CN108228469B - Test case selection method and device - Google Patents
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Abstract
The embodiment of the invention provides a test case selection method and device, and belongs to the technical field of automatic testing. The method comprises the following steps: inputting the updating characteristics into a test case selection model, and outputting the confidence coefficient of each test case in a preset test case set; and selecting the test cases with the confidence degrees larger than the confidence degree threshold value from a preset test case set. Because the updating characteristics can be input into the test case selection model and the test cases are selected in a targeted manner based on the output result, the test mode is more flexible, the manpower can be liberated, and the test efficiency and the code coverage rate can be improved. In addition, test errors caused by human factors cannot be introduced, so that the test quality and the product robustness are improved.
Description
Technical Field
The embodiment of the invention relates to the technical field of automatic testing, in particular to a test case selection method and device.
Background
With the development of computer technology, data communication, network engineering, and IT industry companies, automated testing technology has received increasing attention and usage. When the automated test is realized, a test case is generally selected. In the related technology, when a test case is selected, the test case is generally selected by depending on manual experience, and the automatic test case triggered each time is invariable, so that the test case has no flexibility and pertinence.
Disclosure of Invention
In order to solve the above problems, embodiments of the present invention provide a test case selection method and apparatus that overcome the above problems or at least partially solve the above problems.
According to a first aspect of the embodiments of the present invention, a test case selection method is provided, where the method includes:
inputting the updating characteristics to the test case selection model, and outputting the confidence coefficient of each test case in the preset test case set, wherein the updating characteristics at least comprise submitting personnel information, function modification information and related module information;
and selecting the test cases with the confidence degrees larger than the confidence degree threshold value from a preset test case set.
According to the method provided by the embodiment of the invention, the confidence coefficient of each test case in the preset test case set is output by inputting the updating characteristics into the test case selection model. And selecting the test cases with the confidence degrees larger than the confidence degree threshold value from a preset test case set. Because the updating characteristics can be input into the test case selection model and the test cases are selected in a targeted manner based on the output result, the test mode is more flexible, the manpower can be liberated, and the test efficiency and the code coverage rate can be improved. In addition, test errors caused by human factors cannot be introduced, so that the test quality and the product robustness are improved.
With reference to the first possible implementation manner of the first aspect, in a second possible implementation manner, the method further includes: acquiring a preset number of sample updating characteristics according to the historical submission records, wherein the updating characteristics are the same as the information types contained in the sample updating characteristics; acquiring a data label of each test case under each sample updating characteristic according to a test result of each test case under each sample updating characteristic in a preset test case set, wherein the types of the data labels are divided into two types and respectively correspond to a pass test and a fault test; and taking the updating characteristics of a preset number of samples as the input of the original model, taking the data label of each test case under the updating characteristics of each sample as the output of the original model, and training the original model to obtain a test case selection model.
With reference to the first possible implementation manner of the first aspect, in a third possible implementation manner, the method further includes: obtaining a preset number of sample updating characteristics according to the historical submission record; and testing the test case selection model based on the preset number of sample updating characteristics and the test result of each test case under each sample updating characteristic, and continuously updating the test case selection model and the confidence coefficient threshold value in the test process until the network loss function in the test case selection model meets the preset condition.
With reference to any one of the first to third possible implementation manners of the first aspect, in a fourth possible implementation manner, the update feature further includes at least one of the following four kinds of information, where the following four kinds of information are submission time, compilation environment information, object-oriented information, and related engine information, respectively.
With reference to the first possible implementation manner of the first aspect, in a fifth possible implementation manner, the test case selection model is a convolutional neural network model, the test case selection model includes three convolutional layers and one fully-connected layer, and a pooling layer and an activation function layer are connected behind each convolutional layer.
According to a second aspect of the embodiments of the present invention, there is provided a test case selecting apparatus, including:
the output module is used for inputting the updating characteristics to the test case selection model and outputting the confidence coefficient of each test case in the preset test case set, and the updating characteristics at least comprise submitting personnel information, function modification information and related module information;
and the selecting module is used for selecting the test case with the confidence coefficient larger than the confidence coefficient threshold value from the preset test case set.
According to a third aspect of the embodiments of the present invention, there is provided a test case selecting apparatus, including:
At least one processor; and
at least one memory communicatively coupled to the processor, wherein:
the memory stores program instructions executable by the processor, and the processor calls the program instructions to perform the test case selection method provided by any one of the various possible implementations of the first aspect.
According to a fourth aspect of the present invention, there is provided a non-transitory computer-readable storage medium storing computer instructions for causing a computer to execute the test case selection method provided in any one of the various possible implementations of the first aspect.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory and are not restrictive of embodiments of the invention.
Drawings
FIG. 1 is a schematic flow chart of a test case selection method according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart of a test case selection model training method according to an embodiment of the present invention;
FIG. 3 is a flowchart illustrating a test case selection model testing method according to an embodiment of the present invention;
FIG. 4 is a flowchart illustrating a test case selection method according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of a network structure of a test case selection model according to an embodiment of the present invention;
FIG. 6 is a block diagram of a test case selection apparatus according to an embodiment of the present invention;
fig. 7 is a block diagram of a test case selecting device according to an embodiment of the present invention.
Detailed Description
The following describes embodiments of the present invention in further detail with reference to the drawings and examples. The following examples are intended to illustrate the examples of the present invention, but are not intended to limit the scope of the examples of the present invention.
The embodiment of the invention provides a test case selection method, and referring to fig. 1, the method comprises the following steps: 101. inputting the updating characteristics to the test case selection model, and outputting the confidence coefficient of each test case in the preset test case set, wherein the updating characteristics at least comprise submitting personnel information, function modification information and related module information; 102. and selecting the test cases with the confidence degrees larger than the confidence degree threshold value from a preset test case set.
Before the step 101 is executed, the test cases may be designed, so that a preset test case set is formed by the designed test cases. The content of the designed test case can cover different levels such as an interface function parameter layer, a normal/abnormal interface flow calling layer and the like, and can also consider different aspects such as performance, pressure, strength, functions, compatibility and the like. Preferably, the number of the test cases in the preset test case set may be 300.
After the test cases are designed, the code coverage rate of each test case can be analyzed and counted through opencppcoverage, gcov and other code rate coverage tools, so that the reasonability and the high efficiency of the design of each test case are guaranteed, and certain test depth and breadth of a test product can be met. Additionally, the update feature may be triggered by the product developer when updating code. In particular, when a product developer is detected to update code, the update feature may be automatically obtained. The detection period may be one minute, that is, every minute, whether the product developer updates the source code is detected. Alternatively, the source code may be monitored in real time, and the update feature is obtained whenever the update operation is detected, which is not specifically limited in the embodiment of the present invention.
The update features may include at least submitter information, function modification information, and module information involved. The code level of each product developer is different, and the probability of bug generation of each product developer is also different, so that the information of the submitter for updating the source code is required to be used as an updating characteristic. Due to the characteristics of high cohesion and low coupling during code development, other functional modules can be influenced by changing one function, the function modified during updating the source code and the related functional modules also need to be tested again, and therefore function modification information and related module information also need to be used as updating characteristics. After the three kinds of information are obtained, feature encoding can be performed on the three kinds of information, so that a 3-dimensional feature vector is obtained.
After the update features are obtained when the update code is obtained, the update features may be input to the test case selection model. The test case selection model is obtained after training based on a preset test case set and sample updating characteristics, and the updating characteristics are the same as the information types in the sample updating characteristics. The original model used by the test case selection model may be a convolutional neural network, a cyclic neural network, a deep neural network, a support vector machine, a long-term and short-term memory network model, or the like, which is not specifically limited in this embodiment of the present invention. The test case selection model outputs the confidence level of each test case in the preset test case set, namely, the credibility of each test case when the source code is tested by the test case can be accurately detected whether the source code has problems. After the confidence of each test case in the preset test case set is obtained, the test case with the confidence greater than the confidence threshold value can be selected. The confidence threshold may be set as an initial value according to a requirement, and may be continuously adjusted subsequently, which is not specifically limited in the embodiment of the present invention.
According to the method provided by the embodiment of the invention, the confidence coefficient of each test case in the preset test case set is output by inputting the updating characteristics into the test case selection model. And selecting the test cases with the confidence degrees larger than the confidence degree threshold value from a preset test case set. Because the updating characteristics can be input into the test case selection model and the test cases are selected in a targeted manner based on the output result, the test mode is more flexible, the manpower can be liberated, and the test efficiency and the code coverage rate can be improved. In addition, test errors caused by human factors cannot be introduced, so that the test quality and the product robustness are improved.
Based on the content of the above embodiment, the embodiment of the present invention further provides a method for training to obtain a test case selection model. Referring to fig. 2, the method includes: 201. acquiring a preset number of sample updating characteristics according to the historical submission records, wherein the updating characteristics are the same as the information types contained in the sample updating characteristics; 202. acquiring a data label of each test case under each sample updating characteristic according to a test result of each test case under each sample updating characteristic in a preset test case set, wherein the types of the data labels are divided into two types and respectively correspond to a pass test and a fault test; 203. and taking the updating characteristics of a preset number of samples as the input of the original model, taking the data label of each test case under the updating characteristics of each sample as the output of the original model, and training the original model to obtain a test case selection model.
In step 201, the obtained updated features of the preset number of samples may be used as a training set for training a model. Wherein the sample update characteristic contains the same type of information as the update characteristic in step 101. For example, the update feature is obtained by performing feature coding on the submitted personnel information, the function modification information and the related module information, and the sample update feature is also obtained by performing feature coding on the three information in each history update process. If the number of the information included in the sample update features is three, N sample update features, that is, 3 × N-dimensional feature vectors, can be obtained according to the history submission record.
After the training set is obtained, each test case in the preset test case set can be operated respectively based on the engine version corresponding to each sample updating feature, and therefore the test result of each test case under each sample updating feature is obtained. The test result is divided into a test pass and a test error. The data tag may be represented by 1 and 0, and when the test result is that the test passes, the data tag corresponding to the test result may be 1. When the test result indicates that the test has an error, the data tag corresponding to the test result may be 0. Of course, the data tag may also be in other forms, and the embodiment of the present invention is not limited to this specifically. After the training data is prepared, the model parameters of the original model can be trained through the step 203 to obtain the mapping relation between the existing updated features and the test cases, and finally the test case selection model is obtained.
Through the process of training the model, the test case selection model can be obtained. It is contemplated that the model may also be tested after training. As an optional embodiment, the embodiment of the invention also provides a test method of the test case selection model. Referring to fig. 3, the method includes: 301. obtaining a preset number of sample updating characteristics according to the historical submission record; 302. and testing the test case selection model based on the preset number of sample updating characteristics and the test result of each test case under each sample updating characteristic, and continuously updating the test case selection model and the confidence coefficient threshold value in the test process until the test case selection model meets the preset condition.
In step 301, the obtained updated features of the preset number of samples can be used as a test set for testing the model. The sample update feature is the same as the update feature in step 101 and the type of information included in the sample update feature in the training process. It should be noted that, in the above embodiment, the training set and the test set are respectively obtained, in actual implementation, a large number of sample update features may be obtained and distributed according to a certain ratio (for example, 7: 3), so as to form the training set and the test set, which is not specifically limited in this embodiment of the present invention.
After the test set is obtained, the test case selection model may be tested according to the above step 302 to determine whether the test case selection model has a problem, and the model parameters may be updated. In addition, the confidence threshold value can be adjusted according to the actual output result and the actual requirement of the model. For example, after the initial value of the confidence threshold is determined, if the confidence level greater than the confidence threshold is found in the confidence level output by the model in the testing process, this may result in that only a small number of test cases may be selected for testing subsequently. When the number of the selected test cases needs to be increased, the confidence threshold value can be reduced, so that more test cases can be selected subsequently. Otherwise, the confidence threshold may be increased.
It should be noted that the preset condition may be convergence of a network loss function in the test case selection model, or an edit distance between a test result of each test case under each sample update characteristic and an output result of the test case selection model reaches a minimum, or an identification result on the test set tends to be stable, which is not specifically limited in the embodiment of the present invention.
Taking the example that the number of test cases in the preset test case set is 300, the sample update features are divided into a training set and a test set, and the type of the test case selection model is SVM, the process of training, testing and selecting test cases in the above embodiment can refer to fig. 4.
Based on the content of the foregoing embodiment, considering that the update feature only includes the above three kinds of information, and the code coverage rate may not be enough, as an alternative embodiment, the update feature further includes at least one of the following four kinds of information, which are the commit time, the compilation environment information, the object-oriented information, and the engine information involved, respectively.
Wherein the quality of the code submitted near the release date of the product may have an impact, and the time of submission may be used as an update characteristic. The compiling environment information (such as gcc version, win version, system kernel version, etc.) directly affects the way of writing the code, so that the compiling environment information can be used as an updating characteristic. Object-oriented (such as toB or toC, different product specifications, product cycles, and product acceptance differences all affect the quality of the product in the development process to some extent), related engines (such as a speech recognition engine, a speech synthesis engine, and different engines, which have different technical lines and different technical maturity) also affect the code, so that object-oriented information and related engine information can be used as an update feature.
It should be noted that, when the update feature includes the above 7 kinds of information, the sample update feature used in the training and testing process should also include the above 7 kinds of information.
Based on the content of the above embodiments, as an optional embodiment, the test case selection model is a convolutional neural network model, a cyclic neural network model, a deep neural network model, or a long-short term memory network model.
Based on the content of the above embodiment, as an optional embodiment, the test case selection model is a convolutional neural network model, the test case selection model includes three convolutional layers and a fully-connected layer, and a pooling layer and an activation function layer are connected behind each convolutional layer.
Wherein, the pooling layers all adopt a maximum pooling method, and the ReLU is a linear correction function. When the training data is larger, the ReLU has better adaptability as a sigmoid function than an activation function. Specifically, the network structure design of the test case selection model may be as shown in fig. 5. The last layer in the test case selection model is a Softmax classification layer, which contains 300 neurons and represents the output states of 300 test cases (i.e. 1 test passed or 0 test error). These 300 neurons are fully connected to the outputs of the second and third layers.
It should be noted that, all the above-mentioned alternative embodiments may be combined arbitrarily to form alternative embodiments of the present invention, and are not described in detail herein.
Based on the content of the above embodiment, an embodiment of the present invention provides a test case selection device, where the test case selection device is configured to execute a test case selection method in the above method embodiment. Referring to fig. 6, the apparatus includes:
the output module 601 is used for inputting the updating characteristics to the test case selection model and outputting the confidence coefficient of each test case in the preset test case set, wherein the updating characteristics at least comprise submitting personnel information, function modification information and related module information;
a selecting module 602, configured to select a test case with a confidence greater than a confidence threshold from a preset test case set.
As an alternative embodiment, the apparatus further comprises:
the first acquisition module is used for acquiring a preset number of sample updating characteristics according to the historical submission record, wherein the updating characteristics are the same as the information types contained in the sample updating characteristics;
the second obtaining module is used for obtaining a data label of each test case under each sample updating characteristic according to a test result of each test case under each sample updating characteristic in a preset test case set, wherein the types of the data labels are divided into two types and respectively correspond to test passing and test errors;
And the training module is used for taking the updating characteristics of a preset number of samples as the input of the original model, taking the data label of each test case under the updating characteristics of each sample as the output of the original model, and training the original model to obtain a test case selection model.
As an alternative embodiment, the apparatus further comprises:
the third acquisition module is used for acquiring the updating characteristics of a preset number of samples according to the historical submission record;
and the test module is used for testing the test case selection model based on the preset number of sample updating characteristics and the test result of each test case under each sample updating characteristic, and continuously updating the test case selection model and the confidence coefficient threshold value in the test process until the test case selection model meets the preset condition.
As an alternative embodiment, the update feature further includes at least one of the following four kinds of information, which are the commit time, the compilation environment information, the object-oriented information, and the engine information involved, respectively.
As an alternative embodiment, the test case selection model is a convolutional neural network model, a cyclic neural network model, a deep neural network model, or a long-short term memory network model.
As an optional embodiment, the test case selection model is a convolutional neural network model, the test case selection model comprises three convolutional layers and a full connection layer, and a pooling layer and an activation function layer are connected behind each convolutional layer.
According to the device provided by the embodiment of the invention, the confidence coefficient of each test case in the preset test case set is output by inputting the updating characteristics to the test case selection model. And selecting the test cases with the confidence degrees larger than the confidence degree threshold value from a preset test case set. Because the updating characteristics can be input into the test case selection model and the test cases are selected in a targeted manner based on the output result, the test mode is more flexible, the manpower can be liberated, and the test efficiency and the code coverage rate can be improved. In addition, test errors caused by human factors cannot be introduced, so that the test quality and the product robustness are improved.
The embodiment of the invention provides test case selection equipment. Referring to fig. 7, the apparatus includes: a processor (processor)701, a memory (memory)702, and a bus 703;
the processor 701 and the memory 702 complete mutual communication through the bus 703;
the processor 701 is configured to call the program instructions in the memory 702 to execute the test case selection method provided in the foregoing embodiment, for example, including: inputting the updating characteristics to the test case selection model, and outputting the confidence coefficient of each test case in the preset test case set, wherein the updating characteristics at least comprise submitting personnel information, function modification information and related module information; and selecting the test cases with the confidence degrees larger than the confidence degree threshold value from a preset test case set.
An embodiment of the present invention provides a non-transitory computer-readable storage medium, where the non-transitory computer-readable storage medium stores computer instructions, and the computer instructions enable a computer to execute the test case selection method provided in the foregoing embodiment, for example, the method includes: inputting the updating characteristics to the test case selection model, and outputting the confidence coefficient of each test case in the preset test case set, wherein the updating characteristics at least comprise submitting personnel information, function modification information and related module information; and selecting the test cases with the confidence degrees larger than the confidence degree threshold value from a preset test case set.
Those of ordinary skill in the art will understand that: all or part of the steps for implementing the method embodiments may be implemented by hardware related to program instructions, and the program may be stored in a computer readable storage medium, and when executed, the program performs the steps including the method embodiments; and the aforementioned storage medium includes: various media that can store program codes, such as ROM, RAM, magnetic or optical disks.
The above-described embodiments of the test case selecting device and the like are merely illustrative, where units illustrated as separate components may or may not be physically separate, and components displayed as units may or may not be physical units, may be located in one place, or may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium, such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute the various embodiments or some parts of the methods of the embodiments. Finally, the method of the present application is only a preferred embodiment and is not intended to limit the scope of the embodiments of the present invention. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the embodiments of the present invention should be included in the protection scope of the embodiments of the present invention.
Claims (8)
1. A test case selection method is characterized by comprising the following steps:
inputting the updating characteristics when updating the code into a test case selection model, and outputting the confidence coefficient of each test case in a preset test case set through the test case selection model, wherein the updating characteristics at least comprise submitting personnel information, function modification information and related module information;
Selecting a test case with the confidence coefficient larger than a confidence coefficient threshold value from the preset test case set;
the test case selection model is established based on the following steps:
obtaining a preset number of sample updating features according to a historical submission record, wherein the updating features and the sample updating features have the same information type;
acquiring a data label of each test case under each sample updating characteristic according to a test result of each test case under each sample updating characteristic in the preset test case set, wherein the types of the data labels are divided into two types and respectively correspond to a test pass and a test error;
and taking the updating characteristics of a preset number of samples as the input of an original model, taking the data label of each test case under the updating characteristics of each sample as the output of the original model, and training the original model to obtain the test case selection model.
2. The method of claim 1, further comprising:
obtaining a preset number of sample updating characteristics according to the historical submission record;
and testing the test case selection model based on the preset number of sample updating characteristics and the test result of each test case under each sample updating characteristic, and continuously updating the test case selection model and the confidence coefficient threshold value in the test process until the test case selection model meets the preset condition.
3. The method according to claim 1 or 2, wherein the update feature further comprises at least one of the following four kinds of information, which are commit time, compile environment information, object-oriented information, and engine-related information, respectively.
4. The method of claim 1, wherein the test case selection model is a convolutional neural network model, a cyclic neural network model, a deep neural network model, a support vector machine, or a long-short term memory network model.
5. The method of claim 4, wherein the test case selection model is a convolutional neural network model, and wherein the test case selection model comprises three convolutional layers and one fully connected layer, and wherein each convolutional layer is followed by a pooling layer and an activation function layer.
6. A test case selection device is characterized by comprising:
the output module is used for inputting the updating characteristics when the codes are updated into the test case selection model and outputting the confidence coefficient of each test case in the preset test case set through the test case selection model, wherein the updating characteristics at least comprise submitting personnel information, function modification information and related module information;
The selecting module is used for selecting the test cases with the confidence degrees larger than the confidence degree threshold value from the preset test case set;
the device comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring a preset number of sample updating characteristics according to a historical submission record, and the updating characteristics are the same as the information types contained in the sample updating characteristics;
the second obtaining module is used for obtaining a data label of each test case under each sample updating characteristic according to a test result of each test case under each sample updating characteristic in the preset test case set, wherein the types of the data labels are divided into two types and respectively correspond to a test pass and a test error;
and the training module is used for taking the updating characteristics of a preset number of samples as the input of an original model, taking the data label of each test case under the updating characteristics of each sample as the output of the original model, and training the original model to obtain the test case selection model.
7. A test case selection device is characterized by comprising:
at least one processor; and
at least one memory communicatively coupled to the processor, wherein:
the memory stores program instructions executable by the processor, the processor invoking the program instructions to perform the method of any of claims 1 to 5.
8. A non-transitory computer-readable storage medium storing computer instructions that cause a computer to perform the method of any one of claims 1 to 5.
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| CN110110854B (en) * | 2019-04-01 | 2022-04-22 | 南京邮电大学 | Method for testing sufficiency of deep neural network based on edge state |
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| CN114168565B (en) * | 2021-12-10 | 2022-07-08 | 北京宇信科技集团股份有限公司 | Backtracking test method, device and system of business rule model and decision engine |
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