CN119718939A

CN119718939A - Case processing method, apparatus, device, medium, and program product

Info

Publication number: CN119718939A
Application number: CN202411840840.9A
Authority: CN
Inventors: 张倩雯
Original assignee: Industrial and Commercial Bank of China Ltd ICBC
Current assignee: Industrial and Commercial Bank of China Ltd ICBC
Priority date: 2024-12-13
Filing date: 2024-12-13
Publication date: 2025-03-28

Abstract

The present disclosure provides a case processing method that can be applied to the fields of financial technology and computer software technology. The case processing method includes: generating N initial test cases based on production cases and requirement texts, the requirement text is used to indicate the change information of the source code for the production case, and N is an integer greater than or equal to 2; screening M target test cases that meet the preset conditions from the N initial test cases, 0<M≤N, and is an integer; when it is determined that M≥2, the M target test cases are prioritized based on the code quality index, and the code quality index is used to indicate the risk situation of the target test case; and sending the sorted M target test cases to the test environment in order of priority, so as to execute the target test cases in the test environment in sequence. The present disclosure also provides a case processing device, equipment, storage medium and program product.

Description

Case processing method, apparatus, device, medium, and program product

Technical Field

The present disclosure relates to the field of financial technology and the field of computer software technology, in particular to the field of software testing, and more particularly to a case processing method, apparatus, device, medium and program product.

Background

A test case is a set of test inputs, execution conditions, and expected results that are formulated for a particular goal in order to test a program path or verify whether a particular requirement is met. The importance of the test cases is mainly represented in the following aspects that the test cases form the basis for designing and formulating the test process, the depth of the test is proportional to the number of the test cases, the time schedule of each stage of the test period can be estimated more accurately according to the comprehensive and detailed test cases, and the types of test design and development and required resources are mainly controlled by the test cases.

In the implementation process of the embodiment of the disclosure, the current production test cases are long in test time period, redundant or outdated in test cases, low in test coverage rate, incapable of effectively meeting the requirement of frequent change, and poor in stability of software products.

Disclosure of Invention

In view of the foregoing, the present disclosure provides case processing methods, apparatus, devices, media, and program products.

According to a first aspect of the present disclosure, there is provided a case processing method, including generating N initial test cases based on production cases and a demand text, wherein the demand text is used to indicate change information of source codes for the production cases, N is an integer greater than or equal to 2, selecting M target test cases satisfying a preset condition from the N initial test cases, wherein 0< M is less than or equal to N and is an integer, prioritizing the M target test cases based on a code quality index when the M is determined to be greater than or equal to 2, wherein the code quality index is used to indicate risk conditions of the target test cases, and sequentially transmitting the ordered M target test cases to a test environment in order of priority so as to sequentially execute the target test cases in the test environment.

According to the embodiment of the disclosure, when M is more than or equal to 2, the priority ranking is performed on M target test cases based on the code quality index, wherein the priority ranking comprises the steps of extracting features of the target test cases to obtain initial features based on the code quality index, screening the initial features to obtain target features, determining a risk evaluation value of the target test cases based on feature values corresponding to the target features and weight values corresponding to the target features, and the priority ranking is performed on the M target test cases based on the risk evaluation value of each target test case.

According to the embodiment of the disclosure, the code quality index comprises at least one of code complexity, code change frequency and defect density, and the method comprises the steps of extracting features of a target test case based on the code quality index to obtain initial features, wherein the initial features comprise the steps of extracting circle complexity, code line number and method length features from the target test case based on the code complexity, and/or extracting code change frequency features from the target test case based on the code change frequency to obtain initial features, and/or extracting proportion features of defect number and code line number from the target test case based on the defect density to obtain initial features.

According to the embodiment of the disclosure, M target test cases meeting preset conditions are selected from N initial test cases, wherein the M target test cases meeting the preset conditions are selected from the N initial test cases based on similarity between any two vectors in the N initial test cases, and the M target test cases meeting the preset conditions are selected from the N initial test cases.

According to an embodiment of the present disclosure, vector conversion is performed on each of N initial test cases to obtain N vectors, respectively, including converting the initial test cases into an image in a structured form for each of the N initial test cases, and converting the image into a vector based on graph embedding techniques.

According to the embodiment of the disclosure, M target test cases meeting preset conditions are selected from N initial test cases based on the similarity between any two vectors in N vectors, wherein the method comprises the steps of determining a first vector pair with the similarity larger than a threshold value and a second vector pair with the similarity smaller than or equal to the threshold value when the similarity between any two vectors in N vectors is larger than the threshold value, determining the initial test case corresponding to any one vector in the first vector pair and the initial test case corresponding to the second vector pair as the target test case when the common vector does not exist between the first vector pair and the second vector pair, and determining the initial test case corresponding to the second vector pair as the target test case when the common vector exists between the first vector pair and the second vector pair.

According to an embodiment of the disclosure, the change information includes a change code segment, generating N initial test cases based on the production cases and the demand text, including parsing the production cases to obtain project information based on the structure, the annotation, and the demand specification of the source code, and deriving N initial test cases based on the change code segment and the project information.

The second aspect of the disclosure provides a case processing device, which comprises a generation module, a screening module, a sorting module and a sending module, wherein the generation module is used for generating N initial test cases based on production cases and a demand text, the demand text is used for indicating change information of source codes for the production cases, N is an integer greater than or equal to 2, the screening module is used for screening M target test cases meeting preset conditions from the N initial test cases, wherein 0< M is less than or equal to N and is an integer, the sorting module is used for sorting the M target test cases in priority based on a code quality index when the M is more than or equal to 2, the code quality index is used for indicating risk conditions of the target test cases, and the sending module is used for sequentially sending the sorted M target test cases to a test environment according to the order of priority so as to sequentially execute the target test cases in the test environment.

A third aspect of the present disclosure provides an electronic device comprising one or more processors and a memory for storing one or more computer programs, wherein the one or more processors execute the one or more computer programs to implement the steps of the method.

A fourth aspect of the present disclosure also provides a computer readable storage medium having stored thereon a computer program or instructions which, when executed by a processor, implement the steps of the above method.

A fifth aspect of the present disclosure also provides a computer program product comprising a computer program or instructions which, when executed by a processor, performs the steps of the method described above.

According to the embodiment of the disclosure, an initial test case is generated based on a production case and a demand text, then the initial test case is screened to obtain a target test case, and then risk priority ordering is performed on the target test case based on a code quality index, so that the target test case is sequentially executed in a test environment according to the priority order. Because the initial test cases are generated based on the requirement text, the verification requirement of the change information can be responded in time, and then the initial test cases are screened, so that redundant test cases can be reduced, the test efficiency is improved, the test coverage rate is improved, and the test period is shortened. On the basis, risk priority ranking is performed through code quality indexes, so that a test case with high risk can be executed in a test environment, potential risks can be accurately positioned, and quality and stability of a software product are improved. The problems that the current production test case has long test time period, redundant or outdated test cases, low test coverage, incapability of high-efficiency to meet the requirement of frequent change and poor stability of software products are at least partially solved.

Drawings

The foregoing and other objects, features and advantages of the disclosure will be more apparent from the following description of embodiments of the disclosure with reference to the accompanying drawings, in which:

Fig. 1 schematically illustrates an application scenario diagram of a case processing method, apparatus, device, medium and program product according to an embodiment of the present disclosure;

Fig. 2 schematically illustrates a flow chart of a case processing method according to an embodiment of the present disclosure;

Fig. 3 schematically illustrates a screening schematic of a target test case according to an embodiment of the present disclosure;

fig. 4 schematically illustrates a schematic diagram of determining risk assessment values for a target test case according to an embodiment of the present disclosure;

fig. 5 schematically illustrates a flow chart of a case processing method according to another embodiment of the present disclosure;

fig. 6 schematically shows a block diagram of a case processing apparatus according to an embodiment of the present disclosure, and

Fig. 7 schematically illustrates a block diagram of an electronic device adapted to implement a case-handling method according to an embodiment of the present disclosure.

Detailed Description

Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. It should be understood that the description is only exemplary and is not intended to limit the scope of the present disclosure. In the following detailed description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the present disclosure. It may be evident, however, that one or more embodiments may be practiced without these specific details. In addition, in the following description, descriptions of well-known structures and techniques are omitted so as not to unnecessarily obscure the concepts of the present disclosure.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. The terms "comprises," "comprising," and/or the like, as used herein, specify the presence of stated features, steps, operations, and/or components, but do not preclude the presence or addition of one or more other features, steps, operations, or components.

All terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art unless otherwise defined. It should be noted that the terms used herein should be construed to have meanings consistent with the context of the present specification and should not be construed in an idealized or overly formal manner.

Where a convention analogous to "at least one of A, B and C, etc." is used, in general such a convention should be interpreted in accordance with the meaning of one of skill in the art having generally understood the convention (e.g., "a system having at least one of A, B and C" would include, but not be limited to, systems having a alone, B alone, C alone, a and B together, a and C together, B and C together, and/or A, B, C together, etc.).

It should be noted that, the case processing method and apparatus provided in the present disclosure may be used in the field of financial technology, and may also be used in any field other than the field of financial technology, and the application field of the case processing method and apparatus is not limited in the present disclosure.

In the technical solution of the present disclosure, the related user information (including, but not limited to, user personal information, user image information, user equipment information, such as location information, etc.) and data (including, but not limited to, data for analysis, stored data, displayed data, etc.) are information and data authorized by the user or sufficiently authorized by each party, and the related data is collected, stored, used, processed, transmitted, provided, disclosed, applied, etc. in compliance with relevant laws and regulations and standards, necessary security measures are taken, no prejudice to the public order colloquia is provided, and corresponding operation entries are provided for the user to select authorization or rejection.

In the scenario of using personal information to make an automated decision, the method, the device and the system provided by the embodiment of the disclosure provide corresponding operation inlets for users to choose to agree or reject the automated decision, and enter an expert decision flow if the users choose to reject. The expression "automated decision" here refers to an activity of automatically analyzing, assessing the behavioral habits, hobbies or economic, health, credit status of an individual, etc. by means of a computer program, and making a decision. The expression "expert decision" here refers to an activity of making a decision by a person who is specializing in a certain field of work, has specialized experience, knowledge and skills and reaches a certain level of expertise.

In the implementation process of the embodiment of the disclosure, the system device for verifying the production verification case is generally dependent on manually writing and maintaining the test case, and has the problems of low efficiency, insufficient coverage, easiness in missing boundary conditions, abnormal scenes and the like. In addition, the response to the frequently-changed requirements is not timely, and the verification requirements after the introduction of new features cannot be quickly adapted. Furthermore, verification cases are difficult to update synchronously with the iteration of the software product, and case redundancy or outdated phenomena often occur. The coverage rate is difficult to calculate accurately and optimize dynamically, and the production risk is high. In addition, the lack of automatic feedback and iterative optimization mechanisms is unfavorable for continuously improving the quality of software products and shortening the production period.

Based on the situation, the embodiment of the disclosure provides a case processing method, which comprises the steps of generating N initial test cases based on production cases and a demand text, wherein the demand text is used for indicating change information of source codes for the production cases, N is an integer greater than or equal to 2, screening M target test cases meeting preset conditions from the N initial test cases, wherein 0< M is less than or equal to N and is an integer, and under the condition that M is more than or equal to 2, prioritizing the M target test cases based on a code quality index, wherein the code quality index is used for indicating risk conditions of the target test cases, and sequentially sending the ordered M target test cases to a test environment according to the order of priority so as to sequentially execute the target test cases in the test environment.

Fig. 1 schematically illustrates an application scenario diagram of a case processing method, apparatus, device, medium and program product according to an embodiment of the present disclosure.

As shown in fig. 1, an application scenario 100 according to this embodiment may include a first terminal device 101, a second terminal device 102, a third terminal device 103, a network 104, and a server 105. The network 104 is a medium used to provide a communication link between the first terminal device 101, the second terminal device 102, the third terminal device 103, and the server 105. The network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others.

The user may interact with the server 105 via the network 104 using the first terminal device 101, the second terminal device 102, the third terminal device 103, to receive or send messages etc. Various communication client applications, such as a shopping class application, a web browser application, a search class application, an instant messaging tool, a mailbox client, social platform software, etc. (by way of example only) may be installed on the first terminal device 101, the second terminal device 102, and the third terminal device 103.

The first terminal device 101, the second terminal device 102, the third terminal device 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smartphones, tablets, laptop and desktop computers, and the like.

The server 105 may be a server providing various services, such as a background management server (by way of example only) providing support for websites browsed by the user using the first terminal device 101, the second terminal device 102, and the third terminal device 103. The background management server may analyze and process the received data such as the user request, and feed back the processing result (e.g., the web page, information, or data obtained or generated according to the user request) to the terminal device.

It should be noted that, the case processing method provided by the embodiment of the present disclosure may be generally performed by the server 105. Accordingly, the case processing apparatus provided by the embodiments of the present disclosure may be generally disposed in the server 105. The case processing method provided by the embodiments of the present disclosure may also be performed by a server or a server cluster that is different from the server 105 and is capable of communicating with the first terminal device 101, the second terminal device 102, the third terminal device 103, and/or the server 105. Accordingly, the case processing apparatus provided by the embodiments of the present disclosure may also be provided in a server or a server cluster that is different from the server 105 and is capable of communicating with the first terminal device 101, the second terminal device 102, the third terminal device 103, and/or the server 105.

It should be understood that the number of terminal devices, networks and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.

The case processing method of the disclosed embodiment will be described in detail with reference to fig. 2 to 5 based on the scenario described in fig. 1.

Fig. 2 schematically illustrates a flow chart of a case processing method according to an embodiment of the present disclosure.

As shown in fig. 2, the case processing method of this embodiment includes operations S210 to S240.

In operation S210, N initial test cases are generated based on the production cases and the demand text.

In operation S220, M target test cases satisfying the preset condition are selected from the N initial test cases.

In operation S230, in the case where M is determined to be greater than or equal to 2, the M target test cases are prioritized based on the code quality index.

In operation S240, the M ordered target test cases are sequentially transmitted to the test environment in order of priority, so that the target test cases are sequentially executed in the test environment.

According to embodiments of the present disclosure, production cases may be used to indicate specific instances or scenarios of application software product solutions in an actual production environment. Production cases may be used to describe how a software product is used to solve a practical problem, meet business needs, or improve workflow. The production case may include source code and document data. The demand text may be used to indicate change information for source code for the production case, N being an integer greater than or equal to 2. M is 0< N, and is an integer.

According to the embodiment of the disclosure, the historical source codes and the historical document data of the historical production cases and the plurality of historical test cases can be obtained, so that the case generation model learns the relations between the plurality of historical test cases and the historical source codes and the historical document data, and further, after the demand text and the production cases are input into the case generation model, the production cases serve as prompts, and the case generation model can generate a plurality of initial test cases according to the demand text.

According to an embodiment of the present disclosure, the preset condition may be used to indicate that the initial test cases are different from each other, that is, selecting M target test cases satisfying the preset condition from the N initial test cases may be understood as performing deduplication processing on the N initial test cases, to obtain M target test cases.

According to embodiments of the present disclosure, a code quality indicator may be used to indicate a risk condition of a target test case. The risk level of the target test cases can be determined according to the code quality index, and the M target test cases are ordered in descending order according to the risk level, namely, the risk level is ranked before, the risk level is ranked after, and the priority ordering is completed.

According to an embodiment of the present disclosure, the change information may include a change code segment. The altered code segments may include, but are not limited to, newly added code, modified code, or deleted code.

For operation S210 shown in FIG. 2 above, generating N initial test cases based on the production cases and the demand text may include the operations of parsing the production cases to obtain project information based on the structure, notes, and demand specifications of the source code, and deriving N initial test cases based on the change code segments and the project information.

According to embodiments of the present disclosure, production cases may be parsed from the structure of source code, annotations, and aspects of the requirements specification using deep learning and Natural Language Processing (NLP) techniques to obtain project information. Project information may include, for example, functional points and business logic, important business rules and constraints, performance and security requirements, user interface and interaction requirements, key variables and parameters in the code, and the like.

According to embodiments of the present disclosure, derived information may be derived based on the change code snippets and the project information. The derived information may include change points that affect the behavior of the software product. For example, if a critical function is modified, all functions that rely on that function may be affected. From the derived information, an initial test case may be generated. The initial test cases may be used to verify whether the changes work as intended, whether new questions have been introduced, etc. The initial test cases may include functional test cases to verify whether the change meets business needs and function points, regression test cases to ensure that the change does not destroy existing functions, boundary value test cases to test against input values near the change points to find potential boundary problems, and exception test cases to verify the behavior of the change in case of exceptions or errors.

According to the embodiment of the disclosure, the structure, the annotation and the requirement specification of the source code are analyzed, so that the test case covering the normal flow, the abnormal flow and the boundary condition can be generated, and the test coverage rate is improved. In addition, the test cases are automatically generated, the workload of manually writing the test cases is reduced, and the coverage rate and the efficiency of the test are improved. The method provided by the present disclosure can ensure that the modification is sufficiently tested, and reduce the risk of software product defects.

According to an embodiment of the present disclosure, for operation S220 shown in fig. 2 above, selecting M target test cases from N initial test cases that satisfy a preset condition may include operations of performing vector conversion on each of the N initial test cases to obtain N vectors, where the initial test cases correspond to the vectors one by one, and selecting M target test cases from the N initial test cases that satisfy the preset condition based on similarity between any two vectors from the N vectors.

According to embodiments of the present disclosure, for each initial test case, the textual descriptions in the initial test case may be converted to a numerical vector. It may be determined whether two vectors are similar by determining the similarity between any two vectors, which may be considered similar if the similarity between any two vectors is greater than a threshold.

According to an embodiment of the present disclosure, the preset condition may be used to indicate dissimilarity between two vectors corresponding to any two initial test cases, that is, dissimilarity between two vectors corresponding to any two target test cases among the M target test cases.

According to the embodiment of the disclosure, M target vectors which are dissimilar between any two vectors can be screened from N vectors according to the similarity between any two vectors. And determining the initial test case corresponding to each of the M target vectors as a target test case.

According to the embodiment of the disclosure, due to vector conversion, the target test cases are screened based on vector similarity, and the similar test cases can be rapidly identified through the vector similarity, so that repeated tests are reduced, and the test efficiency is improved. In addition, for the target test cases with larger difference, the coverage rate of the test is improved, and the test cases can cover more code paths.

In implementing the embodiments of the present disclosure, it was found that if the test cases were directly vectorized, when the test cases contained complex business logic and conditions, the rich semantic information and context in the test cases could not be captured completely. Furthermore, if deep analysis or debugging is required for the test cases, the interpretability is poor.

Based on this, vector conversion is performed on each of the N initial test cases, respectively, to obtain N vectors in embodiments of the present disclosure, which may include operations of converting the initial test case into an image in a structured form for each of the N initial test cases, and converting the image into a vector based on a graph embedding technique.

Fig. 3 schematically illustrates a screening schematic of a target test case according to an embodiment of the present disclosure.

As shown in fig. 3, the initial test case 1, the initial test case 2, the initial test case N may be subjected to image conversion in a structured form, respectively, to obtain an image 1, an image 2, an image N, respectively. Based on the graph embedding technique, image 1, image 2, image N were vector-converted, respectively, resulting in vector 1, vector 2, vector N. Initial test case 1, image 1, and vector 1 have a mapping relationship, and so on, initial test case N, image N, and vector N have a mapping relationship. The similarity between any two vectors can be calculated, and M target vectors which are dissimilar between any two vectors can be screened from N vectors based on the similarity. And determining the initial test case corresponding to each vector in the M target vectors as a target test case to obtain M target test cases.

According to embodiments of the present disclosure, image conversion of initial test case 1, initial test case 2, and initial test case N, respectively, in a structured form may include operations in which, for each initial test case, data in the initial test case may be represented in a graphical form, such as a flowchart, a state diagram, a control flow graph (Control Flow Graph, CFG diagram), etc., so that the structure and flow of the test case may be intuitively presented.

According to an embodiment of the disclosure, based on a graph embedding technique, respectively vector-converting image 1, image 2, image N to obtain vector 1, vector 2, image N may include an operation that, for each image, the image may be encoded using a convolutional neural network to obtain a corresponding vector.

According to embodiments of the present disclosure, vector converting image 1, image 2, and image N, respectively, to obtain vector 1, vector 2, vector N may further include, for each image, mapping image data to a low-dimensional space using a dimension reduction technique (e.g., PCA) to obtain a dimension reduced image, which helps to reduce complexity of the data and extract the most important features. Extracting image features from the reduced-dimension image, and vectorizing the image features to obtain corresponding vectors.

According to the embodiment of the disclosure, abstract test cases can be represented as visual structured images through patterning, so that the operability of the test cases is enhanced, and the deep analysis or debugging of the test cases is facilitated. The image is converted into the vector by means of the graph embedding technology, key information and structural characteristics of the image are reserved, similarity among the test cases can be determined accurately, and compared with the direct vectorization of the test cases, when the test cases contain complex business logic and conditions, rich semantic information and context relations in the test cases can be captured.

According to an embodiment of the present disclosure, the present disclosure performs similarity detection and deduplication processing on the generated initial test cases through basic graph theory and graph embedding techniques. In addition, a collaborative learning mechanism can be introduced, the generation of initial test cases is continuously learned and optimized in the process of executing target test cases, invalid and redundant tests are reduced, and the utilization rate of test resources is improved.

According to the embodiment of the disclosure, the method for screening M target test cases meeting preset conditions from N initial test cases based on the similarity between any two vectors in N vectors can comprise the operations of determining a first vector pair with similarity greater than a threshold value and a second vector pair with similarity less than or equal to the threshold value when the similarity between any two vectors in N vectors is determined to be greater than the threshold value, determining an initial test case corresponding to any one vector in the first vector pair and an initial test case corresponding to a second vector pair as a target test case when the common vector does not exist between the first vector pair and the second vector pair, and determining the initial test case corresponding to the second vector pair as the target test case when the common vector exists between the first vector pair and the second vector pair.

According to an embodiment of the present disclosure, the first vector pair is used to indicate that there is similarity between vectors. The second vector pair is used to indicate that there is no similarity between the vectors.

For example, where first vector pair vector 1 is similar to vector 2 and second vector pair vector 2 is dissimilar to vector 3, it may be determined that there is a common vector 2 between the first vector pair and the second vector pair.

According to the embodiment of the disclosure, the redundant test cases are determined through vector similarity, and then the initial test cases are accurately screened to remove the redundant test cases, so that the target test cases are obtained.

According to the embodiment of the disclosure, for the operation S230 shown in fig. 2 above, when determining that M is greater than or equal to 2, prioritizing the M target test cases based on the code quality index may include the operations of, for each of the M target test cases, extracting features of the target test cases based on the code quality index to obtain initial features, screening the initial features to obtain target features, determining a risk evaluation value for the target test case based on a feature value corresponding to the target features and a weight value corresponding to the target features, and prioritizing the M target test cases based on the risk evaluation value of each target test case.

According to the embodiment of the disclosure, a suitable machine learning algorithm, such as a decision tree, a random forest, a neural network, and the like, may be selected to extract features from the target test case to obtain initial features. The most relevant features are selected by using methods such as correlation analysis, mutual information, recursive Feature Elimination (RFE) and the like, and further feature importance assessment, such as feature importance score screening of random forests, is used for obtaining target features.

According to an embodiment of the present disclosure, the target feature may be input into a risk assessment model, and a risk assessment value may be output, the risk assessment value being a numerical value between 0 and 1, where 1 represents the highest risk.

According to embodiments of the present disclosure, data of historical change records, defect reports, code complexity, coupling between historical test cases, and the like of historical production cases may be collected and consolidated. Extracting useful features from the data, removing missing values and abnormal values, for example, the code complexity can be measured by indexes such as circle complexity, code line number, method length and the like, changing frequency can count changing times in a past period, defect density can calculate the proportion of the defect number of each historical test case to the code line number, analyze the dependency relationship among the historical test cases and calculate the coupling degree of the historical test cases. And dividing the extracted historical features into a training set and a testing set, evaluating the performance of the risk assessment model by using methods such as cross verification and the like, and ensuring the generalization capability of the model.

According to embodiments of the present disclosure, a risk threshold may be set, with test cases having risk assessment values above the risk threshold marked as high priority, ensuring that these cases are performed first.

According to the embodiment of the disclosure, when new test cases are generated, the priority ranking can be performed based on the risk assessment, and the ranked test cases are sequentially distributed to the test environment for execution.

According to the embodiment of the disclosure, the target test case is subjected to feature extraction and then is screened again based on the code quality index, so that the obtained target features are important features aiming at the target test case, the risk evaluation value is determined based on the important features, the risk of the field and the risk of the quality characteristics are favorably identified, the high-risk priority is tested, the defects in the test can be found, and the quality and the stability of the software product are improved.

According to embodiments of the present disclosure, the code quality index may include at least one of code complexity, code change frequency, defect density.

According to the embodiment of the disclosure, the feature extraction is performed on the target test case based on the code quality index to obtain initial features, and the method can comprise the operations of extracting circle complexity, code line number and method length features from the target test case based on the code complexity to obtain the initial features, and/or extracting code change frequency features from the target test case based on the code change frequency to obtain the initial features, and/or extracting the proportion features of the defect number and the code line number from the target test case based on the defect density to obtain the initial features.

Fig. 4 schematically illustrates a schematic diagram of determining risk assessment values for a target test case according to an embodiment of the present disclosure.

As shown in fig. 4, for each target test case 401, the circle complexity, number of code lines, and method length features 402 may be extracted from the target test case 401 based on the code complexity. The code change times feature 403 may be extracted from the target test case 401 based on the code change frequency. Based on the defect density, a proportional feature 404 of the number of defects to the number of code lines is extracted from the target test case 401. The circle complexity, number of code lines, and method length feature 402, substitution code change number feature 403, and ratio of defect number to number of code lines feature 404 may be determined as initial feature 405.

Feature importance screening is performed on the initial features 405 to obtain target features 406. Based on the feature value corresponding to the target feature 406 and the weight value corresponding to the target feature 406, a risk assessment value 407 for the target test case 401 is determined.

According to the embodiment of the disclosure, the code complexity is characterized by the circle complexity, the code line number and the method length, so that the code logic of the test case can be predicted, and the test and maintenance are facilitated. The code change frequency is represented by the code change times, so that the test efficiency is improved. The defect density is characterized by the ratio of the defect number to the number of code lines, which is beneficial to reducing the defect rate. The combination of the three is beneficial to accurately determining the risk condition of the target test case.

Fig. 5 schematically illustrates a flow chart of a case processing method according to another embodiment of the present disclosure.

As shown in fig. 5, a case generation model M may be trained based on a test case library 501. The demand text 502 and the production cases 503 are input into the case creation model M, and N initial test cases 504 are output. The N initial test cases 504 are deduplicated to obtain M target test cases 505. The M target test cases 505 are prioritized based on the code quality index. The M ordered target test cases 505 are sequentially sent to the test environment in order of priority, so that the target test cases 505 are sequentially executed in the test environment. Performance metrics 507 are sequentially obtained for the execution state 506 of the target test case 505 in relation to the target test case. The real problem 508 that occurs in the production environment is correlated with the performance metrics 507 associated with the target test case 505 and the execution state 506 of the target test case 505, identifying a defect problem 509. From the defect problem 509, a defect analysis result 510 is determined. Based on the defect analysis results 510, the test case library 501 is updated.

In accordance with an embodiment of the present disclosure, the defect problem 509 may include, for example, but is not limited to, a problem of a genetic point or abnormal situation, etc. The execution state 506 includes an execution success state and an execution failure state. The target test case related performance metrics 507 may include, but are not limited to, test coverage, execution time, resource utilization, etc.

According to embodiments of the present disclosure, case generation models may be built using Bi-directional long-short term memory networks (Bi-LSTM), self-Attention mechanisms (Self-Attention), or transformations, etc., based on deep-learned sequence models. The NLP tool is used to extract key information in the test case library 501 such as function definitions, class structures, business logic descriptions in notes, etc. Based on the correspondence between the key information and the source code, training cases to generate a model.

According to the embodiment of the disclosure, the test case library can be updated in real time by utilizing the feedback of the historical test data and the execution condition of the newly generated target test case, so that the case generation model is updated, and the high-quality test case can be generated more accurately.

According to the embodiment of the disclosure, the case processing method builds a complete feedback mechanism, updates the test case library by using the defect analysis result, further feeds back the case generation model, continuously improves the accuracy of the model for generating effective cases, forms a virtuous circle, can realize automatic and intelligent test case generation, management and execution, has good compatibility and closed loop feedback and continuous optimization capability, and effectively improves the quality and efficiency of production verification.

Based on the case processing method, the disclosure also provides a case processing device. The device will be described in detail below in connection with fig. 6.

Fig. 6 schematically shows a block diagram of a case processing apparatus according to an embodiment of the present disclosure.

As shown in fig. 6, the case processing apparatus 600 of this embodiment includes a generating module 610, a screening module 620, a sorting module 630, and a transmitting module 640.

The generating module 610 is configured to generate N initial test cases based on the production cases and the requirement text, where the requirement text is used to indicate modification information of source codes for the production cases, and N is an integer greater than or equal to 2. In an embodiment, the generating module 610 may be configured to perform the operation S210 described above, which is not described herein.

The screening module 620 is configured to screen M target test cases that satisfy a preset condition from N initial test cases, where M is greater than 0 and less than or equal to N, and is an integer. In an embodiment, the filtering module 620 may be configured to perform the operation S220 described above, which is not described herein.

The sorting module 630 is configured to prioritize the M target test cases based on a code quality indicator when M is greater than or equal to 2, where the code quality indicator is used to indicate a risk condition of the target test cases. In an embodiment, the sorting module 630 may be configured to perform the operation S230 described above, which is not described herein.

The sending module 640 is configured to send the M ordered target test cases to the test environment in order of priority, so as to execute the target test cases in the test environment in order. In an embodiment, the sending module 640 may be configured to perform the operation S240 described above, which is not described herein.

According to the embodiment of the disclosure, through the linkage work of the four modules, the whole production verification case processing device realizes automatic and intelligent full life cycle management, and improves the testing efficiency and quality level of software products.

According to an embodiment of the present disclosure, the sorting module 630 may include an extraction unit, a first sub-filtering unit, a determination unit, and a sub-sorting unit.

For each target test case in the M target test cases, the extraction unit is used for extracting the characteristics of the target test case based on the code quality index to obtain initial characteristics. The first sub-screening unit is used for screening the initial characteristics to obtain target characteristics. The determining unit is used for determining a risk assessment value for the target test case based on the feature value corresponding to the target feature and the weight value corresponding to the target feature. The sub-ranking unit is used for prioritizing the M target test cases based on the risk assessment value of each target test case.

According to an embodiment of the present disclosure, the filtering module 620 includes a vector conversion unit and a second sub-filtering unit. The vector conversion unit is used for carrying out vector conversion on each initial test case in the N initial test cases respectively to obtain N vectors, wherein the initial test cases correspond to the vectors one by one. The second sub-screening unit is used for screening M target test cases meeting preset conditions from N initial test cases based on similarity between any two vectors in the N vectors.

According to an embodiment of the present disclosure, the change information includes a change code segment, and the generation module 610 may include a parsing unit and a deriving unit. The analysis unit is used for analyzing the production case based on the structure, the annotation and the requirement specification of the source code to obtain project information. The deduction unit is used for deducting and generating N initial test cases based on the changed code fragments and project information.

Any of the generation module 610, the screening module 620, the ordering module 630, and the transmission module 640 may be combined in one module to be implemented, or any of the modules may be split into multiple modules, according to embodiments of the present disclosure. Or at least some of the functionality of one or more of the modules may be combined with, and implemented in, at least some of the functionality of other modules. At least one of the generation module 610, the screening module 620, the ordering module 630, and the transmission module 640 may be implemented at least in part as hardware circuitry, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a system on a chip, a system on a substrate, a system on a package, an Application Specific Integrated Circuit (ASIC), or may be implemented in hardware or firmware in any other reasonable way of integrating or packaging circuitry, or in any one of or a suitable combination of three of software, hardware, and firmware. Or at least one of the generation module 610, the screening module 620, the ordering module 630, and the transmission module 640 may be at least partially implemented as a computer program module that, when executed, performs the corresponding functions.

As shown in fig. 7, an electronic device 700 according to an embodiment of the present disclosure includes a processor 701 that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 702 or a program loaded from a storage section 708 into a Random Access Memory (RAM) 703. The processor 701 may include, for example, a general purpose microprocessor (e.g., a CPU), an instruction set processor and/or an associated chipset and/or a special purpose microprocessor (e.g., an Application Specific Integrated Circuit (ASIC)), or the like. The processor 701 may also include on-board memory for caching purposes. The processor 701 may comprise a single processing unit or a plurality of processing units for performing different actions of the method flows according to embodiments of the disclosure.

In the RAM 703, various programs and data necessary for the operation of the electronic apparatus 700 are stored. The processor 701, the ROM 702, and the RAM 703 are connected to each other through a bus 704. The processor 701 performs various operations of the method flow according to the embodiments of the present disclosure by executing programs in the ROM 702 and/or the RAM 703. Note that the program may be stored in one or more memories other than the ROM 702 and the RAM 703. The processor 701 may also perform various operations of the method flow according to embodiments of the present disclosure by executing programs stored in one or more memories.

According to an embodiment of the present disclosure, the electronic device 700 may further include an input/output (I/O) interface 705, the input/output (I/O) interface 705 also being connected to the bus 704. The electronic device 700 may also include one or more of an input portion 706 including a keyboard, mouse, etc., an output portion 707 including a Cathode Ray Tube (CRT), liquid Crystal Display (LCD), etc., and speaker, etc., a storage portion 708 including a hard disk, etc., and a communication portion 709 including a network interface card such as a LAN card, modem, etc., connected to an input/output (I/O) interface 705. The communication section 709 performs communication processing via a network such as the internet. The drive 710 is also connected to an input/output (I/O) interface 705 as needed. A removable medium 711 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 710 as necessary, so that a computer program read therefrom is mounted into the storage section 708 as necessary.

The present disclosure also provides a computer-readable storage medium that may be included in the apparatus/device/system described in the above embodiments, or may exist alone without being assembled into the apparatus/device/system. The computer-readable storage medium carries one or more programs which, when executed, implement methods in accordance with embodiments of the present disclosure.

According to embodiments of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, which may include, for example, but is not limited to, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this disclosure, a computer-readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. For example, according to embodiments of the present disclosure, the computer-readable storage medium may include ROM 702 and/or RAM 703 and/or one or more memories other than ROM 702 and RAM 703 described above.

Embodiments of the present disclosure also include a computer program product comprising a computer program containing program code for performing the methods shown in the flowcharts. The program code, when executed in a computer system, causes the computer system to perform the methods provided by embodiments of the present disclosure.

The above-described functions defined in the system/apparatus of the embodiments of the present disclosure are performed when the computer program is executed by the processor 701. The systems, apparatus, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the disclosure.

In one embodiment, the computer program may be based on a tangible storage medium such as an optical storage device, a magnetic storage device, or the like. In another embodiment, the computer program may also be transmitted, distributed over a network medium in the form of signals, downloaded and installed via the communication section 709, and/or installed from the removable medium 711. The computer program may comprise program code that is transmitted using any appropriate network medium, including but not limited to wireless, wireline, etc., or any suitable combination of the preceding.

In such an embodiment, the computer program may be downloaded and installed from a network via the communication portion 709, and/or installed from the removable medium 711. The above-described functions defined in the system of the embodiments of the present disclosure are performed when the computer program is executed by the processor 701. The systems, devices, apparatus, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the disclosure.

According to embodiments of the present disclosure, program code for performing computer programs provided by embodiments of the present disclosure may be written in any combination of one or more programming languages, and in particular, such computer programs may be implemented in high-level procedural and/or object-oriented programming languages, and/or assembly/machine languages. Programming languages include, but are not limited to, such as Java, c++, python, "C" or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, partly on a remote computing device, or entirely on the remote computing device or server. In the case of remote computing devices, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., connected via the Internet using an Internet service provider).

The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

Those skilled in the art will appreciate that the features recited in the various embodiments of the disclosure may be combined and/or combined in various combinations, even if such combinations or combinations are not explicitly recited in the disclosure. In particular, features recited in various embodiments of the present disclosure may be combined and/or combined in various ways without departing from the spirit and teachings of the present disclosure. All such combinations and/or combinations fall within the scope of the present disclosure.

The embodiments of the present disclosure are described above. These examples are for illustrative purposes only and are not intended to limit the scope of the present disclosure. Although the embodiments are described above separately, this does not mean that the measures in the embodiments cannot be used advantageously in combination. Various alternatives and modifications can be made by those skilled in the art without departing from the scope of the disclosure, and such alternatives and modifications are intended to fall within the scope of the disclosure.

Claims

1. A method of case processing, the method comprising:

Generating N initial test cases based on production cases and a demand text, wherein the demand text is used for indicating change information of source codes aiming at the production cases, and N is an integer greater than or equal to 2;

screening M target test cases meeting preset conditions from N initial test cases, wherein M is larger than 0 and smaller than or equal to N, and is an integer;

Prioritizing M of the target test cases based on a code quality indicator indicating a risk condition of the target test cases if M is greater than or equal to 2, and

And sequentially sending the M ordered target test cases to a test environment according to the order of the priorities so as to sequentially execute the target test cases in the test environment.

2. The method of claim 1, wherein prioritizing the M target test cases based on a code quality indicator if M is determined to be ≡2, comprises:

for each of the M target test cases:

performing feature extraction on the target test case based on the code quality index to obtain initial features;

screening the initial characteristics to obtain target characteristics;

Determining a risk assessment value for the target test case based on the feature value corresponding to the target feature and the weight value corresponding to the target feature;

and prioritizing the M target test cases based on the risk assessment value of each target test case.

3. The method of claim 2, wherein the code quality indicator comprises at least one of code complexity, code change frequency, defect density;

the feature extraction is performed on the target test case based on the code quality index to obtain initial features, including:

Extracting circle complexity, code line number and method length characteristics from the target test case based on the code complexity to obtain the initial characteristics, and/or

Extracting code change times characteristics from the target test case based on the code change frequency to obtain the initial characteristics, and/or

And extracting proportional features of the defect number and the code line number from the target test case based on the defect density to obtain the initial features.

4. The method of claim 1, wherein the screening the M target test cases from the N initial test cases to satisfy a preset condition comprises:

Vector conversion is carried out on each initial test case in the N initial test cases to obtain N vectors, wherein the initial test cases correspond to the vectors one by one, and

And screening M target test cases meeting preset conditions from the N initial test cases based on the similarity between any two vectors in the N vectors.

5. The method of claim 4, wherein the performing vector conversion on each of the N initial test cases to obtain N vectors comprises:

for each of the N initial test cases:

converting the initial test case into an image in a structured form, and

The image is converted to the vector based on a graph embedding technique.

6. The method of claim 4, wherein the selecting M target test cases from the N initial test cases that satisfy a preset condition based on a similarity between any two of the N vectors comprises:

in the case that the similarity between any two vectors in N vectors is larger than a threshold value, determining a first vector pair with the similarity larger than the threshold value and a second vector pair with the similarity smaller than or the threshold value;

Determining the initial test case corresponding to any one of the first vector pair and the initial test case corresponding to the second vector pair as the target test case in the case that no common vector exists between the first vector pair and the second vector pair

In the event that it is determined that the common vector exists between the first vector pair and the second vector pair, the initial test case corresponding to the second vector pair vector is determined to be the target test case.

7. The method of any one of claims 1-6, wherein the change information comprises a change code segment;

the generating N initial test cases based on the production cases and the demand text includes:

Parsing the production case based on the structure, comments, and requirement specifications of the source code to obtain project information, and

Deriving N initial test cases based on the altered code segments and the project information.

8. A case processing apparatus, the apparatus comprising:

The generating module is used for generating N initial test cases based on production cases and a demand text, wherein the demand text is used for indicating change information of source codes aiming at the production cases, and N is an integer greater than or equal to 2;

The screening module is used for screening M target test cases meeting preset conditions from N initial test cases, wherein M is larger than 0 and smaller than or equal to N, and the M is an integer;

A sorting module for prioritizing M target test cases based on a code quality index for indicating risk conditions of the target test cases when M is determined to be greater than or equal to 2, and

And the sending module is used for sequentially sending the M ordered target test cases to a test environment according to the order of the priorities so as to sequentially execute the target test cases in the test environment.

9. An electronic device, comprising:

One or more processors;

A memory for storing one or more computer programs,

Characterized in that the one or more processors execute the one or more computer programs to implement the steps of the method according to any one of claims 1-7.

10. A computer-readable storage medium, on which a computer program or instructions is stored, which, when executed by a processor, carries out the steps of the method according to any one of claims 1 to 7.

11. A computer program product comprising a computer program or instructions which, when executed by a processor, implement the steps of the method according to any one of claims 1 to 7.