Method and system for allocating process approval authority based on time granularity

Abstract

The present invention discloses a method and system for allocating process approval authority based on time granules, and relates to the field of data processing technology. The method comprises: reading pre-test software, performing hierarchical time granule division based on test process, determining hierarchical test nodes, and setting allocation constraint functions; performing module learning based on hierarchical test nodes and allocation constraint functions, determining temporary allocation modules, and performing test node approval allocation and collision hierarchical iterative optimization allocation in combination with tester status, determining target allocation results, and performing allocation visualization and software testing management. The present invention solves the technical problems of unreasonable allocation of process approval authority in the software testing process in the prior art, resulting in redundant execution of testers, low test efficiency and low test result accuracy, and achieves the technical effect of allocating test authority based on time granules, avoiding redundant execution of testers, and improving test efficiency and test result accuracy.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a method and system for allocating process approval authority based on time granules. Background Art

[0002] With the continuous development of software development, the testing process is gradually becoming standardized and regularized. This requires that each link in the testing process has a clear approval process and authority allocation. The allocation of approval authority is a key link to ensure that the testing work is carried out in accordance with established standards and specifications.

[0003] However, due to the increasing complexity of software development, the traditional software testing process approval authority allocation method cannot meet the allocation requirements, resulting in testers performing redundant return adjustments, resulting in insufficient accuracy of test results. Summary of the invention

[0004] The present application provides a method and system for allocating process approval authority based on time granules, which is used to solve the technical problems in the prior art of unreasonable allocation of process approval authority during software testing, resulting in redundant execution of testers, low testing efficiency and low accuracy of test results.

[0005] In a first aspect of the present application, a method for allocating process approval authority based on time granules is provided, the method comprising: reading pre-test software, performing hierarchical time granule division based on a test process, determining hierarchical test nodes, the pre-test software comprising at least one concurrent test software, the hierarchical test node being identified with a test feature; determining a rigid process node and a flexible process node based on the hierarchical test node, and setting an allocation constraint function, wherein the flexible process node is identified with a slack; training an adaptive approval module, performing module learning based on the hierarchical test node and the allocation constraint function, and determining a temporary allocation module, wherein the adaptive approval module comprises a plurality of approval units, including an independent process allocation block and a collaborative collision allocation block; an interactive tester status, the tester status being a time zone status based on time granule division, including a rigid state time zone and a flexible state time zone; for the tester status, performing test node approval allocation for software testing and collision hierarchical iterative optimization allocation based on finite element analysis in combination with the temporary allocation module, and determining a target allocation result; transmitting the target allocation result to an intelligent scheduling center, and performing allocation visualization and software testing management on a software testing end and a user end.

[0006] In a second aspect of the present application, a process approval authority allocation system based on time granules is provided, the system comprising: a hierarchical time granule division module, the hierarchical time granule division module is used to read pre-test software, perform hierarchical time granule division based on the test process, and determine hierarchical test nodes, the pre-test software includes at least one concurrent test software, and the hierarchical test node is identified with a test feature; an allocation constraint function setting module, the allocation constraint function setting module is used to determine a rigid process node and a flexible process node based on the hierarchical test node, and set an allocation constraint function, wherein the flexible process node is identified with a slack; a temporary allocation learning module, the temporary allocation learning module is used to train an adaptive approval module, perform module learning based on the hierarchical test node and the allocation constraint function, and determine a temporary allocation. A matching module, wherein the adaptive approval module includes multiple approval units, including independent process allocation blocks and collaborative collision allocation blocks; a tester status interaction module, wherein the tester status interaction module is used to interact with the tester status, and the tester status is a time zone status based on time granularity, including a rigid state time zone and a flexible state time zone; a target allocation result determination module, wherein the target allocation result determination module is used to determine the target allocation result for the tester status, in combination with the temporary allocation module, to perform test node approval allocation and collision hierarchy iterative optimization allocation based on finite element analysis for software testing; a software testing management module, wherein the software testing management module is used to transmit the target allocation result to the intelligent scheduling center, and perform allocation visualization and software testing management on the software testing end and the user end.

[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0008] The method for allocating process approval authority based on time granules provided by the present application relates to the field of data processing technology. The method reads pre-test software, performs hierarchical time granular division based on the test process, determines hierarchical test nodes, sets allocation constraint functions, performs module learning based on hierarchical test nodes and allocation constraint functions, determines temporary allocation modules, performs test node approval allocation and collision hierarchical iterative optimization and allocation in combination with the tester status, and performs allocation visualization and software testing management according to the target allocation results. The method solves the technical problems of unreasonable allocation of process approval authority in the software testing process in the prior art, resulting in redundant execution of testers, low test efficiency and low accuracy of test results, and realizes the technical effect of allocating test authority based on time granules, avoiding redundant execution of testers, and improving test efficiency and accuracy of test results. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0010] Figure 1 A schematic diagram of a process flow diagram of a method for allocating process approval authority based on time granularity provided in an embodiment of the present application;

[0011] Figure 2 A schematic diagram of a process for determining a target allocation result in a method for allocating process approval authority based on time granules provided in an embodiment of the present application;

[0012] Figure 3 A schematic diagram of the structure of a process approval authority allocation system based on time granularity provided in an embodiment of the present application.

[0013] Explanation of the accompanying drawings: hierarchical time granular division module 11, allocation constraint function setting module 12, temporary allocation learning module 13, tester status interaction module 14, target allocation result determination module 15, software test management module 16. DETAILED DESCRIPTION

[0014] The present application provides a method for allocating process approval authority based on time granules, which is used to solve the technical problems in the prior art of unreasonable allocation of process approval authority during software testing, resulting in redundant execution of testers, low test efficiency and low accuracy of test results.

[0015] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only 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 ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0016] It should be noted that the terms "first", "second", etc. in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or devices.

[0017] Embodiment 1, as Figure 1 As shown, the present application provides a method for allocating process approval authority based on time granularity, the method comprising:

[0018] P10: Read the pre-test software, perform hierarchical time granular division based on the test process, and determine the hierarchical test nodes, wherein the pre-test software includes at least one concurrent test software, and the hierarchical test nodes are marked with test features;

[0019] Furthermore, the hierarchical time granular division based on the test process, step P10 of the embodiment of the present application further includes:

[0020] P11: Use the process stage as the main division layer and the test class as the subordinate sub-division layer to determine the granularity division standard;

[0021] P12: Based on the granularity division standard and according to the test cases, the pre-test software is hierarchically divided to determine the hierarchical test nodes, wherein each pre-test software corresponds to a group of hierarchical test nodes.

[0022] Optionally, the pre-test software is read, and the pre-test software is divided into different hierarchical time granules according to the test process, and hierarchical test nodes of the hierarchical time granules are determined. The pre-test software includes at least one concurrent test software, and the hierarchical test nodes are marked with test features.

[0023] Specifically, the main stages of the test process are first used as the main division layer. For example, a complete software testing process may include unit testing, integration testing, system testing, acceptance testing and other stages. Each stage has its specific goals and tasks, so it can be used as a main layer. Furthermore, within each process stage, different test types are used as subordinate sub-division layers. For example, in the system testing stage, there are multiple types of tests including functional testing, performance testing, and security testing. These test classes can be used as subordinate sub-division layers. The granularity division standard is determined by the main division layer and the sub-division layer.

[0024] Further, referring to the granularity division standard, the pre-test software is hierarchically divided according to the actual test cases, including a detailed analysis of the test cases to determine the test phase and test class to which the test cases belong. After the hierarchical division is completed, the key nodes in each main level and sub-level are used as hierarchical test nodes. And each pre-test software corresponds to a group of hierarchical test nodes.

[0025] P20: Based on the hierarchical test nodes, determine the rigid process nodes and the flexible process nodes, and set the allocation constraint function, wherein the flexible process nodes are marked with slack;

[0026] Specifically, based on the hierarchical test nodes, rigid process nodes and flexible process nodes are further distinguished, and allocation constraint functions are set for these nodes. Among them, the rigid process nodes refer to nodes that must be executed in a specific order, time or condition in the test process. These nodes usually have strict dependencies and timing requirements and cannot be changed or adjusted at will. And the rigid process nodes can be determined based on the critical path of the test process, important test tasks or test steps with strict timing requirements.

[0027] The flexible process node refers to a node in the test process that can flexibly adjust the execution order, time or resources within a certain range. The flexible process node is marked with slack and can be flexibly adjusted according to actual conditions. And the flexible process node can be determined based on non-critical paths in the test process, test tasks that can be executed in parallel, or test steps with alternative resources.

[0028] Furthermore, an allocation constraint function is set, and the allocation constraint function can be set according to factors such as the characteristics of the test process, resource constraints, and test objectives. In addition, the allocation constraint function can include multiple types of constraints, such as time constraints, resource constraints, quality constraints, etc., which can be used to guide test task allocation and scheduling.

[0029] P30: training an adaptive approval module, performing module learning based on the hierarchical test nodes and the allocation constraint function, and determining a temporary allocation module, wherein the adaptive approval module includes a plurality of approval units, including independent process allocation blocks and collaborative collision allocation blocks;

[0030] Furthermore, step P30 of the embodiment of the present application further includes:

[0031] P31: The first avoidance principle is determined based on the relaxation, and the second avoidance principle is determined based on the node association influence;

[0032] P32: Based on the first avoidance principle and the second avoidance principle, average weighting is performed to determine the avoidance priority, and the avoidance allocation principle is set;

[0033] P33: Based on the avoidance allocation principle, supervised learning is performed on the collision allocation block.

[0034] It should be understood that the adaptive approval module is trained, and the adaptive approval module is composed of multiple approval units, which include independent process allocation blocks and collaborative collision allocation blocks. And the adaptive approval module can learn and optimize according to the hierarchical test nodes and allocation constraint functions to determine one or more temporary allocation modules. The temporary allocation module is calculated based on the current data and conditions, and may change with the changes in the system environment. It can be used to test, verify or simulate the approval process to ensure that its performance and effect in the real environment meet expectations.

[0035] Among them, the construction process of the collision allocation block can be: determining the first avoidance principle based on the slack. When a collision occurs, for example, multiple tasks request the same resource or the same time period at the same time, the avoidance priority can be determined according to the slack of the flexible process node. Nodes with larger slack are more likely to be adjusted or postponed to avoid collisions. The second avoidance principle is determined based on the node association influence. The node association influence refers to the degree of influence of the execution of a node on other nodes or the entire test process. For example, the failure of certain key nodes may lead to the failure of the entire test process or the waste of a large amount of resources. Therefore, according to the second avoidance principle, the node association influence is considered to ensure that important nodes or processes are not affected.

[0036] Furthermore, based on the first avoidance principle and the second avoidance principle, the mean weights are assigned to them to determine the avoidance priority of the first avoidance principle and the second avoidance principle, and the avoidance coordination principle can be set according to the actual situation and needs. Based on the avoidance coordination principle, supervised learning is performed on the collision coordination block, which is an important part of the adaptive approval module and is responsible for handling collision problems in the test process. The collision coordination block can be trained by obtaining historical data or simulation data of various collision scenarios and corresponding solutions as training data, and optimization and adjustment are performed based on the avoidance coordination principle as a supervisory constraint to obtain the collision coordination block.

[0037] P40: interactive tester status, which is a time zone status based on time granularity, including a rigid state time zone and a flexible state time zone;

[0038] Optionally, the current status information of the tester is interactively obtained, where the tester status is a time zone status based on time granules, including a rigid state time zone and a flexible state time zone. The rigid state time zone refers to a state in which the tester is in an unchangeable or adjustable state within certain time granules. For example, the tester may have made an appointment for a meeting or training in a certain time period, so they cannot participate in the testing task during this time period. The rigid state time zone is fixed and usually has strict restrictions. When assigning testing tasks, the system needs to avoid assigning these tasks to testers in the rigid state time zone.

[0039] The flexible state time zone means that the tester is in a relatively flexible state within certain time granules, which can be adjusted or changed as needed. For example, the tester may not have a specific schedule in a certain time period, so he can participate in the test task. The flexible state time zone allows a certain degree of flexibility and can be adjusted according to actual conditions. When assigning test tasks, the system can give priority to assigning tasks to testers in the flexible state time zone to make full use of their free time.

[0040] Through the interactive tester status, real-time and accurate tester status information can be obtained, and more reasonable test task allocation and scheduling strategies can be formulated based on this information to improve test efficiency, reduce resource waste, and ensure the smooth progress of test tasks.

[0041] P50: Based on the tester status, the temporary allocation module is combined to perform the test node approval allocation of the software test and the collision hierarchy iterative optimization allocation based on finite element analysis to determine the target allocation result;

[0042] Further, such as Figure 2 As shown, step P50 of the embodiment of the present application also includes:

[0043] P51: Based on the N approval units mapped by each pre-test software in the temporary allocation module, parallel independent processing is performed based on the process allocation block to determine a first allocation result;

[0044] P52: transferring the first allocation result to the collision allocation block, and performing multi-objective optimization allocation based on the flexible allocation part with the avoidance allocation principle as a constraint, and screening and determining a flexible allocation scheme;

[0045] P53: Integrate the first allocation result and the flexible allocation plan to determine the target allocation result.

[0046] Specifically, according to the tester status, the test nodes of the software test are approved and allocated through the temporary allocation module, and the collision hierarchy iterative optimization method of finite element analysis is used to allocate the test tasks, and finally the target allocation result is determined.

[0047] Exemplarily, based on the N approval units mapped for each pre-test software in the temporary allocation module, the process allocation block is used for parallel independent processing, which means that each approval unit will independently make a preliminary allocation of test nodes according to the pre-test software it is responsible for and the allocation constraint function. Through parallel processing, a preliminary first allocation result is quickly generated.

[0048] Furthermore, the first allocation result is transferred to the collision allocation block to handle resource conflicts or collisions that may arise from parallel independent processing. Through the collision allocation block, the flexible allocation part is multi-objective optimized and allocated with the avoidance allocation principle as a constraint. That is, under the premise of following the avoidance principle, the entire test process is optimized by adjusting the execution time and resource allocation of the flexible process nodes to achieve the optimization of multiple goals, such as efficiency, cost, quality, etc. After multi-objective optimization and allocation, the best flexible allocation scheme is screened out, which can optimize the entire test process to the greatest extent while satisfying the avoidance principle.

[0049] Finally, the first allocation result and the flexible deployment scheme are integrated to generate the final target allocation result. The target allocation result not only takes into account the independent allocation of each test node, but also optimizes the entire test process through collision deployment and flexible deployment, ensuring efficient and accurate execution of the test task.

[0050] Furthermore, step P52 of the embodiment of the present application also includes:

[0051] P52-1: Building an optimization space based on the flexible process nodes and the flexible state time zones;

[0052] P52-2: Perform a layer of iterative optimization based on the same time granules to determine a layer of optimization solutions, wherein the layer of optimization solutions includes the single time granule optimal solution and a set of preferred solutions;

[0053] P52-3: Based on the first-layer optimization scheme, a global second-layer iterative optimization is performed with the flexible part as a benchmark to determine the flexible allocation scheme.

[0054] It should be understood that in order to perform multi-objective optimization and deployment based on the flexible allocation part, first, an optimization space is built based on the flexible process nodes and the flexible state time zone, and the space includes all possible adjustment schemes of the flexible process nodes and the available time of the testers in the flexible state time zone. Further, based on the same time granule as the benchmark, a layer of iterative optimization is performed, that is, within the same time granule, all possible adjustment schemes of the flexible process nodes and the flexible state time zone of the testers are considered to find the optimal solution within the time granule as a layer of optimization scheme. The layer of optimization scheme includes a single time granule optimal scheme and a set of preferred schemes. Among them, the single time granule optimal scheme refers to the best allocation scheme obtained after considering all constraints within the time granule, and the set of preferred schemes includes all feasible schemes that meet the constraints within the time granule.

[0055] Furthermore, based on the first-level optimization scheme, a global second-level iterative optimization is performed with the flexible part as the benchmark. That is, the allocation of all time granules is considered globally to ensure the optimization of the entire test process. In the second-level iterative optimization process, the flexible process nodes and flexible state time zones within all time granules, as well as their correlation and mutual influence, are comprehensively considered, and a globally optimal flexible allocation scheme is determined through continuous adjustment and optimization.

[0056] Furthermore, step P52 of the embodiment of the present application also includes:

[0057] P52-1a: Determine pre-adjustment multiple targets, randomly determine a preset number of finite element adjustment targets, perform random perturbations under constraints in the optimization space, and determine a first optimization solution, wherein the preset number is less than or equal to the number of pre-adjustment multiple targets;

[0058] P52-2a: Perform iterative optimization based on the first optimization scheme until the preset number of iterations is met, and then select the global optimal solution;

[0059] P52-3a: Among them, the optimization rules are iteration in the optimal direction and retreat in the inferior direction, and the preset number of iterative variables is used as the finite element standard.

[0060] In a possible embodiment of the present application, the optimization iteration method of the above scheme can be refined as follows: first, determine the pre-adjusted multiple objectives, that is, multiple indicators related to test efficiency, resource utilization, test quality, etc., and randomly determine a preset number of finite element adjustment objectives as a benchmark for random perturbations in the optimization space, and the preset number is less than or equal to the number of the pre-adjusted multiple objectives. In the optimization space, perform constrained random perturbations according to the finite element adjustment objectives, that is, perform random minor adjustments to the flexible process nodes and flexible state time zones, and determine the first optimization scheme.

[0061] Furthermore, the first optimization scheme is used as a benchmark for iterative optimization. In each iteration, the current scheme is adjusted according to optimization rules, such as iteration in the optimal direction and fallback in the inferior direction. Iteration in the optimal direction means that when a better solution is found in a certain direction, the search continues in this direction, while fallback in the inferior direction means that when the solution found in a certain direction is worse than the current solution, fallback to the previous solution and try other directions. Iterate continuously until the preset number of iterations is met, and select the global optimal solution from all iteration results as the flexible deployment scheme.

[0062] P60: The target allocation result is transmitted to the intelligent dispatching center to perform allocation visualization and software testing management on the software testing end and the user end.

[0063] Specifically, the target allocation result is transmitted to the intelligent scheduling center. After receiving the target allocation result, the intelligent scheduling center uses visualization technology to intuitively display the allocation result to the software testing end and the user end. For example, through charts, graphics or other visual elements, the distribution of test tasks, the status of testers, test progress and other information can be clearly displayed. At the same time, the intelligent scheduling center comprehensively manages the software testing process, including the allocation, execution, monitoring and reporting of test tasks. In addition, a detailed test report can be generated to provide the test team with comprehensive data support and analysis basis to improve the transparency and efficiency of the test work, reduce the error rate, and provide strong guarantees for the quality and progress of software testing.

[0064] Furthermore, the embodiment of the present application further includes step P70, and step P70 further includes:

[0065] P71: Reading newly added processing tasks, wherein the newly added processing tasks include software testing tasks and personnel allocation tasks at the user end;

[0066] P72: Performing time particle collision analysis on the newly added processing task and the target allocation result to locate the collision time particles;

[0067] P73: Combined with the collision allocation block, based on the relaxation, the allocation decision under the collision time granularity is made to determine the updated allocation result;

[0068] P74: Perform deployment management on the updated allocation results based on the intelligent dispatching center.

[0069] Optionally, when a new software testing task or a user-side personnel allocation task appears, the newly added processing task is read, and the newly added processing task includes the software testing task and the user-side personnel allocation task. A time granule collision analysis is performed on the newly added processing task and the target allocation result to check whether the newly added task conflicts or overlaps with the existing task in time granules, and locate the time granules that collide with the newly added task, i.e., the collision time granules.

[0070] Furthermore, in combination with the collision allocation block, the allocation decision under the collision time granularity is made based on the relaxation, that is, the priority, execution time, resource allocation, etc. of the task are evaluated and adjusted according to the relaxation to ensure that all tasks can be effectively executed and the updated allocation results are obtained. Finally, the updated allocation results are allocated and managed based on the intelligent scheduling center, including transmitting the updated allocation results to the intelligent scheduling center for visual display and real-time monitoring to ensure that all tasks can be carried out smoothly according to the new allocation results.

[0071] In summary, the embodiments of the present application have at least the following technical effects:

[0072] This application reads the pre-test software, performs hierarchical time granularity division based on the test process, determines the hierarchical test nodes, and sets the allocation constraint function. It performs module learning based on the hierarchical test nodes and the allocation constraint function, determines the temporary allocation module, and performs test node approval allocation and collision hierarchical iterative optimization allocation in combination with the tester status, and performs allocation visualization and software test management based on the target allocation results.

[0073] The technical effect of allocating test permissions based on time granules, avoiding redundant execution by testers, and improving test efficiency and the accuracy of test results is achieved.

[0074] Embodiment 2 is based on the same inventive concept as the method for allocating process approval authority based on time granules in the above embodiment. Figure 3 As shown, the present application provides a process approval authority allocation system based on time granules, and the system and method embodiments in the present application embodiments are based on the same inventive concept. The system includes:

[0075] A hierarchical time granular division module 11, the hierarchical time granular division module 11 is used to read the pre-test software, perform hierarchical time granular division based on the test process, and determine hierarchical test nodes, the pre-test software includes at least one concurrent test software, and the hierarchical test nodes are marked with test features;

[0076] An allocation constraint function setting module 12, the allocation constraint function setting module 12 is used to determine the rigid process node and the flexible process node based on the hierarchical test node, and set the allocation constraint function, wherein the flexible process node is marked with a slack;

[0077] A temporary allocation learning module 13, the temporary allocation learning module 13 is used to train an adaptive approval module, perform module learning based on the hierarchical test nodes and the allocation constraint function, and determine a temporary allocation module, wherein the adaptive approval module includes a plurality of approval units, including independent process allocation blocks and collaborative collision allocation blocks;

[0078] A tester status interaction module 14, wherein the tester status interaction module 14 is used to interact with the tester status, wherein the tester status is a time zone status based on time granularity, including a rigid state time zone and a flexible state time zone;

[0079] A target allocation result determination module 15, which is used to determine the target allocation result by combining the temporary allocation module with the test node approval allocation of the software test and the collision hierarchy iterative optimization allocation based on finite element analysis according to the tester status;

[0080] The software testing management module 16 is used to transmit the target allocation result to the intelligent scheduling center to perform allocation visualization and software testing management between the software testing end and the user end.

[0081] Furthermore, the hierarchical time granularity division module 11 is further configured to perform the following steps:

[0082] Use process stages as the main division layer, test classes as the subordinate sub-division layers, and determine the granularity division standard;

[0083] Based on the granularity division standard and according to the test cases, the pre-test software is divided into levels to determine the level test nodes, wherein each pre-test software corresponds to a group of level test nodes.

[0084] Furthermore, the temporary allocation learning module 13 is further configured to perform the following steps:

[0085] The first avoidance principle is determined based on the relaxation, and the second avoidance principle is determined based on the node association influence;

[0086] Based on the first avoidance principle and the second avoidance principle, average weighting is performed to determine the avoidance priority, and the avoidance allocation principle is set;

[0087] Based on the avoidance allocation principle, supervised learning is performed on the collision allocation block.

[0088] Furthermore, the target allocation result determination module 15 is further configured to perform the following steps:

[0089] Based on the N approval units mapped by each pre-test software in the temporary allocation module, parallel independent processing is performed based on the process allocation block to determine a first allocation result;

[0090] The first allocation result is transferred to the collision allocation block, and a multi-objective optimization allocation based on the flexible allocation part is performed with the avoidance allocation principle as a constraint, so as to screen and determine a flexible allocation scheme;

[0091] The first allocation result and the flexible allocation plan are integrated to determine the target allocation result.

[0092] Furthermore, the target allocation result determination module 15 is further configured to perform the following steps:

[0093] Building an optimization space based on the flexible process nodes and the flexible state time zones;

[0094] Performing a layer of iterative optimization based on the same time granules to determine a layer of optimization solutions, wherein the layer of optimization solutions includes a single time granule optimal solution and a set of preferred solutions;

[0095] Based on the first-layer optimization scheme, a global second-layer iterative optimization is performed with the flexible part as a benchmark to determine the flexible allocation scheme.

[0096] Furthermore, the target allocation result determination module 15 is further configured to perform the following steps:

[0097] Determine pre-adjustment multiple targets, randomly determine a preset number of finite element adjustment targets, perform random perturbations under constraints in the optimization space, and determine a first optimization solution, wherein the preset number is less than or equal to the number of pre-adjustment multiple targets;

[0098] Iterate the optimization based on the first optimization scheme until the preset number of iterations is met and the global optimal solution is selected;

[0099] Among them, the optimization rule is to use the optimal iteration and the inferior retreat, and the preset number of iterative variables is used as the finite element standard.

[0100] Furthermore, the system also includes:

[0101] A newly added processing task reading module, which is used to read newly added processing tasks, including software testing tasks and personnel allocation tasks at the user end;

[0102] A collision time particle positioning module, the collision time particle positioning module is used to perform a time particle collision analysis on the newly added processing task and the target allocation result, and locate the collision time particle;

[0103] An update allocation result determination module, the update allocation result determination module is used to combine the collision allocation block, take the relaxation as a reference, make an allocation decision under the collision time granularity, and determine the update allocation result;

[0104] An update deployment management module is used to perform deployment management on the update allocation result based on the intelligent scheduling center.

[0105] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0106] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

[0107] This specification and drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, those skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. The method for allocating process approval authority based on time granularity is characterized by: The method comprises: Read the pre-test software, perform hierarchical time granular division based on the test process, and determine the hierarchical test nodes, wherein the pre-test software includes at least one concurrent test software, and the hierarchical test nodes are marked with test features, wherein the hierarchical time granularity uses the main stage of the test process as the main division layer, uses the different test types in each process stage as the granularity division standard determined by the subordinate sub-division layer, and uses the key nodes in each main level and sub-level as hierarchical test nodes; Based on the hierarchical test nodes, a rigid process node and a flexible process node are determined, and an allocation constraint function is set, wherein the flexible process node is marked with a slack, wherein the rigid process node refers to a node that must be executed in a specific order, time or condition in the test process, and the flexible process node refers to a node in the test process that can flexibly adjust the execution order, time or resources within a certain range, and the slack is a measure of whether a node is more easily adjusted or postponed; Training an adaptive approval module, performing module learning based on the hierarchical test nodes and the allocation constraint function, and determining a temporary allocation module, wherein the adaptive approval module includes a plurality of approval units, including an independent process allocation block and a collaborative collision allocation block, wherein the temporary allocation module refers to a module that can change with changes in the system environment and is used to test, verify or simulate the approval process, and each approval unit in the process allocation block performs test node allocation according to the pre-test software and allocation constraint function it is responsible for, and the collision allocation block is used to handle resource conflicts or collisions that may arise due to parallel independent processing; Interactive tester status, which is a time zone status based on time granules, including a rigid state time zone and a flexible state time zone. The rigid state time zone refers to a time zone in which the tester is in a state that cannot be changed or adjusted within certain time granules, while the flexible state time zone refers to a time zone in which the tester is in a relatively flexible state within certain time granules, which can be adjusted or changed as needed. According to the tester status, the temporary allocation module is combined to perform the test node approval allocation of the software test and the collision level iterative optimization allocation based on finite element analysis to determine the target allocation result; Transmitting the target allocation result to the intelligent dispatching center to perform allocation visualization and software testing management on the software testing end and the user end; Among them, each approval unit contains a coordinated collision allocation block, including: The first avoidance principle is determined based on the relaxation, and the second avoidance principle is determined based on the node association influence; Based on the first avoidance principle and the second avoidance principle, average weighting is performed to determine the avoidance priority, and the avoidance allocation principle is set; Based on the avoidance allocation principle, supervised learning is performed on the collision allocation block; Wherein, determining the target allocation result includes: Based on the N approval units mapped by each pre-test software in the temporary allocation module, parallel independent processing is performed based on the process allocation block to determine a first allocation result; The first allocation result is transferred to the collision allocation block, and a multi-objective optimization allocation based on the flexible allocation part is performed with the avoidance allocation principle as a constraint, so as to screen and determine a flexible allocation scheme; Integrate the first allocation result and the flexible allocation plan to determine the target allocation result; Among them, the collision level iterative optimization deployment includes: Building an optimization space based on the flexible process nodes and the flexible state time zones; Performing a layer of iterative optimization based on the same time granules to determine a layer of optimization solutions, wherein the layer of optimization solutions includes a single time granule optimal solution and a set of preferred solutions; Based on the first-layer optimization scheme, a global second-layer iterative optimization is performed with the flexible part as a benchmark to determine the flexible allocation scheme.

2. The method according to claim 1, characterized in that The method further comprises: Reading newly added processing tasks, wherein the newly added processing tasks include software testing tasks and personnel allocation tasks at the user end; Performing time particle collision analysis on the newly added processing task and the target allocation result to locate the collision time particles; In combination with the collision allocation block, based on the relaxation, an allocation decision is made under the collision time granularity to determine the updated allocation result; The updated allocation results are managed based on the intelligent dispatching center.

3. The method according to claim 1, characterized in that The hierarchical time granular division based on the test process includes: Use process stages as the main division layer, test classes as the subordinate sub-division layers, and determine the granularity division standard; Based on the granularity division standard and according to the test cases, the pre-test software is divided into levels to determine the level test nodes, wherein each pre-test software corresponds to a group of level test nodes.

4. The method according to claim 1, characterized in that The optimization iteration methods include: Determine pre-adjustment multiple targets, randomly determine a preset number of finite element adjustment targets, perform random perturbations under constraints in the optimization space, and determine a first optimization solution, wherein the preset number is less than or equal to the number of the pre-adjustment multiple targets; Iterate the optimization based on the first optimization scheme until the preset number of iterations is met and the global optimal solution is selected; Among them, the optimization rule is to use the optimal iteration and the inferior retreat, and the preset number of iterative variables is used as the finite element standard.

5. The process approval authority allocation system based on time granularity is characterized by: The system is used to execute the method for allocating process approval authority based on time granules according to any one of claims 1 to 4, and the system comprises: A hierarchical time granular division module, the hierarchical time granular division module is used to read pre-test software, perform hierarchical time granular division based on the test process, and determine hierarchical test nodes, the pre-test software includes at least one concurrent test software, the hierarchical test nodes are marked with test features, wherein the hierarchical time granules use the main stages of the test process as the main division layer, use the different test types in each process stage as the granularity division standard determined by the subordinate sub-division layer, and use the key nodes in each main layer and sub-layer as hierarchical test nodes; An allocation constraint function setting module, the allocation constraint function setting module is used to determine the rigid process node and the flexible process node based on the hierarchical test node, and set the allocation constraint function, wherein the flexible process node is marked with a slack, wherein the rigid process node refers to a node that must be executed in a specific order, time or condition in the test process, and the flexible process node refers to a node in the test process that can flexibly adjust the execution order, time or resources within a certain range, and the slack is a measure of whether the node is more easily adjusted or postponed; A temporary allocation learning module, the temporary allocation learning module is used to train the adaptive approval module, and the module learning is performed based on the hierarchical test node and the allocation constraint function to determine the temporary allocation module, wherein the adaptive approval module includes a plurality of approval units, including an independent process allocation block and a collaborative collision allocation block, wherein the temporary allocation module refers to a module that can change with the change of the system environment and is used to test, verify or simulate the approval process, and each approval unit in the process allocation block performs test node allocation according to the pre-test software and allocation constraint function it is responsible for, and the collision allocation block is used to handle resource conflicts or collisions that may arise due to parallel independent processing; A tester status interaction module, which is used to interact with the tester status. The tester status is a time zone status based on time granules, including a rigid state time zone and a flexible state time zone. The rigid state time zone refers to a state in which the tester cannot be changed or adjusted within certain time granules, while the flexible state time zone refers to a relatively flexible state in which the tester can be adjusted or changed as needed. A target allocation result determination module, which is used to determine the target allocation result by combining the temporary allocation module with the test node approval allocation of the software test and the collision hierarchy iterative optimization allocation based on finite element analysis according to the tester status; A software testing management module is used to transmit the target allocation result to the intelligent scheduling center to perform allocation visualization and software testing management on the software testing end and the user end.

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