CN112000558A - Method for generating automatic test case of rail transit signal system - Google Patents
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Abstract
The invention discloses a method for generating an automatic test case of a rail transit signal system, which comprises the following steps: step S1: analyzing a case scene; step S2: extracting specific influence factors of the scene; step S3: orderly combining the influencing factors through a CT algorithm, and automatically generating test data which covers all scenes and all function points and fault points; step S4: automatically screening and eliminating test data; step S5: selecting partial test data results to carry out manual analysis, and further obtaining a data set with a label; modeling and analyzing the obtained data set by using an LSSVM algorithm to obtain an LSSVM model; and performing expected result judgment and identification on the unknown samples by using the LSSVM model to obtain an expected result of each sample data, and combining the expected result with the corresponding sample data to obtain the finished test case. The invention realizes the automatic generation of the test cases, and the test cases cover 100% without omission.
Description
Technical Field
The invention relates to the technical field of rail transit, in particular to a testing technology of a rail transit signal system.
Background
The rail transit signal system is an important component and a key control system of a rail transit engineering construction project, is a brain and a center of the rail transit system, and plays a key role in the safety and reliability of vehicle-mounted and trackside systems in the operation process. With the development of signal systems, the complexity of the system is higher and higher, and the relationship between safety function modules tends to be complex, so that higher and higher requirements are put on the integrity and the effectiveness of the test of the system.
The current test for the rail transit signal system has the following problems:
(1) the test cases under normal and abnormal conditions are designed according to the understanding of the tester on the requirements and considering a certain fault or abnormal scene or path by depending on the experience of the tester and the understanding of the system, but because of the subjectivity, the design process has certain randomness, the omission and even the error of the test cases can be caused, and the omission and the error can bring potential safety hazards to the operation of a signal system. Therefore, the existing method depends on manual generation of test cases, and has the problems of low efficiency, poor accuracy and long time consumption;
(2) based on a certain algorithm or theory, a requirement is abstracted into limited elements, and then the elements are covered by using certain algorithms, so that a test case is generated, but the method generally has two types of problems, firstly, the test case cannot automatically generate test data, only abstract test specifications can be obtained, and the test case is not a specific test case, so the test case cannot be directly used for testing a system, secondly, the test case considers the abstracted scene in the requirement, but still has the possibility of omission for some functional points and fault points which are vital to safety, so that the completeness of the test cannot be ensured; thirdly, the accuracy of an expected result of the test case generated by the algorithm in the existing stage is not high, so that the generated case is an error case and cannot be used for system test;
(3) the existing automatic test case generation algorithm does not consider the actual scene situation, so that cases are not screened, a large number of invalid cases can be generated, a large number of resources and time can be wasted by directly testing, and the production efficiency is reduced.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide an automatic test case generation method for a rail transit signal system, and solve the problem that the case result is difficult to predict by the automatic case generation method at present; the problem that a large number of invalid cases are obtained without screening through the automatic generation cases at present is solved; the problem that a large number of data samples are needed to be used as model training samples for building a prediction model of the current case automatic generation algorithm is solved.
In order to solve the technical problems, the invention adopts the following technical scheme:
the rail transit signal system automatic test case generation method comprises the following steps:
step S1: analyzing a case scene, analyzing all function points through UML modeling according to the requirements of each level of a signal system, and listing all fault points according to the characteristics of hardware and software of the signal system;
step S2: carrying out artificial depth analysis on the use case scene, extracting specific function influence points of the scene, and extracting specific influence factors of the scene by combining the influence points of the scene analysis in the step S1;
step S3: orderly combining the influence factors generated in the step S2 through a CT algorithm, and specifying a combination coverage criterion, thereby automatically generating test data covering all scenes and all functional points and fault points;
step S4: automatically screening and eliminating the test data generated in the step S3, and removing test data which cannot be realized in actual line data;
step S5: selecting part of the test data generated in the step S4, and manually analyzing the result to obtain a data set with a label;
modeling and analyzing the obtained data set by using an LSSVM algorithm to obtain an LSSVM model;
and performing expected result judgment and identification on the unknown samples by using the obtained LSSVM model, thus obtaining an expected result of each sample data, and combining the expected result with the corresponding sample data to obtain the finished test case.
Preferably, the requirements of each hierarchy include system requirements, subsystem requirements and software requirements.
Preferably, the fault point list is continuously supplemented and optimized through the PDCA process.
Preferably, the test data that cannot be realized in the actual line data includes test steps inconsistent with reality, faults and functional conflicts with a real scene.
Preferably, the modeling analysis of the obtained data set by using the LSSVM algorithm comprises the following steps:
(1) dividing samples by using a KS algorithm;
(2) optimizing parameters by a grid search algorithm;
(3) and establishing an LSSVM model.
The technical scheme adopted by the invention is as follows:
(1) functional points obtained through modeling analysis are combined with fault points which are increased continuously through summary improvement, and due to the fact that PDCA flow is adopted for continuous improvement, omission and errors caused by randomness of manual design cases are made up, and solidification and deletion caused by simple abstract modeling are made up. The method can solve the problems of low efficiency of test cases, poor accuracy of generated cases, omission of generated cases and incapability of realizing full coverage.
(2) The method has the advantages that modeling identification is carried out through the CT-LSSVM algorithm, the prediction of unknown data sample results can be accurately realized, and the problem that the use case results are difficult to predict by the current method for automatically generating use cases, so that the generated use cases are incomplete or the error rate of the generated use cases is high is solved.
(3) By automatically screening and eliminating the cases of the CT algorithm, the problem that a large number of invalid cases are obtained without screening through automatically generated cases at present is solved.
(4) The prediction analysis of the full sample data set is realized by selecting partial samples to carry out modeling by using the LSSVM, and the problem that a large number of data samples are required to be used as model training samples for establishing the prediction model of the current case automatic generation algorithm is solved.
Therefore, the invention has the following beneficial effects:
(1) the automatic generation of the test cases is realized, the test cases cover 100 percent, and no leakage exists.
(2) The expected result accuracy of the test case generated by the CT-LSSVM method can reach more than 97%, and the actual test requirements are met.
(3) By analyzing the actual test scene of the track signal system, cases which do not accord with logic are removed, and invalid test cases are reduced.
(4) The LSSVM method can utilize a small sample to perform modeling analysis, and utilizes a test data sample to perform modeling, so that the model accuracy rate reaches 96% -99%.
The following detailed description will explain the present invention and its advantages.
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The invention is further described with reference to the accompanying drawings and the detailed description below:
FIG. 1 is a flow diagram of test case generation.
Fig. 2 is a flow chart of LSSVM model establishment.
Detailed Description
The technical solutions of the embodiments of the present invention are explained and illustrated below, but the following embodiments are only preferred embodiments of the present invention, and not all of them. Based on the embodiments in the implementation, other embodiments obtained by those skilled in the art without any creative effort belong to the protection scope of the present invention.
Referring to fig. 1, the invention provides a technical scheme of a method for generating a Test case of a rail transit signal system, and provides a method for automatically generating a Test case by using a Combined Test (CT) method in combination with a Least Square Support Vector Machine (LSSVM) method of a machine learning algorithm for a rail transit signal system. The method comprises the steps of using a CT method to generate and screen use cases, and using an LSSVM to predict use case results.
The automatic test case generation method based on the rail transit signal system mainly comprises the following steps: analyzing a case scene, extracting influence factors, generating a sample set by using a CT algorithm, performing modeling analysis by using an LSSVM algorithm, and identifying unknown samples by using a model.
The method comprises the following specific steps:
(1) analyzing all function points through UML modeling according to the requirements (including system requirements, subsystem requirements and software requirements) of each level of a signal system; all fault points are listed by manual analysis according to the characteristics of hardware and software of the signal system.
As can be understood by those skilled in the art, the functional points refer to main step points in the case scenario implementation process, and are also points needing testing and attention. The fault points are faults which are easy to occur when software and hardware run in the scene test process and faults which need to be artificially injected for meeting the test requirements of the use case scene.
The fault point list is continuously supplemented and optimized through a PDCA flow in the project delivery process of a company and is used as company information assets to be shared in the test process of each project, and the improvement points found in a certain project can be immediately supplemented to the fault point list, so that the list tends to be more and more complete.
(2) And (4) performing artificial depth analysis on the use case scene, and extracting specific influence factors of the scene, namely the function points and the fault points, by combining the function points and the fault points which are analyzed in the step S1 scene. As can be understood by those skilled in the art, the specific influence factors of the scene depend on the analysis of the service of the rail transit signal system by the qualified service personnel.
(3) And (3) orderly combining the influencing factors generated in the step (2) through a CT algorithm, and specifying a combination coverage criterion, thereby automatically generating test data which covers all scenes and all functional points and fault points.
The conventional combination criterion adopts Pairwise algorithm, the Pairwise algorithm can realize multi-factor combination, such as two-factor combination, three-factor combination and the like, and when all factors are combined, full coverage can be realized.
(4) And (4) automatically screening and eliminating the test data generated in the step (3) and removing test data which cannot be realized in actual line data. The screening and elimination criteria for test data were: and eliminating factor combination data which do not accord with objective facts and business logic, such as testing step not according with reality, faults and conflict between functions and a real scene.
(5) And 4, selecting part of the test data generated in the step 4, and manually analyzing the result to obtain a data set with a label. The data selection principle is to cover all expected results, and manually analyze the selected data to obtain expected results corresponding to the data, namely labels corresponding to the data.
And modeling and analyzing the obtained data set, and judging and identifying an expected result of an unknown sample by using the obtained LSSVM model, so that an expected result of each sample data can be obtained, and the expected result and the corresponding sample data are combined to obtain a finished test case.
Referring to fig. 2, the modeling analysis of the obtained data set by using the LSSVM algorithm includes the following steps:
(1) dividing samples by using a KS algorithm;
(2) optimizing parameters by a grid search algorithm;
(3) and establishing an LSSVM model.
Example 1: automatic generation of multi-vehicle operation case of FAO system
Step 1: scene analysis
When a track signal system is tested, firstly, a designated train operation scene is selected, then, deep analysis is carried out on the selected scene, test points are selected for observation, whether the test points meet designated expected results or not during train operation is determined, and the test points are used for testing corresponding function points and fault points.
In order to test the train operation basic control capability of the system, a system control scene during multi-train operation is selected. The scene requires that when more than 20 FAO mode trains on the system run on the main line and the trains are at any positions, equipment redundancy operation, operation control operation and fault simulation operation are respectively carried out on the system, and then whether the change of an observation point meets the expected result or not is confirmed.
Step 2: influencing factor extraction
The method comprises the steps that influence factors of a scene need to be extracted before combination and generation of use cases are carried out, in the scene, firstly, the position of a train belongs to one of the influence factors, then equipment redundancy operation is carried out on the system, equipment operations such as a central automatic train monitoring system ATS, a computer interlocking system CI, a vehicle-mounted control system CC, a zone control system ZC and a redundancy network RN are carried out, car buckling, jump stopping and temporary speed limiting control are carried out in operation control, and finally whether a platform emergency stop fault is simulated or not is carried out. Values of these factors are digitized, and eight states of train positions (a turnout area, a turnout-free area, a platform stop, a handover area, a turnaround area (before the station), a turnaround area (after the station), and a non-fixed area) are represented by numeral numbers "1 to" 8 ", respectively.
And step 3: generating a sample data set and screening
After the use case is generated, the generated use case needs to be analyzed to obtain an expected result. 362 use cases generated by the combination of the five factors are used as a sample set for LSSVM model establishment and verification. 362 samples are first classified according to expected results, and the attribute labels of the 7 types of samples are labeled with numbers "1-7" in sequence, so that a labeled use case set is obtained.
And 4, step 4: LSSVM model establishment and analysis
(1) Partitioning samples
And (3) carrying out modeling analysis after carrying out result analysis on 439 use case samples obtained by combining use cases generated by the five-factor combination and increasing the 1 st, 2 nd, 3 rd and 6 th use cases, dividing the use case sets into correction sets and test sets according to the proportion of 3:2, wherein the correction sets are used for establishing the model. The division results in 263 correction set samples and 176 test set samples.
(2) Parameter optimization by grid search algorithm
The parameters of the LSSVM algorithm are optimized through a grid search algorithm, wherein C is 11.31, g is 0.06, and the accuracy of the correction set of the model is 96.58%.
(3) LSSVM model building
The obtained model is applied to the test of the test set samples, 4 samples in the 176 samples in the test set are wrongly classified, and the identification accuracy rate is 97.73%.
(4) Prediction of unknown samples
Further applicable to twelve factor combinations and other factor combinations.
While the invention has been described with reference to specific embodiments, it will be understood by those skilled in the art that the invention is not limited thereto, and may be embodied in other forms without departing from the spirit or essential characteristics thereof. Any modification which does not depart from the functional and structural principles of the present invention is intended to be included within the scope of the claims.
Claims (5)
1. The rail transit signal system automatic test case generation method is characterized by comprising the following steps:
step S1: analyzing a case scene, analyzing all function points through UML modeling according to the requirements of each level of a signal system, and listing all fault points according to the characteristics of hardware and software of the signal system;
step S2: performing artificial depth analysis on the use case scene, and extracting specific influence factors of the scene, namely the function points and the fault points, by combining the function points and the fault points which are analyzed in the step S1;
step S3: orderly combining the influence factors generated in the step S2 through a CT algorithm, and specifying a combined coverage criterion, thereby automatically generating test data covering all scenes and all functional points and fault points;
step S4: automatically screening and eliminating the test data generated in the step S3, and removing test data which cannot be realized in actual line data;
step S5: selecting part of the test data generated in the step S4, and manually analyzing the result to obtain a data set with a label;
modeling and analyzing the obtained data set by using an LSSVM algorithm to obtain an LSSVM model;
and performing expected result judgment and identification on the unknown samples by using the obtained LSSVM model, thus obtaining an expected result of each sample data, and combining the expected result with the corresponding sample data to obtain the test case.
2. The rail transit signal system automated test case generation method of claim 1, characterized in that: the requirements of each level comprise system requirements, subsystem requirements and software requirements.
3. The rail transit signal system automated test case generation method of claim 1, characterized in that: and the fault point list is continuously supplemented and optimized through a PDCA process.
4. The rail transit signal system automated test case generation method of claim 1, characterized in that: test data that cannot be realized in actual line data includes test steps that do not conform to reality, faults, and functional and real scenario conflicts.
5. The rail transit signal system automated test case generation method of claim 1, characterized in that: the modeling analysis of the obtained data set by using the LSSVM algorithm comprises the following steps:
(1) dividing samples by using a KS algorithm;
(2) optimizing parameters by a grid search algorithm;
(3) and establishing an LSSVM model.
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Application publication date: 20201127 |