Data processing method and device, storage medium and program product

Abstract

The invention discloses a data processing method, computer equipment, a computer readable storage medium and a computer program product, and the method comprises the steps: processing first feature data of a first task based on a preset logic function, and obtaining a first probability of the first feature data; determining a first decision node matched with the first probability of each piece of first feature data according to the first probability of the first feature data, at least one decision node configured for each piece of first feature data and a connection relationship between the decision nodes, and determining a first strategy corresponding to the first feature data based on the first decision node; therefore, the decision-making process with unified decision-making logic is provided when strategies are determined by different tasks, and the decision-making stability and reliability of the first task can be guaranteed through the decision-making process.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to a data processing method, a computer device, a computer-readable storage medium, and a computer program product. Background Art

[0002] Currently, to ensure that tasks are completed according to predetermined results and standards, and to guarantee quality, existing technologies typically assign specialized personnel to these tasks. For example, these personnel rely on their accumulated experience to assess task quality and make relevant decisions, ensuring smooth and efficient execution. Consequently, task quality in these technologies relies heavily on the expertise of these personnel, and stability and reliability cannot be guaranteed. Summary of the Invention

[0003] In response to the above technical problems, the embodiments of the present application provide a data processing method, a computer device, a computer-readable storage medium, and a computer program product, which can evaluate the first task through an automated process to determine the strategy, effectively improve the accuracy of the strategy recommendation for the task, and ensure the quality of the task.

[0004] In a first aspect, an embodiment of the present application provides a data processing method, comprising:

[0005] Processing the first feature data of the first task based on a preset logic function to obtain a first probability of the first feature data;

[0006] Determining, based on the first probability of the first feature data, at least one decision node configured for each of the first feature data, and a connection relationship between the decision nodes, a first decision node that matches the first probability of each of the first feature data;

[0007] A first strategy corresponding to the first feature data is determined based on the first decision node.

[0008] In a second aspect, an embodiment of the present application provides a data processing device, including:

[0009] a processing unit, configured to process the first feature data of the first task based on a preset logic function to obtain a first probability of the first feature data;

[0010] A determination unit is used to determine the first decision node that matches the first probability of each first feature data based on the first probability of the first feature data, at least one decision node configured for each first feature data, and the connection relationship between the decision nodes; and determine the first strategy corresponding to the first feature data based on the first decision node.

[0011] In a third aspect, an embodiment of the present application further provides a computer device, comprising a memory storing a plurality of instructions; a processor loading instructions from the memory to execute the steps of any data processing method provided in the embodiment of the present application.

[0012] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium, which stores a plurality of instructions suitable for loading by a processor to execute the steps of any data processing method provided in an embodiment of the present application.

[0013] In a fifth aspect, an embodiment of the present application further provides a computer program product, comprising a computer program or instructions, which, when executed by a processor, implements the steps of any data processing method provided in the embodiment of the present application.

[0014] By adopting the scheme of the embodiment of the present application, the first feature data of the first task can be processed based on a preset logical function to obtain the first probability of the first feature data; the first decision node that matches the first probability of each first feature data is determined based on the first probability of the first feature data, at least one decision node configured for each first feature data, and the connection relationship between the decision nodes; the first strategy corresponding to the first feature data is determined based on the first decision node. Thus, different first tasks can determine the strategy adapted to them through a plurality of reasonably set decision nodes and a fixed connection relationship between the decision nodes. By pre-setting a plurality of decision nodes and a fixed connection relationship between the decision nodes, a decision process with unified decision logic is provided when determining strategies for different tasks. Compared with the artificial subjective strategy for the first task based on personal experience, the decision process of the present application can ensure the decision stability and reliability of the first task. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0016] Figure 1 Schematic diagram of the application environment of the data processing method provided in the embodiment of the present application;

[0017] Figure 2 This is a flow chart of an embodiment of the data processing method provided in the embodiments of the present application;

[0018] Figure 3 This is another example flow chart of the data processing method provided in the embodiments of the present application;

[0019] Figure 4 This is another example flow chart of the data processing method provided in the embodiments of the present application;

[0020] Figure 5 Schematic diagram of a decision tree model of the data processing method provided in the embodiments of the present application;

[0021] Figure 6 This is another example flow chart of the data processing method provided in the embodiments of the present application;

[0022] Figure 7 is a structural diagram of a data processing device provided in an embodiment of the present application;

[0023] Figure 8 It is a schematic diagram of the internal structure of the computer device provided in the embodiment of the present application. DETAILED DESCRIPTION

[0024] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without making creative work are within the scope of protection of this application. At the same time, in the description of the embodiments of the present application, the terms "first", "second", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more features. In the description of the embodiments of the present application, the meaning of "multiple" is two or more, unless otherwise clearly and specifically defined.

[0025] In one embodiment of the present application, the data processing method can be run on a local terminal device or a server. When the data processing method is run on a server, the method can be implemented and executed based on a cloud interaction system, wherein the cloud interaction system includes a server and a client device.

[0026] In order to better understand the data processing method, computer device, computer-readable storage medium and computer program product provided by the embodiments of the present application, the application environment applicable to the embodiments of the present application is described below.

[0027] See also Figure 1 , Figure 1 Schematic diagram of an application environment of the data processing method provided by an embodiment of the present application is shown. As an implementation method, the data processing method provided by the embodiment of the present application can be applied to the same electronic device. The electronic device can be Figure 1The server 110 shown in FIG. 1 can be connected to the terminal device 120 via a network. The network is used to provide a medium for a communication link between the server 110 and the terminal device 120. The network can include various connection types, such as wired communication links, wireless communication links, etc., which are not limited in the embodiments of the present application. Alternatively, in other embodiments, the electronic device can also be a smartphone, a laptop computer, etc.

[0028] It should be understood that Figure 1 The server 110, network, and terminal device 120 are merely illustrative. Any number of servers, networks, and terminal devices may be provided as needed. For example, the server 110 may be a physical server or a server cluster consisting of multiple servers, and the terminal device 120 may be a mobile phone, tablet, desktop computer, laptop computer, or the like. It will be appreciated that embodiments of the present application may also allow multiple terminal devices 120 to access the server 110 simultaneously.

[0029] In some embodiments, a user can propose a task through the terminal device 120, and the terminal device 120 sends the task to the server 110 through the network. The server 110 can obtain task data for the task and extract at least one feature data of the task from the task data. These feature data are processed through the data processing method of the embodiment of the present application to obtain a strategy for reference and implementation.

[0030] The type of the task in this embodiment is not limited. In an optional example, the task may be a task in different stages of a project.

[0031] The following is a detailed description of each step in conjunction with the accompanying drawings. In this embodiment, the execution subject is a terminal device. It should be noted that the order in which the following embodiments are described does not limit the preferred order of the embodiments. Although the flowcharts illustrate a logical order, in some cases, the steps shown or described may be performed in a different order than that shown in the accompanying drawings.

[0032] In existing technologies, to ensure the normal operation of tasks, quality assurance personnel are typically responsible for assessing and deciding on task quality risks. This approach relies heavily on manual experience, and cannot guarantee the accuracy, stability, and efficiency of task assessment and decision-making.

[0033] Please also refer to Figures 2 to 6 , the data processing method provided by this application is described below. Figure 3 The specific process of the data processing method can be as follows: Step 301 to Step 302, wherein:

[0034] Step 301 : Process first feature data of a first task based on a preset logic function to obtain a first probability of the first feature data.

[0035] To facilitate reading later in the text, we will first explain the professional terms that appear in the text.

[0036] One-Hot Encoding: One-Hot Encoding is a method of converting categorical variables into a form that is easy for machine learning algorithms to process.

[0037] CICD: Continuous Integration (CI: Continuous Integration) Continuous Delivery (CD: Continuous Delivery) system.

[0038] QA: QA is the abbreviation of Quality Assurance, which means quality assurance. In project management, QA is a quality assurance engineer who is responsible for ensuring that the output of the project meets the expected quality requirements.

[0039] Feature data: Feature data refers to data used to describe the attributes or characteristics of data objects in fields such as data analysis, machine learning, and data mining.

[0040] Logistic regression: It is a commonly used statistical method for solving classification problems, mainly used to predict the probability of an event occurring given a set of features.

[0041] Decision tree algorithm: It is a commonly used machine learning method for classification and regression tasks.

[0042] In this example, the type and objective of the first task are not limited. The first and second tasks in this example can belong to the same project or different projects. This example has no restrictions on this.

[0043] In an optional example, the first characteristic data of the first task may be obtained from first task data in at least one platform associated with the first task.

[0044] See also Figure 2 In a specific embodiment, a user can create a task through a task management system, such as proposing a demand to create a first task. After receiving the first task proposed by the user, the task management system can automatically predict the quality risk of the first task based on the key risk factors of the first task (i.e., first characteristic data, such as demand characteristics, personnel characteristics, service portraits, historical characteristics, quality infrastructure characteristics, etc.), and determine whether to arrange QA (Quality Assurance) resources based on the predicted quality risks.

[0045] Furthermore, the predicted quality risk can be graded. If the quality risk is low, R&D will conduct self-testing, while if the quality risk is high, quality assurance personnel will intervene in the test.

[0046] Optionally, before step 301 , the method may further include: acquiring first task data of the first task from a platform associated with the first task, and extracting first feature data of the first task from the first task data.

[0047] Optionally, the first task data required for data processing may be pre-set, for example, a task data identifier such as a document name and other information may be set, and the first task data may be obtained according to the task data identifier.

[0048] In the embodiment of the present application, the first task may include a demand task, see Figure 4 , through the demand management system and CICD (Continuous Integration Continuous Delivery) pipeline, quality risk prediction for the demand task can be triggered. The feature engine can obtain the first task data from the first task-related platforms, such as the demand management system, continuous integration continuous delivery (CICD) system, service desk, test platform, etc., to extract the first feature data.

[0049] The first task data includes, but is not limited to, requirement documents related to the first task, release rollback records, application information, historical defects, and user feedback information.

[0050] It is understandable that the platform associated with the above-mentioned first task can be set according to actual needs and is not limited to the above-mentioned examples of this application.

[0051] The feature engine can further select first feature data related to the defect prediction of the first task, and after processing the first feature data, reach a first probability of the first feature data, thereby determining corresponding strategies and conclusions based on the first probability, thereby performing task risk assessment and management through automation, thereby saving time for task risk assessment and management, simplifying the specific steps and processes of task risk assessment and risk management, and improving the efficiency of task risk assessment and association; and, by performing risk assessment on tasks through an automated process, it is possible to objectively assess the risk of tasks and effectively improve the accuracy of the assessment results of task risk assessment.

[0052] Among them, the first characteristic data includes but is not limited to: at least one or more of demand characteristics, application characteristics, R&D experience characteristics, service portrait characteristics, and quality infrastructure characteristics.

[0053] It should be noted that demand characteristics include the module of the requirement, the number of other requirements it depends on, the frequency of changes, the urgency of the requirement, the terminal type, and the R&D schedule; application characteristics include the application level and the number of interfaces that the application depends on.

[0054] R&D experience characteristics include the number of recent R&D release rollbacks and the recent R&D defect rate.

[0055] Service profile features refer to the description of the functions and service scope provided by a service or product; service profile features may include recent user complaint rates and recent number of user work orders.

[0056] Quality infrastructure is a set of systems and practices established to ensure product quality. It is used to maintain and improve product quality during the software development life cycle. Quality infrastructure characteristics may include unit test coverage and automated test coverage.

[0057] In summary, collecting task data related to the first task from multiple platforms can provide more comprehensive task data, provide support for data analysis and decision-making, and ensure the comprehensiveness and completeness of task data.

[0058] In an optional example, the preset logic function can be provided by multiple models. This example does not limit the source and type of the preset logic function. Optionally, the preset logic function can be provided by a trained logistic regression model.

[0059] In step 301, a first probability is predicted for the first feature data of the first task based on a preset logic function to obtain a first probability corresponding to each first feature data;

[0060] Optionally, the selected first feature data can be processed and calculated through One-Hot Encoding and then submitted to a trained logistic regression model. The first probability of the existence of a defect can be predicted based on the model. The greater the probability of the defect, the higher the quality risk.

[0061] Based on the regression model prediction, the first probabilities of demand characteristics, application characteristics, R&D experience characteristics, service portrait characteristics, and quality infrastructure characteristics can be calculated respectively.

[0062] Step 302: Determine the first decision node that matches the first probability of each first feature data based on the first probability of the first feature data, at least one decision node configured for each first feature data, and the connection relationship between the decision nodes; and determine the first strategy corresponding to the first feature data based on the first decision node.

[0063] The first policy is executed, and if the first policy indicates adjustment of the first probability, the first probability is adjusted. That is, if the first policy indicates adjustment of the first feature data, and the change in the first feature data affects the first probability, the first policy indicates adjustment of the first probability.

[0064] In this example, there are multiple types of first feature data. For each type of first feature data, this example configures at least one decision node. There is a connection relationship between different decision nodes. In this example, at least some decision nodes can be configured with the first feature data to which they belong. The first feature data to which two interconnected decision nodes belong can be of different types. The decision node can also be configured with a strategy for the first feature data to which the decision node connected to it belongs.

[0065] Optionally, a decision node can be connected to multiple decision nodes, and these multiple decision nodes can include different strategies for the same first feature data. Therefore, in this example, for the first feature data belonging to a certain decision node, the first decision node that matches the first feature data can be determined based on the first probability from the multiple decision nodes connected to it, thereby obtaining the strategy for the first feature data from the first decision node. It can be understood that for the first feature data belonging to the first decision node, a similar method can be used to determine the strategy again, and repeat the process until the last decision node is found.

[0066] Optionally, the decision node in this example may be a node in a decision tree, and step 302 may include:

[0067] Based on a first probability of first feature data in a first decision node of the decision tree, determining a first decision node hit by a first probability among multiple decision nodes connected to the first decision node;

[0068] Obtaining a first strategy corresponding to the first feature data from a first decision node, wherein the first decision node is configured with the corresponding first feature data;

[0069] Based on the first probability of the first feature data to which the first decision node belongs, determining a new first decision node hit by the first probability from a plurality of decision nodes connected to the first decision node;

[0070] Returning to the step of obtaining the first strategy corresponding to the first feature data from the first decision node, and determining, based on the first probability of the first feature data belonging to the first decision node, a new first decision node hit by the first probability from a plurality of decision nodes connected to the first decision node, until the decision tree execution is completed;

[0071] The type of the first feature data in each first decision node is different.

[0072] Optional, such as Figure 5 As shown, this example can set corresponding risk mitigation strategies (i.e., policies) for different first feature data, use a decision tree algorithm to train the model, and branch according to the risk level (i.e., the first probability level), thereby forming a series of decision nodes. Specifically, a decision tree model can be constructed using a machine learning library (Scikit-learn, an open source machine learning library for the Python programming language), which can branch and form decision nodes based on demand characteristics, application characteristics, R&D experience, service profiles, and quality infrastructure risk levels.

[0073] like Figure 5 As shown, the first decision node can be the root node in the decision tree. The first feature data belonging to the decision node "first probability of demand feature" is "demand feature risk". The decision node is connected to three decision nodes. These three decision nodes are set with another first feature data "R&D experience feature". Different strategies can also be set in these three decision nodes for selection.

[0074] Optionally, in this example, whether to configure a policy in the decision node can be freely set, and this example has no restrictions on this.

[0075] In an optional example, the first feature data belonging to multiple decision nodes connected to the same decision node are the same.

[0076] In this example, a probability level is set for the first probability, and the first decision node that matches each first feature data can be matched based on the probability level. Optionally, the step of "determining a new first decision node hit by the first probability from multiple decision nodes connected to the first decision node" may include: determining the first probability of the first feature data to which the first decision node belongs, and hitting the first probability level among the preset multiple probability levels; and determining, among the multiple decision nodes connected to the first decision node, the decision node corresponding to the first probability level as the new first decision node.

[0077] Similarly, the step of “determining a first decision node with a first probability of being hit among multiple decision nodes connected to the first decision node” may include:

[0078] Determine a first probability of first feature data belonging to a first decision node, hit a first probability level among a plurality of preset probability levels, and determine a decision node corresponding to the first probability level as the first decision node from among a plurality of decision nodes connected to the first decision node.

[0079] For example, see Figure 5If the first probability of a requirement feature is high, the first strategy for the requirement feature can be to have product experts review and control the requirements. If the first probability of the requirement feature is high and the first probability of the R&D experience feature is medium, the first strategy for the R&D experience feature can be code review and self-testing based on smoke test cases. If the first probability of the requirement feature is high, the first probability of the R&D experience feature is medium, and the first probability of the quality infrastructure feature is high, the first strategy for the quality infrastructure feature can be to improve unit test coverage and automate interface testing.

[0080] Optionally, each probability level can have a corresponding probability range. Different probability levels have different corresponding probability ranges. The higher the probability level, the larger the probability value. Optionally, there is no limit on the number of probability levels, for example, 2, 3, 4, etc., which can be set based on the intensity of quality management. This example has no such limit.

[0081] If the first probability belongs to a range of a certain probability level, then the probability level is the first probability level to which the first probability belongs.

[0082] It can be understood that the probability range can also be indicated by multiple first probability thresholds. For example, by setting two first probability thresholds, three probability ranges can be obtained. For example, if the first probability thresholds include 0.2 and 0.6, the three probability ranges include [0-0.2], [0.2-0.6], and (0.6-1].

[0083] Specifically, after the first probabilities of the demand characteristics, application characteristics, R&D experience characteristics, service profile characteristics, and quality infrastructure characteristics have been calculated in step 301, the above-mentioned first probabilities can be input into the decision tree model. The decision tree model executes the decision, sequentially determines the first decision node hit by each type of first feature data, and obtains the first strategy. For example, a first probability <= 0.3 is a low risk level, between 0.3-0.6 is a medium risk level, and greater than 0.6 is a high risk level. The calculated first probability of the demand characteristics is 0.8, the first probability of the R&D experience characteristics is 0.7, the first probability of the quality infrastructure characteristics is 0.3, and the first probability of the application characteristics is 0.3. Based on the rules of the decision tree model, it can be determined that the risk of the demand characteristics is high risk, the risk of the R&D experience characteristics is high risk, and the risk of the quality infrastructure characteristics is low risk. The first strategy includes: 1) having product experts review and control the requirements; 2) focusing on design review, code review, and internal cross-self-testing of R&D; 3) performing unit testing and interface automation testing normally.

[0084] In summary, the embodiment of the present application uses a decision tree model to recommend a first strategy for the first feature data, which can provide data support for the analysis and decision-making process and not rely too much on human experience, making the decision more efficient and reliable. In addition, predicting risks and recommending risk mitigation strategies in the demand stage can enable users to take preventive measures in advance and improve the efficiency of risk management.

[0085] The above solution recommends strategies based on the first feature data. This application also proposes a solution for recommending strategies from the perspective of the overall task. Optionally, the method further includes:

[0086] Calculating a second probability for each first feature data based on the feature weight corresponding to each first feature data and the first probability;

[0087] Summing the second probabilities of the first feature data to obtain a third probability of the first task;

[0088] A second strategy matching the third probability is determined from among the plurality of second strategies for the first task.

[0089] Optionally, the feature weight of each first feature data can be customized, and this example has no limitation on this. Generally, the feature weight can be set based on the degree of impact of the first feature data on the quality of the first task. The greater the quality impact, the greater the feature weight.

[0090] See also Figure 3 In this example, a business rule engine is configured for the quality management of the first task, and the engine can be used to determine the second strategy. The decision tree and feature weights can be configured in the engine.

[0091] For example, a feature weight R is set for each first feature data, and the third probability P of the first task is calculated based on the first probability Pi of each first feature data. The calculation formula of the third probability P is as follows:

[0092]

[0093] Here, i indicates the first feature data in the i-th order, and n is the total number of types of the first feature data.

[0094] For example, the calculated first probability of the demand feature is 0.8, the first probability of the R&D experience feature is 0.7, the first probability of the quality infrastructure feature is 0.3, and the first probability of the application feature is 0.3. Since the demand feature, R&D experience feature, and quality infrastructure feature have a significant impact on the quality of the first task, their weights are set to 25%, the service profile feature is weighted to 15%, and the application feature is weighted to 10%. That is, according to the above formula, the third probability of the first task = the first probability of the demand feature * the feature weight of the demand feature + the first probability of the R&D experience feature * the feature weight of the R&D experience feature + the first probability of the quality infrastructure feature * the feature weight of the quality infrastructure feature + the first probability of the application feature * the feature weight of the application feature + the first probability of the service profile feature * the feature weight of the service profile feature. Therefore, the third probability of the first task = 0.8*0.25+0.7*0.25+0.3*0.25+0.2*0.15+0.3*0.1=0.51.

[0095] In an optional example, at least two second probability levels can be set for the third probability, and a corresponding second strategy can be set for each probability level. The second strategy that matches the third probability can be determined based on the third probability and the preset probability level.

[0096] Optionally, the step of "determining a task recommendation decision that matches the third probability from multiple task recommendation decisions of the first task" may include: performing a level judgment on the third probability of the first task based on a preset probability level evaluation rule, and determining the second probability level to which the third probability belongs; and determining a second strategy that matches the second probability level from multiple second strategies of the first task.

[0097] Optionally, at least two second probability levels can be set in the probability level evaluation rules, such as a high level and a low level, and a probability threshold or probability range corresponding to each level can be set. When the third probability hits the probability range of a second probability level, a second strategy matching the second probability level is determined.

[0098] For example, if the third probability is in the range of 0.1-0.5, it is a low risk level. The second strategy may include: R&D can conduct self-testing to ensure it. If the third probability is in the range of 0.5-1, it is a high risk level. The second strategy may include: QA can intervene in the testing process.

[0099] See also Figure 4 After obtaining the second probability level to which the third probability belongs, a decision can be made to draw a risk conclusion and push it. The specific decision can include determining a second strategy that matches the second probability level based on the second probability level.

[0100] For example, the third probability calculated in the above steps is 0.51, indicating high risk. The second strategy involves QA intervention in the testing process. The first probability of the requirement characteristic is 0.8, indicating high risk. The first strategy involves recommending relevant product experts to conduct requirements control. The first probability of the R&D experience characteristic is 0.7, indicating high risk. The first strategy involves recommending attention to design and code reviews, and cross-testing and verification by the R&D team. The remaining risks are low and can be carried out normally.

[0101] In summary, the embodiment of the present application uses a decision tree model to determine the first strategy based on the first probability of each first feature data, and recommend the first strategy to the user, so that the analysis and decision-making process is supported by data and does not rely too much on human experience, making the decision more efficient and reliable. Moreover, from the global perspective of the first task, the third probability is calculated for the first task, and the second strategy for the first task is determined. Therefore, by combining the first strategy and the second strategy, a more comprehensive, detailed, and targeted strategy recommendation can be made for the first task, which can enable users to take preventive measures in advance and improve the efficiency of risk management.

[0102] Optional, see Figure 2 After development is completed according to the requirements, testing is required. The embodiment of the present application provides an evaluation report that provides a reference for testing, so as to assist in testing decisions and determine the test scope and test resource investment plan.

[0103] Optionally, the method of this example may further include:

[0104] Acquiring second feature data of a second task;

[0105] Determining a first score corresponding to each second feature data based on a scoring rule corresponding to each second feature data;

[0106] determining a second score for the second task based on the first score corresponding to each second feature data;

[0107] A task evaluation report for the second task is generated based on the second score.

[0108] Optionally, the second task may include a code change task. For example, see Figure 2If the user submits a code change task to create a second task, feature data can be extracted based on the change risk factor. The feature data is used to describe the change risk factor in the change analysis. Change risk factors include change complexity, change size, change impact, code quality, and unit test coverage. Furthermore, you can set scoring rules and weights for each feature data, perform weighted scoring on each feature data, and calculate the change impact score comprehensively. The change risk assessment report is pushed as a task assessment report, and low-risk use case assurance can be automatically executed. When the risk is high, the code change method involved in this requirement and the method affected by this code change are pushed. Combined with the relationship between the method and the system function, the test scope recommendation is given. In addition, please refer to Figure 6 , the code change task (i.e., the second task) will trigger the change analysis, which in turn triggers scoring according to the business rules (i.e., the above-mentioned scoring rules), and finally obtains the change risk report (i.e., the above-mentioned task evaluation report).

[0109] The second feature data of this example can be obtained from the platform associated with the second task. Optionally, the platform associated with the second task can be the same as or different from the platform associated with the first task, which is not limited in this example.

[0110] Optionally, the platform associated with the second task includes but is not limited to at least one of a demand management system, a CICD system, a service desk, and a testing platform.

[0111] Similar to the acquisition scheme of the first feature data, second task data related to the second task can be obtained from these platforms, and second feature data can be obtained based on the second task data. The optional second task data may include: the number of changed code lines, the number of files / modules, the complexity of changed code functions, the code static scanning score, the number of changed interfaces, the number of affected call chains, the overall complexity of the link, the code unit test coverage, and the historical defect rate, etc.

[0112] In the present application, the second characteristic data of the second task (ie, the code change risk characteristic) can be set, and business rules can be set to evaluate the degree of change risk.

[0113] In an optional example, the second characteristic data may include: at least one of code cyclomatic complexity, number of changed code lines, number of method dependency links, unit test coverage, and historical defect rate.

[0114] In this example, for each type of second feature data, a reasonable threshold and scoring rule can be set to assess the degree of change risk.

[0115] For example, code cyclomatic complexity is used to measure the complexity of code control flow. A threshold can be set, and scoring rules may include: code cyclomatic complexity <= 3, indicating that the code is very easy to understand and maintain, with a score of 10 points; code cyclomatic complexity between 3-7, medium complexity, with a score of 8 points; code cyclomatic complexity between 7-15, high complexity, with a score of 6 points; code cyclomatic complexity exceeding 15, extreme complexity, with a score of 4 points.

[0116] Scoring rules for changing the number of lines of code can include: a minor change (<20 lines of code), a score of 10 points; a medium change (between 20 and 100 lines of code), a major change (between 100 and 500 lines of code), a significant change (6 points); and a significant change (over 500 lines of code), a significant change (4 points). The line of code threshold can be set within a reasonable range based on language characteristics, with points deducted if the threshold is exceeded.

[0117] The scoring rules for the number of method dependency links may include: the number of method dependency links <= 3, low dependency, easy to understand and test, score 10 points; the number of method dependency links between 3-5, moderate dependency, score 8 points; the number of method dependency links between 5-10, high dependency, score 6 points; the number of method dependency links exceeds 10, high dependency, score 4 points.

[0118] Unit test coverage, scoring rules may include: unit test coverage greater than 90%, excellent coverage, score 10 points, unit test coverage between 70%-90%, high coverage, score 8 points, unit test coverage between 50%-70%, medium coverage, score 6 points, unit test coverage below 50%, low coverage, score 4 points.

[0119] Scoring rules for historical defect rates may include: the higher the historical defect rate, the greater the quality risk and the lower the score; a defect rate below 0.5 defects / kloc (the number of defects per thousand lines of code) is a low defect rate with a score of 10 points; a defect rate between 0.5 and 2 defects / kloc is a medium defect rate with a score of 8 points; a defect rate between 2 and 5 defects / kloc is a high defect rate with a score of 6 points; a defect rate exceeding 5 defects / kloc is an extremely high defect rate with a score of 4 points.

[0120] Therefore, when it is detected that a user submits a code change task, the above solution can be triggered to extract the second feature data of the second task and perform a first score on the second feature data according to the above rules.

[0121] In the present application, when determining the second score of the second task based on the first scores corresponding to the respective second feature data, the first scores may be directly added to obtain the second score.

[0122] Alternatively, the first scores may be weighted and summed according to the second feature weights of the respective second feature data to obtain the second score.

[0123] In another example, the second feature data can be divided into multiple types of second feature data according to characteristics, and a third score can be calculated for each type of second feature data based on the first score, and then the third score of each type of second feature data is integrated to obtain the second score.

[0124] Optionally, the second characteristic data includes characteristic data associated with the code changed in the second task, and characteristic data associated with a link on which the code depends.

[0125] In one embodiment, the second task is a code change task, and the second feature data includes feature data associated with the changed code in the code change task and feature data associated with a link on which the code depends. The step of “determining the second score of the second task based on the first score corresponding to each second feature data” may include:

[0126] determining a fourth score for the second task based on the first score of the feature data in the second feature data that is associated with the changed code in the code change task;

[0127] determining a fifth score of the second task based on the first score of the feature data associated with the code-dependent link in the second feature data;

[0128] Based on the fourth score and the fifth score, a second score for the second task is determined.

[0129] Optionally, the characteristic data associated with the code changed in the second task includes at least one of code cyclomatic complexity, number of changed code lines, unit test coverage, and historical defect rate. The characteristic data associated with code dependency links includes at least the number of method dependency links.

[0130] Optionally, a fourth score may be obtained by performing a weighted sum based on the first scores of the feature data associated with the code changed in the second task and the weights corresponding to the feature data.

[0131] Similarly, a weighted sum can be performed based on the first score of the feature data associated with the code-dependent link and the weight of the feature data to obtain a fifth score. The fourth and fifth scores can be directly summed to obtain the second score, or a weighted sum can be performed to obtain the fifth score. The weights required for the weighted summation in this example can be set according to actual needs.

[0132] In the present application, for the second scoring, at least two scoring levels and a scoring threshold can be set, and different scoring levels can generate different task evaluation reports.

[0133] Optional. There is no limit to the number of rating levels, it can be 2, 3, 4, etc.

[0134] For example, there are two scoring levels: a high scoring level and a medium-high scoring level. The scoring threshold is 7. A second score less than 7 is a medium-high scoring level, indicating that the second task has a medium-high risk. A score not less than 7 is a low scoring level, indicating that the second task has a low risk.

[0135] See also Figure 6 When a project undergoes pipeline testing, a change risk report (i.e., task assessment report) can be pushed to assist in testing decisions and determine test scope and resource investment. Pipeline testing can automatically execute use cases affected by the change based on the relationships between branches and use cases, and between interfaces and use cases. If the execution pass rate exceeds 90%, the project enters testing, with QA intervening in the testing.

[0136] If the test is completed, the changed function coverage, affected link coverage, interface coverage, and automated test pass rate data can be calculated from the precise testing platform, and combined with the change risk report to promote the test to produce a risk report.

[0137] The embodiment of the present application can also customize the configuration of quality approval rules. If the test results meet the approval rules, they will be automatically approved and can be published; otherwise, QA will conduct manual review.

[0138] In summary, the embodiment of the present application analyzes the scope of the change impact of the second task, calculates a score based on the second characteristic data and preset business rules, and thus evaluates the change risk of the task, which can effectively improve the efficiency and accuracy of the test, help the team arrange resources more reasonably, and reduce the risk of missed tests; and through automatic access testing, it simplifies the test submission process and improves efficiency, and through automatic test exit, it reduces manual verification time, speeds up the test to deployment process, and reduces waiting time. It can also combine strict testing standards with quality access control to improve test quality assurance.

[0139] See also Figure 2 , Figure 2 Taking the R&D project as an example, the quality assurance schemes for different tasks in the entire R&D process are demonstrated. Figure 2 As shown, the quality control plan for the R&D process is as follows:

[0140] 1. Determine whether to arrange QA (Quality Assurance) resources

[0141] a) Users can create tasks through the task management system, such as proposing requirements. After receiving the required tasks proposed by the users, the task management system can automatically predict the quality risks of the first task based on the key risk factors of the required tasks (i.e., first feature data, such as demand characteristics, personnel characteristics, service profiles, historical characteristics, quality infrastructure characteristics, etc.).

[0142] b) Classify the product based on the predicted quality risk. If the risk is low, R&D will conduct self-testing; if the risk is high, quality assurance personnel will intervene in the test.

[0143] In one specific embodiment, a user-provided requirement task may be a first task. First feature data for the first task is processed based on a preset logical function to obtain a first probability for the first feature data. A second probability for each first feature data item is then calculated based on the feature weight corresponding to each first feature data item and the first probability. The second probabilities for each first feature data item are summed to obtain a third probability for the first task. The third probability for the first task can then be graded based on a preset probability grade assessment rule to determine the second probability grade to which the third probability belongs. A second strategy matching the second probability grade is determined from multiple second strategies for the first task. Specifically, the probability grade assessment rule can include at least two second probability grades, such as high and low, with corresponding probability thresholds or ranges for each grade. When the third probability falls within a probability range for a second probability grade, a second strategy matching the second probability grade is determined. For example, if the third probability falls within the range of 0.1-0.5, indicating a low risk grade, the second strategy may include self-testing by R&D. If the third probability falls within the range of 0.5-1, indicating a high risk grade, the second strategy may include intervention by QA in the testing process.

[0144] 2. During the development phase, determine whether the test standards are met

[0145] After the code written for the required task is submitted for testing, the test quality can be checked based on the test quality access control. If it passes, the code will be automatically admitted for testing.

[0146] 3. During the testing phase, determine the test scope

[0147] a) If a code change task is submitted, feature data is extracted based on the change risk factors. This feature data is used to describe the change risk factors in the change analysis. Change risk factors include change complexity, change size, change impact, code quality, and unit test coverage.

[0148] b) Scoring rules and weights are set for each feature data point, and weighted scores are calculated for each feature data point. The change impact score is then delivered, and a change risk assessment report is released. For low-risk cases, use case assurance can be automatically executed. For high-risk cases, the code change methods involved in this requirement are delivered, as well as the methods affected by this code change. Based on the relationship between the methods and system functions, test scope recommendations are provided.

[0149] In a specific implementation, the code change task can be the second task. After obtaining the second feature data of the second task, the first score corresponding to the second feature data can be determined based on the scoring rules corresponding to each second feature data; then, based on the first score of the feature data associated with the code-dependent link in the second feature data, the fifth score of the second task is determined; based on the fourth score and the fifth score, the second score of the second task is determined. In this application, at least two scoring levels and a scoring threshold can be set for the second score. The scoring levels include two, a high score level and a medium-high score level. The scoring threshold is 7. The second score is less than 7 for a medium-high score level, indicating that the second task has a medium-high risk. The score is not less than 7 for a low score level, indicating that the second task has a low risk. Among them, low risk can automatically execute use case assurance; when the risk is high, the code change method involved in this requirement and the method affected by this code change are pushed, and the test scope recommendation is given based on the relationship between the method and the system function.

[0150] 4. After the testing phase, determine whether the quality meets the release standards

[0151] Comprehensive test coverage, change coverage, link coverage, interface coverage, and automated test pass rate data can be extracted from the precision testing platform. Quality approval rules are used for quality checkpoints. If a quality checkpoint passes, the quality risk is considered low and the product is automatically approved for release. Otherwise, QA will conduct manual review.

[0152] Among them, different projects can customize the quality approval rules.

[0153] It should be understood that, although each step in the flowcharts involved in the above embodiments is shown in sequence as indicated by the arrows, these steps are not necessarily performed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be performed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the above embodiments may include multiple steps or multiple stages, and these steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0154] Based on the same inventive concept, the embodiments of the present application further provide a data processing device for implementing the aforementioned data processing method, and a data processing device for implementing the aforementioned data processing method. The implementation solution provided by the device is similar to the implementation solution described in the aforementioned method, so the specific limitations of the one or more data processing devices and embodiments of the data processing device provided below can be found in the above-mentioned limitations on the data processing method and data processing method, and the specific limitations are not repeated here.

[0155] This embodiment also provides a data processing device, which can be integrated into a terminal device. Figure 7 As shown, the data processing device may include:

[0156] A processing unit 701 is configured to process first feature data of a first task based on a preset logic function to obtain a first probability of the first feature data;

[0157] The determination unit 702 is used to determine the first decision node that matches the first probability of each first feature data based on the first probability of the first feature data, at least one decision node configured for each first feature data, and the connection relationship between the decision nodes; and determine the first strategy corresponding to the first feature data based on the first decision node.

[0158] In an optional example, there are multiple types of first feature data, and the determination unit is used to determine the first decision node hit by the first probability among the multiple decision nodes connected to the first decision node based on the first probability of the first feature data in the first decision node of the decision tree; obtain the first strategy corresponding to the first feature data from the first decision node, wherein the first decision node is configured with the first feature data belonging to it; based on the first probability of the first feature data belonging to the first decision node, determine a new first decision node hit by the first probability from the multiple decision nodes connected to the first decision node; return to execute the step of obtaining the first strategy corresponding to the first feature data from the first decision node, and based on the first probability of the first feature data belonging to the first decision node, determine a new first decision node hit by the first probability from the multiple decision nodes connected to the first decision node, until the decision tree is executed; wherein the type of the first feature data in each first decision node is different.

[0159] In some embodiments, a determination unit is used to determine a first probability of first feature data belonging to a first decision node, and a first probability level hit among a preset plurality of probability levels; and determine, among a plurality of decision nodes connected to the first decision node, a decision node corresponding to the first probability level as a new first decision node.

[0160] In some embodiments, the data processing apparatus further comprises:

[0161] The second determination unit is used to calculate the second probability of each first feature data based on the feature weight corresponding to each first feature data and the first probability; sum the second probabilities of each first feature data to obtain the third probability of the first task; and determine the second strategy that matches the third probability from multiple second strategies of the first task.

[0162] In some embodiments, the second determination unit is used to perform a level judgment on the third probability of the first task based on a preset probability level evaluation rule, and determine the second probability level to which the third probability belongs; and determine a second strategy that matches the second probability level from multiple second strategies of the first task.

[0163] In some embodiments, the data processing apparatus further comprises:

[0164] The second task processing unit is used to obtain second feature data of the second task; determine the first score corresponding to the second feature data based on the scoring rules corresponding to each second feature data; determine the second score of the second task based on the first score corresponding to each second feature data; and generate a task evaluation report for the second task based on the second score.

[0165] In some embodiments, the second task is a code change task, and the second feature data includes feature data associated with the changed code in the code change task, and feature data associated with the code-dependent link. The second task processing unit is used to determine the fourth score of the second task based on the first score of the feature data associated with the changed code in the code change task in the second feature data; determine the fifth score of the second task based on the first score of the feature data associated with the code-dependent link in the second feature data; and determine the second score of the second task based on the fourth score and the fifth score.

[0166] The device of this embodiment can process the first feature data of the first task based on a preset logical function to obtain the first probability of the first feature data; determine the first decision node that matches the first probability of each first feature data according to the first probability of the first feature data, at least one decision node configured for each first feature data, and the connection relationship between the decision nodes, and determine the first strategy corresponding to the first feature data based on the first decision node; wherein the first strategy is used to adjust the first probability generated by the first feature data after being implemented, thereby, different first tasks can determine the strategy adapted thereto through a plurality of reasonably set decision nodes and a fixed connection relationship between the decision nodes. By pre-setting a plurality of decision nodes and a fixed connection relationship between the decision nodes, a decision process with a unified decision logic is provided when different tasks determine strategies. Compared with the artificial subjective strategy for the first task based on personal experience, the decision process of the present application can ensure the decision stability and reliability of the first task.

[0167] The specific implementation of the above operations can be found in the previous embodiments and will not be repeated here.

[0168] In one embodiment, the computer device is a terminal device, for example, its internal structure diagram can be as follows Figure 8 As shown. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit and an input device. The processor, the memory and the input / output interface are connected via a system bus, and the communication interface, the display unit and the input device are connected to the system bus via the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a data processing method is implemented. The display unit of the computer device is used to form a visually visible image, and can be a display screen, a projection device or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device casing, or an external keyboard, touchpad or mouse, etc.

[0169] Those skilled in the art will understand that Figure 8The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0170] Based on the same inventive concept, an embodiment of the present application also provides a computer-readable storage medium, which may include: a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk, etc.

[0171] Since the computer program stored in the computer-readable storage medium can execute any data processing method provided in the embodiments of the present application, the beneficial effects that can be achieved by any data processing method provided in the embodiments of the present application can be achieved. Please refer to the previous embodiments for details and will not be repeated here.

[0172] Based on the same inventive concept, embodiments of the present application further provide a computer program product or computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the methods provided in various optional implementations of the above embodiments.

[0173] It should be noted that the data involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with the relevant laws, regulations and standards of the relevant countries and regions. Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, which can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods.

[0174] Any reference to the memory, database or other media used in the various embodiments provided herein may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory may include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0175] The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processors involved in the various embodiments provided herein may include, but are not limited to, general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic units, data processing logic units based on quantum computing, and the like.

[0176] In the above-mentioned data processing apparatus, computer-readable storage medium, computer device, and computer program product embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a particular embodiment, reference can be made to the relevant descriptions of other embodiments. Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes and beneficial effects of the above-described data processing apparatus, computer-readable storage medium, computer program product, computer device, and their corresponding units can be referred to as described in the data processing method in the above embodiments, and the details will not be repeated here.

[0177] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0178] The above is a detailed introduction to a data processing method, computer device, computer-readable storage medium and computer program product provided in the embodiments of the present application. Specific examples are used herein to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method of the present application and its core ideas. At the same time, for those skilled in the art, based on the ideas of the present application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A data processing method, characterized in that: include: Processing the first feature data of the first task based on a preset logic function to obtain a first probability of the first feature data; Determining, based on the first probability of the first feature data, at least one decision node configured for each of the first feature data, and a connection relationship between the decision nodes, a first decision node that matches the first probability of each of the first feature data; A first strategy corresponding to the first feature data is determined based on the first decision node.

2. The method according to claim 1, characterized in that There are multiple types of the first feature data, and determining a first decision node that matches the first probability of each first feature data according to the first probability of the first feature data, at least one decision node configured for each first feature data, and a connection relationship between the decision nodes; Determining a first strategy corresponding to the first feature data based on the first decision node includes: Based on a first probability of first feature data in a first decision node of the decision tree, determining a first decision node hit by the first probability among multiple decision nodes connected to the first decision node; Obtaining a first policy corresponding to the first feature data from the first decision node, wherein the first decision node is configured with the corresponding first feature data; Based on a first probability of first feature data belonging to the first decision node, determining a new first decision node hit by the first probability from a plurality of decision nodes connected to the first decision node; Returning to the step of obtaining the first strategy corresponding to the first feature data from the first decision node, and determining, based on the first probability of the first feature data belonging to the first decision node, a new first decision node hit by the first probability from a plurality of decision nodes connected to the first decision node, until the decision tree is executed; The type of the first feature data in each of the first decision nodes is different.

3. The method according to claim 2, characterized in that The determining, from among the plurality of decision nodes connected to the first decision node, a new first decision node hit by the first probability, includes: Determining a first probability of first feature data belonging to the first decision node, a first probability level of hitting among a plurality of preset probability levels; Among the multiple decision nodes connected to the first decision node, a decision node corresponding to the first probability level is determined as a new first decision node.

4. The method according to any one of claims 1 to 3, characterized in that The method further comprises: Calculating a second probability for each of the first feature data based on a feature weight corresponding to each of the first feature data and the first probability; Summing the second probabilities of the first feature data to obtain a third probability of the first task; A second strategy matching the third probability is determined from a plurality of second strategies for the first task.

5. The method according to claim 4, characterized in that Determining a second strategy that matches the third probability from a plurality of second strategies for the first task includes: Performing a level judgment on the third probability of the first task based on a preset probability level evaluation rule to determine the second probability level to which the third probability belongs; A second strategy matching the second probability level is determined from a plurality of second strategies for the first task.

6. The method according to any one of claims 1 to 3, characterized in that The method further comprises: Acquiring second feature data of a second task; Determining a first score corresponding to each second feature data based on a scoring rule corresponding to each second feature data; determining a second score for the second task based on the first score corresponding to each of the second feature data; Based on the second score, a task evaluation report for the second task is generated.

7. The method according to claim 6, characterized in that The second task is a code change task, the second feature data includes feature data associated with the changed code in the code change task, and feature data associated with a link on which the code depends, and determining the second score of the second task based on the first score corresponding to each second feature data includes: determining a fourth score of the second task based on the first score of the feature data in the second feature data that is associated with the changed code in the code change task; determining a fifth score of the second task based on a first score of feature data associated with the code-dependent link in the second feature data; A second score for the second task is determined based on the fourth score and the fifth score.

8. A computer device, characterized in that: The method comprises a processor and a memory, wherein the memory stores a plurality of instructions; the processor loads instructions from the memory to execute the steps of the data processing method according to any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a plurality of instructions, and the instructions are suitable for being loaded by a processor to execute the steps of the data processing method according to any one of claims 1 to 7.

10. A computer program product, characterized in that The computer program product includes a computer program, and the computer program is used by a processor to execute the steps of the data processing method according to any one of claims 1 to 7.

Images