CN107102943B - Intelligent electric energy meter software reliability testing method and system - Google Patents
Intelligent electric energy meter software reliability testing method and system Download PDFInfo
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- CN107102943B CN107102943B CN201710218237.0A CN201710218237A CN107102943B CN 107102943 B CN107102943 B CN 107102943B CN 201710218237 A CN201710218237 A CN 201710218237A CN 107102943 B CN107102943 B CN 107102943B
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Abstract
The invention discloses a software reliability testing method for an intelligent electric energy meter, which comprises the following steps: compiling specific test actions in each test unit, and respectively configuring the test priority and the test parameters of each test unit; constructing a subordinate cloud model of the priority tested by each test unit, and constructing a certainty matrix according to the certainty of the cloud droplets of the subordinate cloud model of the priority tested by each test unit; generating a state transition step according to the certainty matrix and carrying out automatic detection; and generating a test report according to the detection parameters. The invention has the beneficial effects that: by utilizing the cloud model to generate the certainty matrix, the test flow can jump according to the priority probability of the test unit, the test flow randomization can be realized, the immunity of the tested software caused by the sequential test can be avoided, and the test flow is closer to the real running state of the electric energy meter; meanwhile, the labor intensity can be reduced by utilizing automatic testing, and the operation interference caused by manual testing can be avoided.
Description
Technical Field
The invention relates to the technical field of embedded software testing, in particular to a method and a system for testing software reliability of an intelligent electric energy meter.
Background
Software inside the intelligent electric energy meter is an important main center for responding to interactive demands of the electric energy meter operation strategy. Because the electric energy meter software is uniformly filled in the same batch of electric meters in batches, once faults occur, the electric meters in the whole batch have accident potential, and the image and the high-quality service level of an electric power company are directly influenced.
The working emphasis of the integrated test stage of the software test is to verify the integration of different functions, and the gray box test is generally adopted. By developing correct software automatic test activities, the defects of the software can be found in advance and repaired in time, and the product quality is improved. In the software reliability test of the intelligent electric energy meter, the main method at present relies on manual test, and as the test task tends to be complex, the test steps are increased continuously, and an automatic test method is required to replace the manual test of the manual test.
Therefore, it is required to provide a method for testing the reliability of the software of the intelligent electric energy meter as soon as possible, so as to research and analyze the performance of the software of the intelligent electric energy meter.
Disclosure of Invention
The invention provides a method and a system for testing the software reliability of an intelligent electric energy meter, which are used for solving the problem of testing the software reliability of the intelligent electric energy meter.
In order to solve the above problem, according to an aspect of the present invention, there is provided a method for testing software reliability of an intelligent electric energy meter, the method including:
compiling specific test actions in each test unit, and respectively configuring the test priority and the test parameters of each test unit;
constructing a subordinate cloud model of the priority tested by each test unit, and constructing a certainty matrix according to the certainty of the cloud droplets of the subordinate cloud model of the priority tested by each test unit;
generating a state transition step according to the certainty matrix and carrying out automatic detection; and
and generating a test report according to the detection parameters.
Preferably, wherein the configuration parameters include: and task execution time and task execution marks in the test process of each test unit.
Preferably, the task execution flag includes: and the task execution success flag and the task execution failure flag are set, wherein the task execution success flag is set to be 1, and the task execution failure flag is set to be 0.
Preferably, the constructing a membership cloud model of the priority of each test unit test, and the constructing a certainty matrix according to the certainty of the cloud droplets of the membership cloud model of the priority of each test unit test includes:
constructing a subordinate cloud model of each test unit by using a forward cloud generator according to a cloud model intelligent algorithm; and
and constructing a certainty matrix according to the certainty of each cloud drop in the membership cloud model.
Preferably, the step of generating a state transition according to the certainty matrix and performing automated detection includes:
and taking the test unit represented by the element with the maximum row number in the certainty matrix as a transfer target of the next step, performing state jump according to the transfer target, and performing automatic test.
According to another aspect of the invention, a software reliability test system for an intelligent electric energy meter is provided, which comprises: a setting unit, a certainty matrix constructing unit, an automatic detecting unit and a test report generating unit,
the setting unit is used for compiling specific test actions in each test unit and respectively configuring the test priority and the test parameters of each test unit;
the transition probability matrix production unit is used for constructing a subordinate cloud model of the test priority of each test unit and constructing a certainty matrix according to the certainty of the cloud droplets of the subordinate cloud model of the test priority of each test unit;
the automatic detection unit is used for generating a state transition step according to the certainty matrix and carrying out automatic detection; and
and the test report generating unit is used for generating a test report according to the detection parameters.
Preferably, wherein the configuration parameters include: and task execution time and task execution marks in the test process of each test unit.
Preferably, the task execution flag includes: and the task execution success flag and the task execution failure flag are set, wherein the task execution success flag is set to be 1, and the task execution failure flag is set to be 0.
Preferably, the determining degree matrix constructing unit is configured to construct a subordinate cloud model of the priority level of each test unit test, and construct a determining degree matrix according to the determining degree of the cloud droplet of the subordinate cloud model of the priority level of each test unit test, and includes:
constructing a subordinate cloud model of each test unit by using a forward cloud generator according to a cloud model intelligent algorithm; and
and constructing a certainty matrix according to the certainty of the cloud droplets in the membership cloud model.
Preferably, the automatic detection unit generates a state transition step according to the certainty matrix and performs automatic detection, and the method includes:
and taking the test unit represented by the element with the maximum row number in the certainty matrix as a transfer target of the next step, performing state jump according to the transfer target, and performing automatic test.
The invention has the beneficial effects that:
according to the technical scheme, the cloud model method is utilized, so that the test process can jump according to the priority probability of the test unit, the test process is randomized, immunity of the tested software caused by sequential test is avoided, and the test process is closer to the real running state of the electric energy meter; meanwhile, the labor intensity can be reduced by utilizing automatic testing, the operation interference caused by manual testing can be avoided, and the accuracy is improved.
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A more complete understanding of exemplary embodiments of the present invention may be had by reference to the following drawings in which:
FIG. 1 is a flow diagram of a reliability testing method 100 according to an embodiment of the invention; and
FIG. 2 is a schematic diagram of a reliability testing system 200 according to an embodiment of the invention.
Detailed Description
The exemplary embodiments of the present invention will now be described with reference to the accompanying drawings, however, the present invention may be embodied in many different forms and is not limited to the embodiments described herein, which are provided for complete and complete disclosure of the present invention and to fully convey the scope of the present invention to those skilled in the art. The terminology used in the exemplary embodiments illustrated in the accompanying drawings is not intended to be limiting of the invention. In the drawings, the same units/elements are denoted by the same reference numerals.
Unless otherwise defined, terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Further, it will be understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense.
FIG. 1 is a flow chart of a reliability testing method 100 according to an embodiment of the invention. As shown in fig. 1, the reliability testing method 100 is used for performing reliability testing on intelligent electric energy meter software. The reliability testing method 100 of the embodiment of the invention firstly compiles specific testing actions in each testing unit, respectively configures the testing priority and detection parameters of each testing unit, then generates a certainty matrix by using a cloud model according to the testing priority of each testing unit, carries out automatic detection according to the state transfer step generated by the certainty matrix, and finally generates a testing report, thereby solving the problem of reliability testing of electric energy meter software, realizing test flow randomization and avoiding operation interference generated by manual testing. The reliability testing method starts at step 101, and compiles specific testing actions in each testing unit and configures the testing priority and the testing parameters of each testing unit respectively at step 101. Preferably, wherein the configuration parameters include: and task execution time and task execution marks in the test process of each test unit. Preferably, the task execution flag includes: task execution success flagAnd a task execution failure flag, wherein the task execution success flag is set to 1, and the task execution failure flag is set to 0. In an embodiment of the invention, n integrated test units are constructed and each of the test units is taken as a transition state of the Markov model. Where xi (xi ═ a, B, c.) denotes the number of each test unit, and θ denotes the number of each test unitξ(ξ ═ a, B, c.) denotes the priority of each test unit test. In the unit test, the electric energy meter is considered to execute the test task, t represents the execution time, and p represents the task execution success mark or the task failure mark.
Preferably, in step 102, a membership cloud model of the priority level of each test unit test is constructed, and a certainty matrix is constructed according to the certainty of the cloud droplets in the membership cloud model of the priority level of each test unit test. Preferably, the constructing a membership cloud model of the priority of each test unit test, and the constructing a certainty matrix according to the certainty of the cloud droplets of the membership cloud model of the priority of each test unit test includes:
constructing a subordinate cloud model of each test unit by using a forward cloud generator according to a cloud model intelligent algorithm; and
and constructing a certainty matrix according to the certainty of each cloud drop in the membership cloud model. And outputting a plurality of cloud droplets meeting certain probability distribution by using a cloud model intelligent algorithm and taking the priority of the test unit as an input quantity, and constructing a certainty matrix according to the certainty of the cloud droplets. When the Cloud model is used, for input characteristic parameters in the Cloud model, the numerical value of an expected E is equal to theta, the entropy En, the super-entropy He and the Cloud droplet number are set according to the field application condition, then a forward Cloud generator in the Cloud model theory is used for constructing a membership Cloud model of each test unit test priority, and the membership Cloud model is recorded as membership Cloudξ(ξ ═ a, B, c.); the output parameter of the cloud model is cloud droplets which are subordinate to the cloud, and the cloud droplets which are subordinate to the cloud are set to
Wherein i represents the ith cloud droplet in the membership cloud
And two parameters x and mu are provided, wherein x is the expected value of the cloud drop, mu represents the certainty of the cloud drop to the corresponding membership cloud, and the certainty mu of the cloud drop in each membership cloud is used for constructing a matrix:
wherein m is the total number of cloud droplets, and n is the total number of items tested by the test unit.
Preferably, a state transition step is generated in step 103 based on the certainty matrix and automated detection is performed. Preferably, the step of generating a state transition according to the certainty matrix and performing automated detection includes:
and taking the test unit represented by the element with the maximum row number in the certainty matrix as the target of the next state transition, performing state jump according to the transferred target, and performing automatic test. In an embodiment of the invention, the matrix T is selectedm×nAnd the testing unit xi represented by the element with the maximum value in each row is the target of the next state transition, and the automatic test is carried out.
Preferably, a test report is generated in step 104 based on the detection parameters. Wherein the test report includes: a task execution success flag or a task execution failure flag p, and an execution time t. And the tester can analyze the reliability of the electric energy meter software according to the test report.
FIG. 2 is a schematic diagram of a reliability testing system 200 according to an embodiment of the invention. As shown in fig. 2, the reliability testing system 200 includes: a setting unit 201, a certainty matrix constructing unit 202, an automation detecting unit 203, and a test report generating unit 204. Preferably, the setting unit 201 compiles specific test actions in each test unit, and configures the test priority and the test parameters of each test unit respectively. Preferably, wherein the configuration parameters include: and task execution time and task execution marks in the test process of each test unit. Preferably, the task execution flag includes: and the task execution success flag and the task execution failure flag are set, wherein the task execution success flag is set to be 1, and the task execution failure flag is set to be 0.
Preferably, the transition probability matrix production unit 202 generates a certainty matrix by using a cloud model according to the test priority of each test unit. Preferably, the constructing unit 202 of the certainty matrix constructs a subordinate cloud model of the priority of each test unit test, and constructs the certainty matrix according to the certainty of the cloud droplets in the subordinate cloud model of the priority of each test unit test, including:
constructing a subordinate cloud model of each test unit by using a forward cloud generator according to a cloud model intelligent algorithm; and
and constructing a certainty matrix according to the certainty of each cloud drop in the membership cloud model.
Preferably, a state transition step is generated in the automation detection unit 203 according to the certainty matrix, and automation detection is performed. Preferably, the automatic detection unit 203 generates a state transition step according to the certainty matrix and performs automatic detection, including:
and taking the test unit represented by the element with the maximum row number in the certainty matrix as a transfer target of the next step, performing state jump according to the transfer target, and performing automatic test.
Preferably, the test report generating unit 204 generates a test report according to the detection parameters.
The system 200 for testing the reliability of the intelligent electric energy meter software according to the embodiment of the present invention corresponds to the method 100 for testing the reliability of the intelligent electric energy meter software according to another embodiment of the present invention, and details thereof are not repeated herein.
The invention has been described with reference to a few embodiments. However, other embodiments of the invention than the one disclosed above are equally possible within the scope of the invention, as would be apparent to a person skilled in the art from the appended patent claims.
Generally, all terms used in the claims are to be interpreted according to their ordinary meaning in the technical field, unless explicitly defined otherwise herein. All references to "a/an/the [ device, component, etc ]" are to be interpreted openly as referring to at least one instance of said device, component, etc., unless explicitly stated otherwise. The steps of any method disclosed herein do not have to be performed in the exact order disclosed, unless explicitly stated.
Claims (6)
1. A method for testing software reliability of an intelligent electric energy meter is characterized by comprising the following steps:
compiling specific test actions in each test unit, and respectively configuring the test priority and the test parameters of each test unit;
constructing a subordinate cloud model of the priority tested by each test unit, and constructing a certainty matrix according to the certainty of cloud droplets in the subordinate cloud model of the priority tested by each test unit; according to the cloud model intelligent algorithm, a forward cloud generator is used for constructing a subordinate cloud model of each test unit, the priority of each test unit is used as an input quantity, a plurality of cloud droplets meeting preset probability distribution are output, and when the cloud model is applied, the numerical value of an expected E is equal to theta, and the theta is equal to the input characteristic parameter of the cloud modelξ(ξ ═ a, B, c.) represents the priority of each test unit test; xi represents the number of each test unit; setting entropy En, super entropy He and cloud drop number; remember that the membership Cloud is Cloudξ(ξ ═ a, B, c.); the output parameter of the cloud model is cloud droplets which are subordinate to the cloud, and the cloud droplets which are subordinate to the cloud are set to
Wherein i represents the ith cloud droplet in the membership cloud
And two parameters x and mu are provided, wherein x is the expected value of the cloud drop, mu represents the certainty of the cloud drop to the corresponding membership cloud, and the certainty mu of the cloud drop in each membership cloud is used for constructing a matrix:
wherein m is the total number of cloud droplets, and n is the total number of items tested by the test unit;
generating a state transition step according to the certainty matrix and carrying out automatic detection; taking a test unit represented by the element with the maximum row number in the certainty matrix as a target of next state transition, performing state jump according to the transferred target, and performing automatic test;
and generating a test report according to the detection parameters.
2. The method of claim 1, wherein detecting the parameter comprises: and task execution time and task execution marks in the test process of each test unit.
3. The method of claim 2, wherein the task performance flag comprises: and the task execution success flag and the task execution failure flag are set, wherein the task execution success flag is set to be 1, and the task execution failure flag is set to be 0.
4. An intelligent electric energy meter software reliability testing system, characterized in that, the system includes: a setting unit, a certainty matrix constructing unit, an automatic detecting unit and a test report generating unit,
the setting unit is used for compiling specific test actions in each test unit and respectively configuring the test priority and the test parameters of each test unit;
the certainty matrix constructing unit is used for constructing a subordinate cloud model of the priority tested by each testing unit and constructing a certainty matrix according to the certainty of the cloud droplets of the subordinate cloud model of the priority tested by each testing unit; wherein the certainty matrix constructing unit is specifically configured to: constructing a subordinate cloud model of each test unit by using a forward cloud generator according to a cloud model intelligent algorithm to test the unitsThe priority is input quantity, a plurality of cloud droplets meeting the preset probability distribution are output, and when the cloud model is applied, the numerical value of E is expected to be equal to theta for input characteristic parameters in the cloud model, and the theta isξ(ξ ═ a, B, c.) represents the priority of each test unit test; xi represents the number of each test unit; setting entropy En, super entropy He and cloud drop number; remember that the membership Cloud is Cloudξ(ξ ═ a, B, c.); the output parameter of the cloud model is cloud droplets which are subordinate to the cloud, and the cloud droplets which are subordinate to the cloud are set to
Wherein i represents the ith cloud droplet in the membership cloud
And two parameters x and mu are provided, wherein x is the expected value of the cloud drop, mu represents the certainty of the cloud drop to the corresponding membership cloud, and the certainty mu of the cloud drop in each membership cloud is used for constructing a matrix:
wherein m is the total number of cloud droplets, and n is the total number of items tested by the test unit;
the automatic detection unit is used for generating a state transition step according to the certainty matrix and carrying out automatic detection; wherein, the automatic detection unit is specifically used for: taking a test unit represented by the element with the maximum row number in the certainty matrix as a target of next state transition, performing state jump according to the transferred target, and performing automatic test;
and the test report generating unit is used for generating a test report according to the detection parameters.
5. The system of claim 4, wherein the detection parameters comprise: and task execution time and task execution marks in the test process of each test unit.
6. The system of claim 5, wherein the task performance flag comprises: and the task execution success flag and the task execution failure flag are set, wherein the task execution success flag is set to be 1, and the task execution failure flag is set to be 0.
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| CN104503913A (en) * | 2014-12-27 | 2015-04-08 | 中国人民解放军63655部队 | Component software reliability evaluation method based on migration paths and improved Markov chain |
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| CN103164337A (en) * | 2013-02-28 | 2013-06-19 | 汉柏科技有限公司 | Cloud calculation software test method based on finite-state machine (FSM) |
| CN104503913A (en) * | 2014-12-27 | 2015-04-08 | 中国人民解放军63655部队 | Component software reliability evaluation method based on migration paths and improved Markov chain |
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