CN112363914A - Parallel test resource configuration optimization method, computing device and storage medium - Google Patents
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Abstract
One embodiment of the present application discloses a method, a computing device and a storage medium for optimizing parallel test resource configuration, the method comprising: s10, obtaining test parameters, wherein the test parameters comprise test objects, test items and test resources; s20, automatically entering the test parameters into a relational database, wherein the relational database is configured into a test requirement table and a test resource configuration table which are associated with the test parameters, the test requirement table stores the corresponding relationship between the test items and the test resources, and the resource configuration table is related to the number of the test objects and the types of the test resources; s30, establishing a simulation training task according to the test requirement table and the resource allocation table to perform resource allocation optimization, and forming a test result table; and S40, forming an optimal resource allocation strategy according to the test result table.
Description
Technical Field
The present application relates to the field of testing of complex electronic products. And more particularly, to a method, computing device, and storage medium for concurrent test resource configuration optimization.
Background
The core of the parallel test is to fairly and reasonably distribute limited test resources to each test task, so that all tasks executed in parallel can be completed in the shortest time under the condition of meeting resource constraints, and the optimal resource utilization rate of the system is achieved. The current parallel test is generally based on fixed resources to develop task scheduling strategy research, and the task scheduling strategy comprises dynamic scheduling and static scheduling. The dynamic scheduling can be understood as random scheduling, that is, when a task enters the system and applies for the execution of a resource request, the system determines the execution sequence of the task in real time according to a series of factors such as the priority of the task, the use condition of the resource and the like; whereas static scheduling may be understood as fixed scheduling, the scheduling scheme is pre-designed before the test tasks are executed.
The traditional parallel test based on fixed resources can not meet the requirements of complexity increase and test growth of test objects.
Disclosure of Invention
The present application aims to provide a method, a computing device, and a storage medium for optimizing a parallel test resource configuration to solve the technical problems mentioned in the background section above that the traditional parallel test method is difficult to achieve the highest test efficiency and the maximum resource utilization.
In order to achieve the purpose, the following technical scheme is adopted in the application:
in a first aspect, the present application provides a method for optimizing a parallel test resource configuration, the method comprising:
s10, obtaining test parameters, wherein the test parameters comprise test objects, test items and test resources;
s20, automatically entering the test parameters into a relational database, wherein the relational database is configured into a test requirement table and a test resource configuration table which are associated with the test parameters, the test requirement table stores the corresponding relationship between the test items and the test resources, and the resource configuration table is related to the number of the test objects and the types of the test resources;
s30, establishing a simulation training task according to the test requirement table and the resource allocation table to perform resource allocation optimization, and forming a test result table;
and S40, forming an optimal resource allocation strategy according to the test result table.
In a specific embodiment, the number of the test objects is N, and each test object is set to have the same test task and to be independent of each other; the test tasks comprise M test items which are independent from each other and have fixed execution sequence; the kind of the test resource is Q, wherein N, M and Q are natural numbers larger than 0.
In a specific embodiment, a first extreme manner of resource allocation in the resource allocation table is that the N test objects share one set of test resources, a second extreme manner is that the N test objects share N sets of test resources, and the remaining resource allocation manners are between the first extreme manner and the second extreme manner.
In a specific embodiment, the resource allocation manner of the resource allocation table is NQAnd (4) seed preparation.
In a specific embodiment, the S30 includes:
s300, combining the test requirement table, sequentially executing all resource configuration modes in the resource configuration table, recording the entry time of the first test item and the exit time of the last test item in the execution process, and obtaining the execution time through a difference value, wherein the use right of the required test resource is obtained before each test item is executed, the resource is released after the execution, other test items cannot obtain the occupied resource in the process, and a spin lock is used for ensuring that one test resource can only be occupied by one test item at the same time;
s302, judging NQAnd if the resource allocation modes are completely executed, forming a test result table, and if not, repeating the step S300.
In one embodiment, the test result table stores N of the resource allocation tableQAnd (5) executing time corresponding to the resource allocation mode.
In a specific embodiment, the S40 includes:
s401, calculating the longest bearable test time of a unit test object according to preset test time;
s402, traversing the test result table, and screening out a resource allocation mode with the execution time less than the bearable longest test time;
s404, carrying out cost accounting on the screened resource modes, and selecting the most appropriate resource configuration mode to form an optimal resource configuration strategy.
In one embodiment, the maximum allowable test time per unit of the test object is the preset test time divided by the number of the test objects.
In a second aspect, the present application also provides a computing device comprising a processor and a memory storing a program, the processor implementing the method as described in the first aspect above when executing the program.
In a third aspect, the present application also provides a computer readable storage medium having a computer program stored thereon, wherein the program is adapted to, when executed by a processor, perform the method as described in the first aspect above.
The beneficial effect of this application is as follows:
the application discloses a parallel test resource allocation optimizing method based on a fixed task, wherein a simulation training task is established according to a test requirement table, and a strategy which not only meets the requirement of a test period, but also realizes optimal resource allocation is searched through a large number of training by combining a resource allocation table, so that the method can be applied to single product large-batch parallel tests.
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In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 shows a flow diagram of a method of parallel test resource configuration optimization according to one embodiment of the present application.
FIG. 2 shows a schematic structural diagram of a computing device according to one embodiment of the present application.
Detailed Description
In order to more clearly illustrate the present application, the present application is further described below in conjunction with the preferred embodiments and the accompanying drawings. Similar parts in the figures are denoted by the same reference numerals. It is to be understood by persons skilled in the art that the following detailed description is illustrative and not restrictive, and is not to be taken as limiting the scope of the invention
Example one
In order to further improve the testing efficiency, the resource configuration needs to be optimized, and a fitting point between resource enrichment and efficiency improvement is found, as shown in fig. 1, an embodiment of the present application provides a method for parallel testing resource configuration optimization, where the method includes:
s10, obtaining test parameters, wherein the test parameters comprise test objects, test items and test resources;
in a specific embodiment, the number of the test objects is N, and each test object is set to have the same test task and to be independent of each other; the test tasks comprise M test items which are independent from each other and have fixed execution sequence; the kind of the test resource is Q, wherein N, M and Q are natural numbers larger than 0.
For example, N ═ 3, M ═ 3, and Q ═ 3 are set, i.e., three independent test subjects having the same attribute are denoted by Task1, Task2, and Task3, respectively; each test object has the same test task, the test task contains three independent test items, respectively denoted by Item1, Item2 and Item3, and the order of execution of the test items is fixed; the type of test resource used by each test object is represented by test resource R1, test resource R2, and test resource R3.
S20, automatically entering the test parameters into a relational database, wherein the relational database is configured into a test requirement table and a test resource configuration table which are associated with the test parameters, the test requirement table stores the corresponding relationship between the test items and the test resources, and the resource configuration table is related to the number of the test objects and the types of the test resources;
in a specific embodiment, the test requirement table is shown in table 1, the test requirement table defines an allocation relationship between test items and test resources, and it should be noted that Task1, Task2, and Task3 have the same attribute and have the same test items.
TABLE 1
| Testing tasks | Test items | Testing resources 1 | Test resource 2 | Test resources 3 |
| Task1 | Item1 | R1 | R2 | |
| Task1 | Item2 | R2 | R3 | |
| Task1 | Item3 | R1 | R3 | |
| Task2 | Item1 | R1 | R2 | |
| Task2 | Item2 | R2 | R3 | |
| Task2 | Item3 | R1 | R3 | |
| Task3 | Item1 | R1 | R2 | |
| Task3 | Item2 | R2 | R3 | |
| Task3 | Item3 | R1 | R3 |
In one embodiment, the resource allocation tableThe first extreme mode of the resource allocation in (1) is that the N test objects share one set of test resources, the second extreme mode is that the N test objects share N sets of test resources, the rest resource allocation modes are between the first extreme mode and the second extreme mode, and the resource allocation mode of the resource allocation table is that N isQAnd (4) seed preparation.
For example, when N is 3, 3 measurands are determined according to S10, then an extreme possible resource allocation manner is to allocate one set of test resources, i.e. the number of R1, R2 and R3 is 1, three measurands share one set of test resources (corresponding to table 2 number 1) or allocate three sets of test resources, i.e. the number of R1, R2 and R3 is 3, three measurands share independent test resources (corresponding to table 2 number 27), and the rest allocation case is a case of one of three test resources (corresponding to tables 2 to 26), so as to form the resource allocation table shown in table 2, and 3 are shared by the idea (corresponding to tables 2 to 26)3Namely 27 resource allocation modes.
TABLE 2
And S30, establishing a simulation training task according to the test requirement table and the resource allocation table to perform resource allocation optimization, and forming a test result table.
For example, when N is 3, M is 3, and Q is 3, S30 includes:
s300, combining the test requirement table 1, sequentially executing all resource configuration modes in the resource configuration table 2, firstly obtaining the use right of the required test resource before each test item is executed, releasing the resource after the execution, ensuring that other test items cannot obtain the occupied resource, ensuring that a certain resource can only be occupied by a certain test item at the same time through a spin lock, recording the entering time of the first test item and the exiting time of the last test item in the execution process, and obtaining the execution time of the test task through a difference value.
S302, judging NQIf the resource allocation modes are completely executed, forming a test result table if the resource allocation modes are completely executed, otherwise, repeating S300, wherein the test result table stores N types of the resource allocation tableQAnd (5) executing time corresponding to the resource allocation mode.
In an embodiment, when the resource allocation manner is changed to the case of the serial number 2 in table 2, the process of S300 is repeated until the test in the resource allocation manner of the serial number 27 is completed, so as to form a test result table as shown in table 3, where the test result table stores the execution times corresponding to the 27 resource allocation manners in the resource allocation table.
TABLE 3
And S40, forming an optimal resource allocation strategy according to the test result table.
In a particular embodiment, S40 includes:
s401, calculating the longest bearable test time of the unit test object according to the preset test time, wherein the longest bearable test time of the unit test object is the preset test time divided by the number of the test objects.
For example, in an actual test, assuming that the number of objects to be tested (N ═ 3) is limited and needs to be completed in a limited time (30 minutes), the maximum test time that can be borne by the unit object, i.e., 30/3 ═ 10 is calculated, and the maximum test time that can be borne by the unit object is 10 minutes.
S402, traversing the test result table, and screening out a resource allocation mode with the execution time less than the bearable longest test time;
s404, carrying out cost accounting on the screened resource modes, and selecting the most appropriate resource configuration mode to form an optimal resource configuration strategy.
For example, the resource allocation condition that the test time is shorter than the longest bearable test time in the table 3 is further looked up, the resource allocation cost meeting the condition is calculated, and the most suitable resource allocation is selected preferentially, so that the configuration optimization of the parallel test resources can be realized.
Aiming at the existing problems, the application discloses a parallel test resource allocation optimizing method based on a fixed task, wherein a simulation training task is established according to a test requirement table, and a strategy which not only meets the requirement of a test period, but also realizes the optimal resource allocation is searched through a large amount of training by combining a resource allocation table, so that the method can be applied to the large-batch parallel test of a single product.
Example two
Fig. 2 shows a schematic structural diagram of a computing device according to another embodiment of the present application. The computing device 50 shown in fig. 2 is only one example and should not impose any limitations on the functionality or scope of use of embodiments of the present application.
As shown in fig. 2, computing device 50 is embodied in the form of a general purpose computing device. Components of computing device 50 may include, but are not limited to: one or more processors or processing units 500, a system memory 516, and a bus 501 that couples various system components including the system memory 516 and the processing unit 500.
The system memory 516 may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM)504 and/or cache memory 506. Computing device 50 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 508 may be used to read from and write to non-removable, nonvolatile magnetic media (not shown in FIG. 2, and commonly referred to as a "hard disk drive"). Although not shown in FIG. 2, a magnetic disk drive for reading from and writing to a removable, nonvolatile magnetic disk (e.g., a "floppy disk") and an optical disk drive for reading from or writing to a removable, nonvolatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to the bus 501 by one or more data media interfaces. Memory 516 may include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiment one.
A program/utility 510 having a set (at least one) of program modules 512 may be stored, for example, in memory 516, such program modules 512 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which examples or some combination thereof may comprise an implementation of a network environment. Program modules 512 generally perform the functions and/or methodologies of the embodiments described herein.
The processor unit 500 executes various functional applications and data processing by executing programs stored in the system memory 516, for example, to implement a method for optimizing a parallel test resource configuration provided in an embodiment of the present application.
Aiming at the existing problems, the application discloses computing equipment applying a parallel test resource allocation optimization method based on a fixed task, a simulation training task is constructed according to a test requirement table, and a strategy which not only meets the requirement of a test period, but also realizes the optimal resource allocation is searched through a large amount of training by combining a resource allocation table, so that the method can be applied to the large-batch parallel test of a single product.
EXAMPLE III
Another embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored, which when executed by a processor implements the method provided by the first embodiment.
In practice, the computer-readable storage medium may take any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present embodiment, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for aspects of the present application may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
Aiming at the existing problems, the computer readable storage medium storing the method provided by the embodiment is formulated, the simulation training task is constructed according to the test requirement table, and then the strategy which not only meets the requirement of the test period, but also realizes the optimal resource allocation is searched through a large amount of training by combining the resource allocation table, so that the method can be applied to the massive parallel test of single products.
It is noted that, in the description of the present application, relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
It should be understood that the above-mentioned examples are given for the purpose of illustrating the present application clearly and not for the purpose of limiting the same, and that various other modifications and variations of the present invention may be made by those skilled in the art in light of the above teachings, and it is not intended to be exhaustive or to limit the invention to the precise form disclosed.
Claims (10)
1. A method for optimizing parallel test resource allocation, comprising:
s10, obtaining test parameters, wherein the test parameters comprise test objects, test items and test resources;
s20, automatically entering the test parameters into a relational database, wherein the relational database is configured into a test requirement table and a test resource configuration table which are associated with the test parameters, the test requirement table stores the corresponding relationship between the test items and the test resources, and the resource configuration table is related to the number of the test objects and the types of the test resources;
s30, establishing a simulation training task according to the test requirement table and the resource allocation table to perform resource allocation optimization, and forming a test result table;
and S40, forming an optimal resource allocation strategy according to the test result table.
2. The method according to claim 1, wherein the number of the test objects is N, and each test object is set to have the same test task and to be independent of each other; the test tasks comprise M test items which are independent from each other and have fixed execution sequence; the kind of the test resource is Q, wherein N, M and Q are natural numbers larger than 0.
3. The method of claim 2, wherein a first extreme manner of resource allocation in the resource allocation table is that the N test objects share one set of test resources, a second extreme manner is that the N test objects share N sets of test resources, and the remaining resource allocation manner is between the first extreme manner and the second extreme manner.
4. The method according to claim 3, wherein the resource allocation manner of the resource allocation table is NQAnd (4) seed preparation.
5. The method according to claim 1, wherein the S30 includes:
s300, combining the test requirement table, sequentially executing all resource configuration modes in the resource configuration table, recording the entry time of the first test item and the exit time of the last test item in the execution process, and obtaining the execution time through a difference value, wherein the use right of the required test resource is obtained before each test item is executed, the resource is released after the execution, other test items cannot obtain the occupied resource in the process, and a spin lock is used for ensuring that one test resource can only be occupied by one test item at the same time;
s302, judging NQAnd if the resource allocation modes are completely executed, forming a test result table, and if not, repeating the step S300.
6. The method of claim 5, wherein the test result table stores N in the resource allocation tableQAnd (5) executing time corresponding to the resource allocation mode.
7. The method according to claim 1, wherein the S40 includes:
s401, calculating the longest bearable test time of a unit test object according to preset test time;
s402, traversing the test result table, and screening out a resource allocation mode with the execution time less than the bearable longest test time;
s404, carrying out cost accounting on the screened resource modes, and selecting the most appropriate resource configuration mode to form an optimal resource configuration strategy.
8. The method of claim 7, wherein the maximum allowable test time per unit of the test object is the preset test time divided by the number of test objects.
9. A computing device comprising a processor and a memory storing a program, wherein the processor implements the method of any one of claims 1-8 when executing the program.
10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1-8.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN112363913A (en) * | 2020-10-22 | 2021-02-12 | 北京电子工程总体研究所 | Parallel test task scheduling optimization method and device and computing equipment |
| CN112363913B (en) * | 2020-10-22 | 2024-01-26 | 北京电子工程总体研究所 | Parallel test task scheduling optimizing method, device and computing equipment |
| CN115327252A (en) * | 2022-06-27 | 2022-11-11 | 上海轩田工业设备有限公司 | High-low temperature microwave performance test scheduling optimization method and system |
| CN115327252B (en) * | 2022-06-27 | 2023-08-08 | 上海轩田工业设备有限公司 | High-low temperature microwave performance test scheduling optimization method and system |
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