CN114416583A - Workload determination method, device, equipment and storage medium for automatic test - Google Patents
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Abstract
The invention discloses a method, a device, equipment and a storage medium for determining workload of automatic test, wherein the method comprises the following steps: acquiring an automatic test workload estimation model; determining parameters corresponding to each stage in the automated test, wherein the stages comprise a planning stage, a preparation stage, an execution stage and a summary stage; and inputting the parameters corresponding to each stage into an estimation model to obtain the workload of the automatic test. The method and the device have the advantages that the workload of the automatic test is obtained based on the workload estimation model obtained in advance according to the determined parameters by determining the parameters corresponding to each stage in the automatic test, so that the efficiency and the accuracy of workload obtaining are improved.
Description
Technical Field
The invention relates to the technical field of software development, in particular to a workload determination method, device, equipment and storage medium for automatic testing.
Background
With the rapid development of the computer internet industry, the investment of software project development is higher and higher, and the testing work is a key link in the software project development work.
In order to effectively organize the test work of a system project, the workload of the test work needs to be reasonably estimated, the overall workload of the project can be estimated by a function point, an expert estimation and the like, but particularly the test workload, especially the test workload which is tested by adopting an automatic test tool, cannot be scientifically and reasonably estimated, so that the condition that the investment of human resources in a test link is insufficient or test manpower is arranged only by the experience of a test manager, and test activities cannot be scientifically and finely managed.
Disclosure of Invention
The invention provides a workload determination method, a workload determination device, equipment and a storage medium for an automatic test, which aim to solve the problem that the total workload determination cannot be realized in the automatic test.
According to an aspect of the present invention, there is provided a workload determination method for an automation test, including: acquiring an automatic test workload estimation model;
determining parameters corresponding to each stage in the automated test, wherein the stages comprise a planning stage, a preparation stage, an execution stage and a summary stage;
and inputting the parameters corresponding to each stage into the estimation model to obtain the workload of the automatic test.
According to an aspect of the present invention, there is provided a workload determination apparatus for an automated test, including: the estimation model obtaining module is used for obtaining an automatic testing workload estimation model;
the parameter determining module is used for determining parameters corresponding to each stage in the automatic test, wherein the stages comprise a planning stage, a preparation stage, an execution stage and a summary stage;
and the total work amount determining module is used for inputting the parameters corresponding to each stage into the estimation model to obtain the workload of the automatic test.
According to another aspect of the present invention, there is provided an electronic apparatus including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores a computer program executable by the at least one processor, the computer program being executable by the at least one processor to enable the at least one processor to perform a method of workload determination for automated testing according to any of the embodiments of the invention.
According to another aspect of the present invention, there is provided a computer-readable storage medium storing computer instructions for causing a processor to implement the workload determination method for automated testing according to any one of the embodiments of the present invention when the computer instructions are executed.
According to the technical scheme of the embodiment of the invention, the workload of the automatic test is obtained based on the pre-obtained workload estimation model according to the determined parameters by determining the corresponding parameters of each stage in the automatic test, so that the efficiency and the accuracy of workload obtaining are improved.
It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present invention, nor do they necessarily limit the scope of the invention. Other features of the present invention will become apparent from the following description.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used 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 invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a flowchart of a workload determination method for an automated test according to an embodiment of the present invention;
FIG. 2 is a flowchart of a workload determination method for an automated test according to a second embodiment of the present invention;
fig. 3 is a schematic structural diagram of a workload determination apparatus for an automated test according to a third embodiment of the present invention;
fig. 4 is a schematic structural diagram of an electronic device implementing the workload determination method for automated testing according to the embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Example one
Fig. 1 is a flowchart of a workload determination method for an automated test according to an embodiment of the present invention, where this embodiment is applicable to a case of determining a workload of an automated test, and the method may be executed by a workload determination apparatus for an automated test according to an embodiment of the present invention, and the apparatus may be implemented in a form of hardware and/or software. As shown in fig. 1, the method includes:
and step S110, obtaining an automatic testing workload estimation model.
Optionally, obtaining an automated testing workload estimation model includes: obtaining an automatic test sample; and training according to the automatic test sample to obtain an automatic test workload estimation model.
Specifically, in the present embodiment, before obtaining the estimation model, an automatic test sample is obtained first, where the automatic test sample in the present embodiment may be a plurality of projects that have completed a test work, so as to obtain a relevant parameter of each project and an actual workload corresponding to the project in a test process, so as to use the relevant parameter and the actual workload of each project as the automatic test sample, and in order to ensure accuracy of model training, a large number of projects are generally required as sample data sources in the present embodiment.
After the automatic test sample is obtained, the automatic test sample data is input into the initial deep learning network model, the initial deep learning network model is trained according to the preset iteration times through the sample, and the automatic test workload estimation model is obtained according to the training result.
Step S120, determining a parameter corresponding to each stage in the automated test.
Optionally, determining a parameter corresponding to each stage in the automated test includes: acquiring steps included in each stage in the automatic test; and determining parameters corresponding to each step, wherein the parameters comprise working contents, parameter names and parameter values.
Optionally, the parameter name includes a system characteristic, a tested transaction amount, a script number, and an execution turn.
In this embodiment, it may be determined that the phases involved in the automated test specifically include a planning phase, a preparation phase, an execution phase, a summary phase, and the like, and steps included in each phase are acquired, for example, a test planning step included in the acquisition planning phase; acquiring tested system deployment verification steps, data preparation steps and script program compiling and debugging steps which are included in a preparation stage; acquiring a test execution step contained in an execution stage; the test summary steps and the like included in the summary stage are obtained, and the parameters corresponding to each step are determined, but this embodiment is merely an example, and the specific steps involved in each stage are not limited, as shown in table 1 below, which is an example of the parameters corresponding to each stage in the automated test:
TABLE 1
The parameters corresponding to each step specifically include work content, parameter name, parameter value, and the like, and since the space limitation table 1 only exemplifies the parameters corresponding to each stage in the automated test, and does not limit the specific type of the parameters corresponding to each stage, as shown in the following table 2, the following description is a specific definition of the tool record in table 1:
TABLE 2
| Parameter definition | Tool recording | Hand writing |
| The manual intervention frequency in the execution process is less than or equal to 1 | Class I | Class IV |
| The manual intervention frequency is less than or equal to 5 in the execution process of more than or equal to 2 | Class II | Class V |
| The manual intervention times in the execution process are more than or equal to 6 | Class III | Class VI |
The following table 3 shows the specific definition of the parameters in table 1:
TABLE 3
Step S130, inputting the parameters corresponding to each stage into the estimation model to obtain the workload of the automatic test.
Optionally, inputting the parameter corresponding to each stage into the estimation model to obtain the workload of the automated test, including: denoising the parameters corresponding to each stage to obtain the processed parameters; and inputting the processed parameters into an estimation model to obtain the workload of the automatic test.
Specifically, in this embodiment, after the parameters corresponding to each stage are obtained, denoising processing needs to be performed on the parameters corresponding to each stage, for example, parameters with obvious errors in table 1 are removed, so as to ensure the accuracy of the data input into the estimation model.
According to the technical scheme of the embodiment of the invention, the workload of the automatic test is obtained based on the pre-obtained workload estimation model according to the determined parameters by determining the corresponding parameters of each stage in the automatic test, so that the efficiency and the accuracy of workload obtaining are improved.
Example two
Fig. 2 is a flowchart of a workload determination method for an automatic test according to a second embodiment of the present invention, where this embodiment is based on the foregoing embodiment, and after obtaining the workload of the automatic test, the method further includes correcting the estimation model according to an actual workload corresponding to an implementation process of the automatic test. As shown in fig. 2, the method includes:
and step S210, obtaining an automatic testing workload estimation model.
Optionally, obtaining an automated testing workload estimation model includes: obtaining an automatic test sample; and training according to the automatic test sample to obtain an automatic test workload estimation model.
Step S220, determining the corresponding parameters of each stage in the automated test.
Optionally, determining a parameter corresponding to each stage in the automated test includes: acquiring steps included in each stage in the automatic test; and determining parameters corresponding to each step, wherein the parameters comprise working contents, parameter names and parameter values.
Optionally, the parameter name includes a system characteristic, a tested transaction amount, a script number, and an execution turn.
Step S230, inputting the parameters corresponding to each stage into the estimation model, and obtaining the workload of the automated testing.
Optionally, inputting the parameter corresponding to each stage into the estimation model to obtain the workload of the automated test, including: denoising the parameters corresponding to each stage to obtain the processed parameters; and inputting the processed parameters into an estimation model to obtain the workload of the automatic test.
Step S240, acquiring an actual workload corresponding to the automated test implementation process.
Specifically, in the embodiment, the actual workload corresponding to the implementation and execution process of the automated test is also obtained, wherein the actual workload includes the actual number of people and the actual number of days. And the actual workload may or may not be the same as the estimated workload output by the estimation model.
And step S250, correcting the estimation model according to the Newton' S cooling law by adopting the actual workload.
Specifically, the automatic test workload estimation model in the embodiment is used as an estimation model, the calculation of each parameter value is a core important process, the model uses an empirical value as an initial parameter value, a parameter value corresponding to a real project is continuously obtained through an actual project, and then the estimated parameter value is weighted and corrected through a newton cooling law so as to guide the next estimation process. The model has strong convergence, the estimated value is infinitely close to the actual value along with the accumulation of the items, and the correction of the estimated model is realized through continuous iteration and parameter value correction of the actual items. Therefore, the target that the estimated value is infinitely close to the actual value is finally achieved through accumulation of a large number of projects in the embodiment, reasonable resource distribution is facilitated, and smooth development of the projects is guaranteed. Of course, in this embodiment, the newton cold region law is merely used as an example for explanation, and a specific algorithm for correction is not limited, and as long as the estimation model can be corrected and optimized, the method is within the protection scope of the present application, and details in this embodiment are not described again.
The method comprises the steps of reasonably dividing testing working stages, defining working contents of all stages and identifying characteristics of a system, providing a model capable of quantitatively estimating automatic testing workload, providing a calculation methodology, and popularizing and applying the overall model construction idea to other types of estimation work. The method provides a tool basis for the test personnel and IT project management personnel to carry out fine management, can scientifically allocate test resources and improve project development efficiency.
According to the technical scheme of the embodiment of the invention, the workload of the automatic test is obtained based on the pre-obtained workload estimation model according to the determined parameters by determining the corresponding parameters of each stage in the automatic test, so that the efficiency and the accuracy of workload obtaining are improved. And the estimation model is corrected by adopting the actual workload, so that the estimated value is infinitely close to the target of the actual value, the reasonable distribution of resources is facilitated, and the smooth development of projects is ensured.
EXAMPLE III
Fig. 3 is a schematic structural diagram of a workload determination apparatus for an automated test according to a third embodiment of the present invention. As shown in fig. 3, the apparatus includes:
an estimation model obtaining module 310, configured to obtain an automatic test workload estimation model;
the parameter determining module 320 is configured to determine a parameter corresponding to each stage in the automated test, where the stages include a planning stage, a preparation stage, an execution stage, and a summary stage;
the total work amount determining module 330 is configured to input the parameters corresponding to each stage into the estimation model to obtain the workload of the automated testing.
Optionally, the estimation model obtaining module is specifically configured to obtain an automated test sample;
and training according to the automatic test sample to obtain an automatic test workload estimation model.
Optionally, the parameter determining module includes:
the step determination submodule is used for acquiring steps contained in each stage in the automatic test;
and the parameter determining submodule is used for determining the parameters corresponding to each step, wherein the parameters comprise working contents, parameter names and parameter values.
Optionally, the parameter name includes a system characteristic, a tested transaction amount, a script number, and an execution turn.
Optionally, the step determining sub-module is configured to obtain a test plan making step included in the planning stage;
acquiring tested system deployment verification steps, data preparation steps and script program compiling and debugging steps which are included in a preparation stage;
acquiring a test execution step contained in an execution stage;
and acquiring a test summary step contained in the summary stage.
Optionally, the total work amount determining module is configured to perform denoising processing on the parameter corresponding to each stage to obtain a processed parameter;
and inputting the processed parameters into an estimation model to obtain the workload of the automatic test.
Optionally, the device further includes a model correction module, configured to obtain an actual workload corresponding to the automated test implementation process, where the actual workload includes an actual number of people and an actual number of days;
and correcting the estimation model according to Newton's cooling law by adopting the actual workload.
The workload determining device for the automatic test, provided by the embodiment of the invention, can execute the workload determining method for the automatic test, provided by any embodiment of the invention, and has corresponding functional modules and beneficial effects of the executing method.
Example four
FIG. 4 shows a schematic block diagram of an electronic device 10 that may be used to implement an embodiment of the invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.
As shown in fig. 4, the electronic device 10 includes at least one processor 11, and a memory communicatively connected to the at least one processor 11, such as a Read Only Memory (ROM)12, a Random Access Memory (RAM)13, and the like, wherein the memory stores a computer program executable by the at least one processor, and the processor 11 can perform various suitable actions and processes according to the computer program stored in the Read Only Memory (ROM)12 or the computer program loaded from a storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data necessary for the operation of the electronic apparatus 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.
A number of components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, or the like; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network such as the internet and/or various telecommunication networks.
The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, or the like. Processor 11 performs various methods and processes described above, such as method XXX.
In some embodiments, the workload determination method of automated testing may be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of method XXX described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured by any other suitable means (e.g., by means of firmware) to perform the workload determination method of the automated test.
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), system on a chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
A computer program for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be performed. A computer program can execute entirely on a machine, partly on a machine, as a stand-alone software package partly on a machine and partly on a remote machine or entirely on a remote machine or server.
In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. A computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on 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.
To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), Wide Area Networks (WANs), blockchain networks, and the internet.
The computing system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical host and VPS service are overcome.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present invention may be executed in parallel, sequentially, or in different orders, and are not limited herein as long as the desired results of the technical solution of the present invention can be achieved.
The above-described embodiments should not be construed as limiting the scope of the invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A workload determination method for automated testing, comprising:
acquiring an automatic test workload estimation model;
determining parameters corresponding to each stage in the automated test, wherein the stages comprise a planning stage, a preparation stage, an execution stage and a summary stage;
and inputting the parameters corresponding to each stage into the estimation model to obtain the workload of the automatic test.
2. The method of claim 1, wherein obtaining an automated test workload estimation model comprises:
obtaining an automatic test sample;
and training according to the automatic test sample to obtain the automatic test workload estimation model.
3. The method of claim 1, wherein determining the parameters corresponding to each stage in the automated test comprises:
acquiring steps included in each stage in the automatic test;
and determining parameters corresponding to each step, wherein the parameters comprise working contents, parameter names and parameter values.
4. The method of claim 3, wherein the parameter names include system characteristics, number of transactions tested, number of scripts, and execution rounds.
5. The method of claim 3, wherein the step of obtaining the information contained in each stage of the automated testing comprises:
a test plan making step included in the planning stage is obtained;
acquiring a tested system deployment verification step, a data preparation step and a script program compiling and debugging step which are included in the preparation stage;
acquiring a test execution step contained in the execution stage;
and acquiring the test summary steps contained in the summary stage.
6. The method of claim 1, wherein the inputting the parameters corresponding to each stage into the estimation model to obtain the workload of the automated testing comprises:
denoising the parameters corresponding to each stage to obtain the processed parameters;
and inputting the processed parameters into the estimation model to obtain the workload of the automatic test.
7. The method according to any one of claims 1 to 6, wherein after inputting the parameters corresponding to each stage into the estimation model and obtaining the workload of the automated testing, the method further comprises:
acquiring actual workload corresponding to the automatic test implementation process, wherein the actual workload comprises actual number of people and actual number of days;
and correcting the estimation model according to Newton's cooling law by adopting the actual workload.
8. An automated testing workload determination apparatus, comprising:
the estimation model obtaining module is used for obtaining an automatic testing workload estimation model;
the parameter determining module is used for determining parameters corresponding to each stage in the automatic test, wherein the stages comprise a planning stage, a preparation stage, an execution stage and a summary stage;
and the total work amount determining module is used for inputting the parameters corresponding to each stage into the estimation model to obtain the workload of the automatic test.
9. An electronic device, characterized in that the electronic device comprises:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the method of automated testing workload determination of any of claims 1-7.
10. A computer-readable storage medium storing computer instructions for causing a processor to perform the method of automated testing workload determination of any of claims 1-7 when executed.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115187130A (en) * | 2022-07-29 | 2022-10-14 | 青岛美迪康数字工程有限公司 | Method and device for judging working efficiency based on mouse motion track |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115187130A (en) * | 2022-07-29 | 2022-10-14 | 青岛美迪康数字工程有限公司 | Method and device for judging working efficiency based on mouse motion track |
| CN115187130B (en) * | 2022-07-29 | 2023-11-21 | 青岛美迪康数字工程有限公司 | Method and device for judging working efficiency based on mouse movement track |
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