CN112363914B - Parallel test resource allocation optimizing method, computing device and storage medium - Google Patents
Parallel test resource allocation optimizing method, computing device and storage medium Download PDFInfo
- Publication number
- CN112363914B CN112363914B CN202011139174.8A CN202011139174A CN112363914B CN 112363914 B CN112363914 B CN 112363914B CN 202011139174 A CN202011139174 A CN 202011139174A CN 112363914 B CN112363914 B CN 112363914B
- Authority
- CN
- China
- Prior art keywords
- test
- resource allocation
- resource
- time
- resources
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000012360 testing method Methods 0.000 title claims abstract description 227
- 238000013468 resource allocation Methods 0.000 title claims abstract description 65
- 238000000034 method Methods 0.000 title claims abstract description 43
- 238000012549 training Methods 0.000 claims abstract description 12
- 238000004088 simulation Methods 0.000 claims abstract description 6
- 238000004590 computer program Methods 0.000 claims description 4
- 238000012216 screening Methods 0.000 claims description 3
- 230000003287 optical effect Effects 0.000 description 6
- 101150039208 KCNK3 gene Proteins 0.000 description 5
- 101150083764 KCNK9 gene Proteins 0.000 description 4
- 238000012545 processing Methods 0.000 description 4
- 238000005457 optimization Methods 0.000 description 3
- 238000010586 diagram Methods 0.000 description 2
- 230000006870 function Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 239000013307 optical fiber Substances 0.000 description 2
- 230000002093 peripheral effect Effects 0.000 description 2
- 230000000644 propagated effect Effects 0.000 description 2
- 230000003068 static effect Effects 0.000 description 2
- 238000003491 array Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 238000010998 test method Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/36—Preventing errors by testing or debugging software
- G06F11/3668—Software testing
- G06F11/3672—Test management
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Computer Hardware Design (AREA)
- Quality & Reliability (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Test And Diagnosis Of Digital Computers (AREA)
Abstract
One embodiment of the application discloses a method, a computing device and a storage medium for optimizing parallel test resource allocation, wherein the method comprises the following steps: s10, acquiring test parameters, wherein the test parameters comprise test objects, test items and test resources; s20, automatically inputting 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, wherein the test requirement table stores the corresponding relation 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 demand table and the resource allocation table to perform resource allocation optimizing to form a test result table; s40, forming an optimal resource allocation strategy according to the test result table.
Description
Technical Field
The application relates to the field of complex electronic product testing. And more particularly, to a method, computing device, and storage medium for parallel test resource configuration optimization.
Background
The parallel test is an effective method for improving the test efficiency, and has the core that limited test resources are fairly and reasonably distributed to each test task, so that all the tasks which are executed in parallel can be completed in the shortest time under the condition of meeting the resource constraint, and the optimal resource utilization rate of the system is achieved. At present, the parallel test is commonly based on the research of a task scheduling strategy carried out by fixed resources, and the task scheduling strategy comprises two types of dynamic scheduling and static scheduling. The dynamic scheduling can be understood as random scheduling, that is, when a task enters a system and requests for execution of resources, 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 resources and the like; while static scheduling can be understood as fixed scheduling, the scheduling scheme is already scheduled to be designed before the test task is executed.
Conventional parallel testing based on fixed resources has failed to meet the increasing complexity and increasing test needs of test objects.
Disclosure of Invention
The invention aims to provide a parallel test resource allocation optimizing method, computing equipment and a storage medium, which are used for solving the technical problems that the traditional parallel test method is difficult to realize the highest test efficiency and the maximized resource utilization rate.
In order to achieve the above purpose, the following technical scheme is adopted in the application:
in a first aspect, the present application provides a method for optimizing parallel test resource allocation, where the method includes:
s10, acquiring test parameters, wherein the test parameters comprise test objects, test items and test resources;
s20, automatically inputting 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, wherein the test requirement table stores the corresponding relation 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 demand table and the resource allocation table to perform resource allocation optimizing to form a test result table;
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 is independent from each other; the test tasks comprise M test items which are mutually independent and have fixed execution sequence; the test resource is of the type Q, wherein N, M and Q are natural numbers greater than 0.
In a specific embodiment, the first extreme mode of resource allocation in the resource allocation table 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, and the rest of the resource allocation modes are between the first extreme mode and the second extreme mode.
In a specific embodiment, the resource configuration manner of the resource configuration table is N Q A kind of module is assembled in the module and the module is assembled in the module.
In a specific embodiment, the step S30 includes:
s300, combining the test demand table, sequentially executing all resource configuration modes in the resource configuration table, recording the entering time of a first test item and the exiting time of a last test item in the execution process, and obtaining the execution time through a difference value, wherein the use right of a 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 the fact that a certain test resource can only be occupied by a certain test item at the same time is ensured through spin locking;
s302, judge N Q And (3) whether the resource allocation modes are all executed or not, if so, forming a test result table, and if not, repeating the step (S300).
In a specific embodiment, the test result table stores N for the resource allocation table Q And executing time corresponding to the resource allocation mode.
In a specific embodiment, the step S40 includes:
s401, calculating the sustainable longest test time of a unit test object according to the preset test time;
s402, traversing a test result table, and screening out a resource allocation mode with execution time smaller than the sustainable longest test time;
s404, carrying out cost accounting on the screened resource modes, and selecting the most suitable resource allocation mode to form an optimal resource allocation strategy.
In a specific embodiment, the sustainable maximum test time of the unit test object is the preset test time divided by the number of test objects.
In a second aspect, the present application further provides a computing device, including 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 stored thereon a computer program, characterized in that the program, when executed by a processor, implements a method as described in the first aspect above.
The beneficial effects of this application are as follows:
the application discloses a parallel test resource allocation optimizing method based on a fixed task, which constructs a simulation training task according to a test demand table, combines a resource allocation table, searches a strategy which meets the test cycle requirement and realizes the optimal resource allocation through a large amount of training, and can be applied to the parallel test of a single product in a large batch.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 illustrates a flow chart of a method of parallel test resource configuration optimization in accordance with one embodiment of the present application.
FIG. 2 illustrates a structural schematic diagram of a computing device according to one embodiment of the present application.
Detailed Description
For a clearer description of the present application, the present application is further described below with reference to preferred embodiments and the accompanying drawings. Like parts in the drawings 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 should not be taken as limiting the scope of the invention
Example 1
To further improve the testing efficiency, the resource configuration needs to be optimized, and a contract point for enriching the resources and improving the efficiency is found, as shown in fig. 1, an embodiment of the present application provides a method for optimizing the parallel testing resource configuration, where the method includes:
s10, acquiring 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 is independent from each other; the test tasks comprise M test items which are mutually independent and have fixed execution sequence; the test resource is of the type Q, wherein N, M and Q are natural numbers greater than 0.
For example, n=3, m=3, and q=3 are set, that is, three independent test objects having the same attribute are respectively represented by Task1, task2, and Task 3; each test object has the same test task, the test task comprises three independent test items which are respectively represented by Item1, item2 and Item3, and the execution sequence of the test items is fixed; the type of test resources used by each test object is represented by test resource R1, test resource R2, and test resource R3.
S20, automatically inputting 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, wherein the test requirement table stores the corresponding relation 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, and the test requirement table defines an allocation relationship between the test items and the test resources, and it should be noted that the Task1, the Task2 and the Task3 have identical attributes and identical test items.
TABLE 1
| Test tasks | Test item | Test resource 1 | Test resource 2 | Test resource 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 a specific embodiment, the first extreme mode of resource allocation in the resource allocation table 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 of the 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 N Q A kind of module is assembled in the module and the module is assembled in the module.
For example, when n=3, 3 objects to be tested are specified according to S10, then the possible extreme way of resource allocation is to configure a set ofThe number of the test resources, namely R1, R2 and R3 is 1, three tested objects share one set of test resources (corresponding to the sequence number 1 of the table 2) or are configured with three sets of test resources, namely the number of the R1, the R2 and the R3 is 3, the three tested objects share independent test resources (corresponding to the sequence number 27 of the table 2), the rest of the configuration conditions are the conditions that the number of the three test resources is one of 1, 2 and 3 (corresponding to the sequence numbers 27 of the table 2) and the resource configuration tables shown in the table 2 are formed according to the idea, namely 3 3 I.e. 27 ways of resource allocation.
TABLE 2
S30, establishing a simulation training task according to the test demand table and the resource allocation table to perform resource allocation optimizing, and forming a test result table.
Taking the above n=3, m=3, and q=3 as an example, the S30 includes:
s300, in combination with the test requirement table 1, all the resource allocation modes in the resource allocation table 2 are sequentially executed, the use right of the required test resources is firstly obtained before each test item is executed, the resources are released after the execution, other test items cannot obtain occupied resources in the process, the fact that a certain resource can only be occupied by a certain test item at the same time is ensured by spin locking, the entering time of the first test item and the exiting time of the last test item are recorded in the execution process, and the execution time of the test task is obtained through a difference value.
S302, judge N Q If all the resource allocation modes are executed, forming a test result table, if not, repeating S300, wherein the test result table stores N of the resource allocation table types Q And executing time corresponding to the resource allocation mode.
In a specific embodiment, in the case that the resource allocation mode is changed to the number 2 in table 2, the above procedure of S300 is repeated until the test under the number 27 resource allocation mode is completed, so as to form a test result table as shown in table 3, where the test result table stores execution times corresponding to the 27 resource allocation modes of the resource allocation table.
TABLE 3 Table 3
S40, forming an optimal resource allocation strategy according to the test result table.
In a specific embodiment, S40 includes:
s401, calculating the sustainable maximum test time of the unit test object according to the preset test time, wherein the sustainable maximum 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 the tested objects (n=3) is limited and needs to be completed in a limited time (30 minutes), the longest test time that the unit object can withstand, that is, 30/3=10, is calculated, and the longest test time that the unit object can withstand is 10 minutes.
S402, traversing a test result table, and screening out a resource allocation mode with execution time smaller than the sustainable longest test time;
s404, carrying out cost accounting on the screened resource modes, and selecting the most suitable resource allocation mode to form an optimal resource allocation strategy.
For example, the resource configuration condition that the test time is less than the sustainable longest test time in the table 3 is further searched, and the resource configuration cost meeting the condition is calculated, and the most suitable resource configuration is preferentially selected, so that the configuration optimization of the parallel test resources can be realized.
Aiming at the existing problems at present, the application discloses a parallel test resource allocation optimizing method based on a fixed task, which constructs a simulated training task according to a test demand table, combines a resource allocation table, searches a strategy which meets the test period requirement and realizes the optimal resource allocation through a large amount of training, and can be applied to the parallel test of single products in a large scale.
Example two
Fig. 2 illustrates a schematic structural diagram of a computing device provided in another embodiment of the present application. The computing device 50 shown in fig. 2 is merely an example and should not be taken as limiting the functionality and scope of use of embodiments of the present application.
As shown in fig. 2, computing device 50 is 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 connects the various system components, including the system memory 516 and the processing units 500.
Bus 501 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, and a local bus using any of a variety of bus architectures. By way of example, and not limitation, such architectures include Industry Standard Architecture (ISA) bus, micro channel architecture (MAC) bus, enhanced ISA bus, video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus.
Computing device 50 typically includes a variety of computer system readable media. Such media can be any available media that is accessible by computing device 50 and includes both volatile and nonvolatile media, removable and non-removable media.
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, 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 non-volatile magnetic disk (e.g., a "floppy disk"), and an optical disk drive for reading from or writing to a removable non-volatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be coupled to bus 501 through one or more data medium interfaces. Memory 516 may include at least one program product having a set (e.g., at least one) of program modules 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 a 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 or some combination of which may include an implementation of a network environment. Program modules 512 generally perform the functions and/or methods in the embodiments described herein.
Computing device 50 may also communicate with one or more external devices 70 (e.g., keyboard, pointing device, display 60, etc.), one or more devices that enable a user to interact with computing device 50, and/or any devices (e.g., network card, modem, etc.) that enable computing device 50 to communicate with one or more other computing devices. Such communication may occur through an input/output (I/O) interface 502. Moreover, computing device 50 may also communicate with one or more networks such as a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the Internet, through network adapter 514. As shown in FIG. 2, network adapter 514 communicates with other modules of computing device 50 over bus 501. It should be appreciated that although not shown in fig. 2, other hardware and/or software modules may be used in connection with computing device 50, including, but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, data backup storage systems, and the like.
The processor unit 500 executes various functional applications and data processing by running programs stored in the system memory 516, for example, to implement a parallel test resource configuration optimizing method provided in the first embodiment of the present application.
Aiming at the existing problems at present, the application discloses a computing device applying a parallel test resource configuration optimizing method based on a fixed task, a simulated training task is constructed according to a test demand table, and a resource configuration table is combined, so that a strategy which meets the test period requirement and realizes optimal resource configuration is searched through a large amount of training, and the application can be applied to the parallel test of single products in a large batch.
Example III
Another embodiment of the present application provides a computer readable storage medium having stored thereon a computer program which when executed by a processor implements the method provided by the first embodiment described above.
In practical applications, the computer-readable storage medium may take the form of 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. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any 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 this 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.
The computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. 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 of the present application may be written in 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 kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).
Aiming at the existing problems, the method establishes a computer readable storage medium storing the method provided by the first embodiment, constructs a simulation training task according to the test requirement table, combines a resource allocation table, searches a strategy which meets the test period requirement and realizes optimal resource allocation through a large amount of training, and can be applied to the parallel test of single products in a large batch.
It should be 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. Moreover, 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 one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
It should be apparent that the foregoing examples of the present application are merely illustrative of the present application and not limiting of the embodiments of the present application, and that various other changes and modifications may be made by one of ordinary skill in the art based on the foregoing description, and it is not intended to be exhaustive of all embodiments, and all obvious changes and modifications that come within the scope of the present application are intended to be embraced by the technical solution of the present application.
Claims (8)
1. A method for optimizing parallel test resource allocation, comprising:
s10, acquiring test parameters, wherein the test parameters comprise test objects, test items and test resources;
s20, automatically inputting 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, wherein the test requirement table stores the corresponding relation 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 demand table and the resource allocation table to perform resource allocation optimizing to form a test result table;
s40, forming an optimal resource allocation strategy according to the test result table;
the S30 includes:
s300, combining the test demand table, sequentially executing all resource configuration modes in the resource configuration table, recording the entering time of a first test item and the exiting time of a last test item in the execution process, and obtaining the execution time through a difference value, wherein the use right of a 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 the fact that a certain test resource can only be occupied by a certain test item at the same time is ensured through spin locking;
s302, judge N Q Whether the resource allocation modes are all executed or not, if yes, forming a test result table, and if not, repeating the step S300;
the S40 includes:
s401, calculating the sustainable longest test time of a unit test object according to the preset test time;
s402, traversing a test result table, and screening out a resource allocation mode with execution time smaller than the sustainable longest test time;
s404, carrying out cost accounting on the screened resource modes, and selecting the most suitable resource allocation mode to form an optimal resource allocation strategy.
2. The method according to claim 1, wherein the number of 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 mutually independent and have fixed execution sequence; the test resource is of the type Q, wherein N, M and Q are natural numbers greater 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 manners are between the first extreme manner and the second extreme manner.
4. A method according to claim 3, wherein the resource allocation table has a resource allocation pattern of N Q A kind of module is assembled in the module and the module is assembled in the module.
5. The method of claim 1, wherein the test results table stores N for the resource allocation table Q And executing time corresponding to the resource allocation mode.
6. The method of claim 1, wherein the sustainable maximum test time for the unit test object is the preset test time divided by the number of test objects.
7. A computing device comprising a processor and a memory storing a program, wherein the processor implements the method of any of claims 1-6 when executing the program.
8. A computer readable storage medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the method according to any of claims 1-6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011139174.8A CN112363914B (en) | 2020-10-22 | 2020-10-22 | Parallel test resource allocation optimizing method, computing device and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011139174.8A CN112363914B (en) | 2020-10-22 | 2020-10-22 | Parallel test resource allocation optimizing method, computing device and storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112363914A CN112363914A (en) | 2021-02-12 |
| CN112363914B true CN112363914B (en) | 2024-03-19 |
Family
ID=74511664
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011139174.8A Active CN112363914B (en) | 2020-10-22 | 2020-10-22 | Parallel test resource allocation optimizing method, computing device and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112363914B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| 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 |
| 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 |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101976221A (en) * | 2010-10-14 | 2011-02-16 | 北京航空航天大学 | Particle swarm taboo combination-based parallel test task dispatching method and platform |
| CN102646061A (en) * | 2012-02-27 | 2012-08-22 | 北京航空航天大学 | Method for allocating satellite concurrent testing resources based on genetic algorithm |
| CN104461899A (en) * | 2014-12-19 | 2015-03-25 | 北京奇虎科技有限公司 | Method and system for analyzing resource consumption in testing process and testing terminal |
| US9032373B1 (en) * | 2013-12-23 | 2015-05-12 | International Business Machines Corporation | End to end testing automation and parallel test execution |
| US9514037B1 (en) * | 2015-12-16 | 2016-12-06 | International Business Machines Corporation | Test program scheduling based on analysis of test data sets |
| CN108536579A (en) * | 2017-03-01 | 2018-09-14 | 维布络有限公司 | Resource-constrained devices test system and method |
| CN108874650A (en) * | 2017-05-09 | 2018-11-23 | 上海秦苍信息科技有限公司 | A kind of continuous integrating automated testing method |
| CN109240928A (en) * | 2018-09-13 | 2019-01-18 | 广东小天才科技有限公司 | Test method, device, equipment and storage medium |
| CN111522668A (en) * | 2020-04-29 | 2020-08-11 | 青岛海尔科技有限公司 | Method and apparatus for resource configuration and computer-readable storage medium |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10467128B2 (en) * | 2016-09-08 | 2019-11-05 | International Business Machines Corporation | Measuring and optimizing test resources and test coverage effectiveness through run time customer profiling and analytics |
-
2020
- 2020-10-22 CN CN202011139174.8A patent/CN112363914B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101976221A (en) * | 2010-10-14 | 2011-02-16 | 北京航空航天大学 | Particle swarm taboo combination-based parallel test task dispatching method and platform |
| CN102646061A (en) * | 2012-02-27 | 2012-08-22 | 北京航空航天大学 | Method for allocating satellite concurrent testing resources based on genetic algorithm |
| US9032373B1 (en) * | 2013-12-23 | 2015-05-12 | International Business Machines Corporation | End to end testing automation and parallel test execution |
| CN104461899A (en) * | 2014-12-19 | 2015-03-25 | 北京奇虎科技有限公司 | Method and system for analyzing resource consumption in testing process and testing terminal |
| US9514037B1 (en) * | 2015-12-16 | 2016-12-06 | International Business Machines Corporation | Test program scheduling based on analysis of test data sets |
| CN108536579A (en) * | 2017-03-01 | 2018-09-14 | 维布络有限公司 | Resource-constrained devices test system and method |
| CN108874650A (en) * | 2017-05-09 | 2018-11-23 | 上海秦苍信息科技有限公司 | A kind of continuous integrating automated testing method |
| CN109240928A (en) * | 2018-09-13 | 2019-01-18 | 广东小天才科技有限公司 | Test method, device, equipment and storage medium |
| CN111522668A (en) * | 2020-04-29 | 2020-08-11 | 青岛海尔科技有限公司 | Method and apparatus for resource configuration and computer-readable storage medium |
Non-Patent Citations (3)
| Title |
|---|
| An optimizing algorithm for resources allocation in parallel test;Dexin Zhou;《2009 IEEE International Conference on Control and Automation》;1-11 * |
| 基于改进蚁群算法的并行测试任务调度方法研究;范锟;《计算机测量与控制》;2 * |
| 基于遗传蚁群算法的并行测试任务调度与资源配置;方甲永;《测试技术学报》;343-349 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112363914A (en) | 2021-02-12 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108537543B (en) | Parallel processing method, device, equipment and storage medium for blockchain data | |
| CN110515739B (en) | Deep learning neural network model load calculation method, device, equipment and medium | |
| Verma et al. | Orchestrating an ensemble of MapReduce jobs for minimizing their makespan | |
| US7818410B1 (en) | System and method of implementing major application migration | |
| CN112363913B (en) | Parallel test task scheduling optimizing method, device and computing equipment | |
| US8839198B2 (en) | Automated analysis of composite applications | |
| US8606905B1 (en) | Automated determination of system scalability and scalability constraint factors | |
| CN102880503A (en) | Data analysis system and data analysis method | |
| CN112363914B (en) | Parallel test resource allocation optimizing method, computing device and storage medium | |
| CN111045911B (en) | Performance test method, performance test device, storage medium and electronic equipment | |
| CN107451062B (en) | User interface traversal test method, device, server and storage medium | |
| CN110806933A (en) | Batch task processing method, device, equipment and storage medium | |
| CN112052082B (en) | Task attribute optimization method, device, server and storage medium | |
| CN111985831A (en) | Scheduling method and device of cloud computing resources, computer equipment and storage medium | |
| CN111522796A (en) | Data migration method and device between systems, computer equipment and medium | |
| CN103064955A (en) | Inquiry planning method and device | |
| CN115168398A (en) | Data query method and device, electronic equipment and storage medium | |
| CN113204425A (en) | Method and device for process management internal thread, electronic equipment and storage medium | |
| CN110515954B (en) | Data processing method, device, equipment and storage medium | |
| CN114416669B (en) | Group process file management method, device, network disk and storage medium | |
| CN113495498B (en) | Simulation method, simulator, device and medium for hardware device | |
| US10554502B1 (en) | Scalable web services execution | |
| US20150154094A1 (en) | Method and system for optimizing testing efforts in system of systems testing | |
| CN115237931A (en) | Method and system for updating sub-service processing result of service arrangement | |
| CN114217790A (en) | Interface scheduling method and device, electronic equipment and medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |