object to be tested;
performing prediction processing on the test information according to a preset regression model to obtain a predicted value; the preset regression model is obtained by training according to a plurality of historical test information and actual result information data pairs;
and if the predicted value characterization test is determined to be sufficient, stopping the test processing of the object to be tested.
2. The method according to claim 1, wherein the test information is subjected to prediction processing according to a preset regression model to obtain a predicted value, and specifically is used for:
and carrying out prediction processing on the test information according to a prediction equation in a preset regression model to obtain a predicted value.
3. The method of claim 2, wherein the prediction equation is:
wherein x is 1 An average test case defect discovery number x of the objects to be tested in the test information 2 The average daily defect newly-added condition, x, of the objects to be tested in the test information 3 Testing the system in the test information for a proportion of severe and/or fatal defects, x 4 The ratio of the number of defects of serious and fatal grade in the test information to the total number of defects, x k For other information in the test information, w 1 、w 2 、w 3 、w 4 、w k 、v 1 、v 2 、v 3 、v 4 、v k Are all preset parameters.
4. The method according to any one of claims 1-3, further comprising:
acquiring a historical data set, wherein the historical data set comprises a plurality of historical test information and actual result information data pairs;
training an initial model according to the historical test information and actual result information data pair based on a polynomial regression algorithm to obtain a regression model; the regression model is used for predicting the testing fullness of the object to be tested according to the testing information of the object to be tested.
5. A processing apparatus for testing sufficiency, comprising:
the device comprises a first obtaining unit, a second obtaining unit and a prediction unit, wherein the first obtaining unit is used for obtaining a prediction request of an object to be tested in the process of testing the object to be tested; wherein the prediction request comprises test information of the object to be tested;
the prediction unit is used for carrying out prediction processing on the test information according to a preset regression model to obtain a predicted value; the preset regression model is obtained by training according to a plurality of historical test information and actual result information data pairs;
and the stopping unit is used for stopping the test processing of the object to be tested if the predicted value characterization test is determined to be sufficient.
6. The apparatus according to claim 5, wherein the prediction unit is specifically configured to:
and carrying out prediction processing on the test information according to a prediction equation in a preset regression model to obtain a predicted value.
7. The apparatus of claim 6, wherein the prediction equation is:
wherein x is 1 An average test case defect discovery number x of the objects to be tested in the test information 2 The average daily defect newly-added condition, x, of the objects to be tested in the test information 3 Testing the system in the test information for a proportion of severe and/or fatal defects, x 4 The ratio of the number of defects of serious and fatal grade in the test information to the total number of defects, x k For other information in the test information, w 1 、w 2 、w 3 、w 4 、w k 、v 1 、v 2 、v 3 、v 4 、v k Are all preset parameters.
8. A server, characterized by comprising a memory, a processor, a computer program being stored in the memory and being executable on the processor, the processor implementing the method of any of the preceding claims 1-4 when executing the computer program.
9. A computer-readable storage medium having computer-executable instructions stored thereon, which when executed by a processor, perform the method of any one of claims 1-4.
10. A computer program product, characterized in that it comprises a computer program which, when being executed by a processor, carries out the method of any one of claims 1-4.
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