CN112445702A - Automatic testing method and system based on ant colony algorithm - Google Patents
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Abstract
The invention discloses an automatic testing method and system based on an ant colony algorithm, wherein the method comprises the following steps: selecting core parameters for a preset test scene to generate a full Cartesian product combination; forming an initial set of test cases according to the full combination of Cartesian products; adopting a parameter-by-parameter expansion algorithm based on an ant colony algorithm, adding the remaining parameters except the core parameters one by one, and respectively performing transverse expansion and longitudinal expansion to form an expansion test case set; acquiring parameter constraint conditions, traversing the extended test case set, and deleting the test cases containing the parameter constraint conditions to generate a final test case set; and generating an automatic test task according to the final test case set to complete the test. By the technical scheme, the total number of the test cases is greatly reduced under the condition of covering the full-scene test cases, the generation quality of the test case set is improved, the test efficiency is improved, and the development test period is shortened.
Description
Technical Field
The invention relates to the technical field of testing, in particular to an automatic testing method based on an ant colony algorithm and an automatic testing system based on the ant colony algorithm.
Background
With the development of communication technology, the telecommunication industry has also developed dramatically in charging services. Therefore, higher requirements are provided for a service support system, the architecture is continuously upgraded, and the system is continuously and flexibly expanded. The support system intellectualization and operation automation become the inevitable result of the future development trend and system evolution. In the 5G era, the service of the telecommunication system is more complex, and the requirements on the test quality and efficiency are higher and higher in the service promotion process of new requirements.
In the existing testing technology, the scene coverage rate is not complete, the number of testing scenes is too large, the testing is inaccurate, and the testing time is too long.
Disclosure of Invention
Aiming at the problems, the invention provides an automatic test method and system based on an ant colony-Order (In-Parameter-Order) algorithm, which reduces the total number of test cases under the condition of covering a full-scene test case by optimizing a test scene core Parameter and combining Cartesian product operation and an improved ant colony algorithm, improves the generation quality of a test case set, greatly improves the test efficiency and further shortens the development test period.
In order to achieve the above object, the present invention provides an automated testing method based on ant colony algorithm, comprising: selecting core parameters for a preset test scene to generate a Cartesian product total combination of the core parameters; forming an initial set of test cases of the core parameters according to the full combination of Cartesian products; adding the residual parameters except the core parameters into the initial test case set one by adopting a parameter-by-parameter expansion algorithm based on an ant colony algorithm to perform transverse expansion and longitudinal expansion respectively to form an expanded test case set; acquiring parameter constraint conditions, traversing the extended test case set, and deleting the test cases containing the parameter constraint conditions to generate a final test case set; and generating an automatic test task according to the final test case set to complete the test.
In the foregoing technical solution, preferably, the forming of the extended test case set by using a parameter-by-parameter extension algorithm based on an ant colony algorithm and adding the remaining parameters except the core parameter to the initial test case set one by one for performing horizontal extension and vertical extension respectively includes:
determining the remaining parameters except the core parameters under the test scene; adding the residual parameters item by item along the transverse direction aiming at the full Cartesian product combination generated by the core parameters, and performing transverse expansion to generate a transverse test case set; and after the transverse expansion is finished, longitudinally expanding the uncovered residual parameter combination form to the transverse test case set until all the parameter combinations are covered to generate the expanded test case set.
In the foregoing technical solution, preferably, the obtaining of the parameter constraint condition and the traversing of the extended test case set to delete the test case including the parameter constraint condition to generate the final test case set specifically include: acquiring parameters with mutual exclusion constraint conditions according to the test scene, and determining a test case containing the parameters of the mutual exclusion constraint conditions; and traversing and searching in the extended test case set, deleting the test cases containing the mutual exclusion constraint condition parameters, and generating a final test case set.
The invention also provides an automatic test system based on the ant colony algorithm, which applies the automatic test method based on the ant colony algorithm in any one of the technical schemes and comprises the following steps: the Cartesian product module is used for generating a Cartesian product complete combination for the core parameters selected from the preset test scene; the initial case module is used for forming a test case initial set of the core parameters according to the full Cartesian product combination; the extended case module is used for adding the residual parameters except the core parameters into the initial test case set one by one to perform transverse extension and longitudinal extension respectively by adopting a parameter-by-parameter extended algorithm based on an ant colony algorithm to form an extended test case set; the final case module is used for acquiring parameter constraint conditions, traversing the extended test case set and deleting the test cases containing the parameter constraint conditions so as to generate a final test case set; and the automatic test module is used for generating an automatic test task according to the final test case set so as to complete the test.
In the foregoing technical solution, preferably, the extension case module is specifically configured to: determining the remaining parameters except the core parameters under the test scene; adding the residual parameters item by item along the transverse direction aiming at the full Cartesian product combination generated by the core parameters, and performing transverse expansion to generate a transverse test case set; and after the transverse expansion is finished, longitudinally expanding the uncovered residual parameter combination form to the transverse test case set until all the parameter combinations are covered to generate the expanded test case set.
In the above technical solution, preferably, the end-use-case module is specifically configured to: acquiring parameters with mutual exclusion constraint conditions according to the test scene, and determining a test case containing the parameters of the mutual exclusion constraint conditions; and traversing and searching in the extended test case set, deleting the test cases containing the mutual exclusion constraint condition parameters, and generating a final test case set.
Compared with the prior art, the invention has the beneficial effects that: by means of optimized test scene core parameters, combined with Cartesian product operation and an improved ant colony algorithm, the total number of test cases is greatly reduced under the condition of covering a full-scene test case, the generation quality of a test case set is improved, the test efficiency is improved, and therefore the development test period is shortened.
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Fig. 1 is a schematic flow chart of an automated testing method based on an ant colony algorithm according to an embodiment of the present invention;
FIG. 2 is a comparison diagram of test efficiency before and after an improvement of the ant colony algorithm according to an embodiment of the present invention;
FIG. 3 is a test case quantity analysis diagram of an improved ant colony algorithm according to an embodiment of the disclosure;
fig. 4 is a block diagram of an automated testing system based on an ant colony algorithm according to an embodiment of the present invention.
In the drawings, the correspondence between each component and the reference numeral is:
11. the system comprises a Cartesian product module, 12 an initial case module, 13 an extended case module, 14 a final case module and 15 an automatic test module.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, 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 some, but not all, embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
The invention is described in further detail below with reference to the attached drawing figures:
as shown in fig. 1, the automated testing method based on the ant colony algorithm provided by the present invention includes: selecting core parameters for a preset test scene to generate a Cartesian product total combination of the core parameters; forming an initial set of test cases of the core parameters according to the full combination of Cartesian products; adopting a parameter-by-parameter expansion algorithm based on an ant colony algorithm, adding the residual parameters except the core parameters into the initial test case set one by one, and respectively performing transverse expansion and longitudinal expansion to form an expanded test case set; acquiring parameter constraint conditions, traversing the extended test case set, and deleting the test cases containing the parameter constraint conditions to generate a final test case set; and generating an automatic test task according to the final test case set to complete the test.
In the embodiment, by means of optimized test scenario core parameters, combined with Cartesian product operation and an improved ant colony algorithm, the total number of test cases is greatly reduced under the condition of covering the whole scenario test cases, the generation quality of the test case set is improved, the test efficiency is improved, and therefore the development test period is shortened.
Specifically, according to the multi-year statistical analysis of the telecommunication industry service, if there are 10 parameters related to a test requirement, more than 90% of errors are caused by the interaction of the parameters within 3 of the core, which are critical to the quality of the test case.
Secondly, for many systems or programs to be tested, the relationship between the use case parameters has an incidence relationship and a constraint condition. For example, a value of one parameter and a value of another parameter cannot occur simultaneously. In this case, some combinations of test case sets are meaningless and sometimes even ineffective for testing. Therefore, in the process of generating the test cases by using the ant colony algorithm, the dependency constraint relationship between the parameters of the use cases should be systematically considered.
Therefore, the generation of the initial set of test cases is optimized on the basis of the original ant colony algorithm, the initial parameter set is modified from the full Cartesian product combination of any two parameters in the original ant colony algorithm to the full Cartesian product combination of the core parameters, so that 100% of the full scene of the test cases of the core parameters is completely covered by improving the ant colony algorithm, and other related parameters are gradually added into the initial set of test cases according to the steps of horizontal extension and vertical extension of the ant colony algorithm to generate the extended test case set. Because the number of the core parameters is not more than 3 generally, compared with the original ant colony algorithm, the total number of the extended test case set is not increased remarkably, but the key case coverage rate of the extended test case set is greatly improved.
In the above embodiment, preferably, the obtaining of the parameter constraint condition, and the traversing the extended test case set to delete the test case containing the parameter constraint condition, so as to generate the final test case set specifically includes: acquiring parameters with mutual exclusion constraint conditions according to a test scene, and determining a test case containing the parameters of the mutual exclusion constraint conditions; and traversing and searching in the extended test case set, deleting the test cases containing the mutual exclusion constraint condition parameters, and generating a final test case set.
Specifically, after the extended test case set is generated, a parameter mutual exclusion condition constraint deletion step is added, the test case set is scanned globally, and redundant invalid test cases with constraint conditions such as mutual exclusion dependence and the like on values among parameters are deleted. By deleting the constraint condition parameter cases, the number of invalid test cases in the test case set is reduced, the test efficiency is improved, and the generation quality of the test case set is improved.
In the specific implementation process, the flow steps for improving the ant colony algorithm are as follows:
1) acquiring a core parameter list, wherein the number of core parameters is t;
2) generating a complete Cartesian product combination of t core parameters to obtain an initial set of test cases of the t core parameters;
3) adding other parameters one by one for transverse expansion and longitudinal expansion until all the parameters are added into the initial set of test cases;
4) generating an extended test case set;
5) acquiring a parameter constraint condition;
6) and traversing the extended test case set, deleting the test cases containing the constraint conditions, and generating a final test case set.
Through the improved ant colony algorithm, 100% full coverage of Cartesian product cases of core parameter combinations is realized, all parameters related to test cases are used in a test case generation algorithm, and dependence mutual exclusion constraint and the like among parameter conditions are considered.
Taking telecommunication service requirement test as an example, the test is carried out according to ant colony algorithms before and after improvement, so that the workload of requirement test needing manual repetition is greatly reduced, the requirement test efficiency is greatly improved, and the average required test time is reduced to within 69 minutes from 125 minutes of the previous full-combination test algorithm. As shown in fig. 2, the abscissa is the required number, and the ordinate is the average test service time (in minutes), and according to the test result, the improved ant colony algorithm is adopted, so that the test time is shortened, the development and test period is shortened, and when the test requirements are more, the improved ant colony algorithm has a greater advantage in the test efficiency, and the efficiency of pushing the required products to the market for commercial use is greatly improved.
Practice shows that the improved ant colony algorithm also greatly improves the coverage rate of test cases. The coverage rate of the key core test cases is guaranteed to be 100% before and after the improved ant colony algorithm is used, but after the improved ant colony algorithm is used, the total coverage rate of the test cases is improved from 90% of the previous core test cases to more than 99.9% of the total coverage rate of the test cases considering all relevant parameters, the quality of the test cases is greatly improved, and the required error fault rate is reduced by more than 90%.
By using the improved ant colony algorithm, under the condition that the number of the test case parameters is not more than 15, the number of the generated test case sets is within 1000, and the number of the generated test cases is shown in fig. 3.
In the foregoing embodiment, preferably, a parameter-by-parameter extension algorithm based on an ant colony algorithm is adopted, and the remaining parameters except the core parameter are added to the initial test case set one by one to perform horizontal extension and vertical extension, respectively, so as to form an extended test case set specifically including:
determining the residual parameters except the core parameters in the test scene; adding the rest parameters item by item along the transverse direction aiming at the full Cartesian product combination generated by the core parameters, and performing transverse expansion to generate a transverse test case set; and after the transverse expansion is finished, longitudinally expanding the uncovered residual parameter combination form to a transverse test case set until all the parameter combinations are covered to generate an expanded test case set.
Specifically, lateral expansion refers to: after the initial combination set is determined, a new parameter value is added into the initial set of test cases horizontally, so that the purpose of newly expanding the parameters to the initial set of test cases is achieved.
Longitudinal expansion means that: after the transverse expansion is completed, if other cases with parameter values combined pairwise can not be covered, the uncovered combination form is also added into the test case initial set.
Assuming that 3 input parameters X1, X2 and X3 are provided, and the parameters are respectively (V11, V12), (V21, V22), (V31, V32 and V33), firstly, the X1 and X2 parameters are combined to obtain an initial case set, and then transverse expansion and longitudinal expansion are carried out.
And (3) a transverse expansion step: after the 4 combined values of the first two parameters are obtained, the 3 values of the parameter X3 are respectively expanded by the column corresponding to the parameter X3, and since (V22, V31) are not covered, V31 is expanded to the four rows, as shown in the following table.
A longitudinal expansion step: and after the transverse expansion, expanding the supplementary channel test case set until all the pairwise combinations of all the parameters cover the test case set, wherein the pairwise combinations do not cover the parameter combinations in the test case set, so that a final test case set is generated, and the table is shown in the following table.
As shown in fig. 4, the present invention further provides an automated testing system based on ant colony algorithm, which applies the automated testing method based on ant colony algorithm according to any one of the above embodiments, including: the Cartesian product module 11 is used for generating a Cartesian product complete combination for the core parameters selected from the preset test scene; an initial case module 12, configured to form a test case initial set of core parameters according to a full cartesian product combination; the extended case module 13 is configured to add the remaining parameters except the core parameters to the initial test case set one by using a parameter-by-parameter extended algorithm based on an ant colony algorithm, and perform horizontal extension and vertical extension, respectively, to form an extended test case set; the final case module 14 is configured to obtain the parameter constraint conditions, and traverse the extended test case set to delete the test cases containing the parameter constraint conditions, so as to generate a final test case set; and the automatic test module 15 is used for generating an automatic test task according to the final test case set so as to complete the test.
In the foregoing embodiment, preferably, the extended use case module 13 is specifically configured to: determining the residual parameters except the core parameters in the test scene; adding the rest parameters item by item along the transverse direction aiming at the full Cartesian product combination generated by the core parameters, and performing transverse expansion to generate a transverse test case set; and after the transverse expansion is finished, longitudinally expanding the uncovered residual parameter combination form to a transverse test case set until all the parameter combinations are covered to generate an expanded test case set.
In the above embodiment, preferably, the end-use case module 14 is specifically configured to: acquiring parameters with mutual exclusion constraint conditions according to a test scene, and determining a test case containing the parameters of the mutual exclusion constraint conditions; and traversing and searching in the extended test case set, deleting the test cases containing the mutual exclusion constraint condition parameters, and generating a final test case set.
In the above embodiment, in the automatic test system based on the ant colony algorithm, the functions implemented by each component are consistent with the functions implemented by each step of the automatic test method based on the ant colony algorithm in the above embodiment, and are not described herein again.
The above is only a preferred embodiment of the present invention, and is not intended to limit the present invention, and various modifications and changes will occur to those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (6)
1. An automated testing method based on an ant colony algorithm is characterized by comprising the following steps:
selecting core parameters for a preset test scene to generate a Cartesian product total combination of the core parameters;
forming an initial set of test cases of the core parameters according to the full combination of Cartesian products;
adding the residual parameters except the core parameters into the initial test case set one by adopting a parameter-by-parameter expansion algorithm based on an ant colony algorithm to perform transverse expansion and longitudinal expansion respectively to form an expanded test case set;
acquiring parameter constraint conditions, traversing the extended test case set, and deleting the test cases containing the parameter constraint conditions to generate a final test case set;
and generating an automatic test task according to the final test case set to complete the test.
2. The ant colony algorithm-based automatic testing method according to claim 1, wherein the forming of the extended test case set specifically comprises, by using a parameter-by-parameter extension algorithm based on an ant colony algorithm, adding the remaining parameters except the core parameters to the initial test case set one by one, and performing horizontal extension and vertical extension, respectively:
determining the remaining parameters except the core parameters under the test scene;
adding the residual parameters item by item along the transverse direction aiming at the full Cartesian product combination generated by the core parameters, and performing transverse expansion to generate a transverse test case set;
and after the transverse expansion is finished, longitudinally expanding the uncovered residual parameter combination form to the transverse test case set until all the parameter combinations are covered to generate the expanded test case set.
3. The ant colony algorithm-based automated testing method according to claim 1, wherein the obtaining of the parameter constraint condition and the traversing of the extended test case set to delete the test cases containing the parameter constraint condition to generate the final test case set specifically comprises:
acquiring parameters with mutual exclusion constraint conditions according to the test scene, and determining a test case containing the parameters of the mutual exclusion constraint conditions;
and traversing and searching in the extended test case set, deleting the test cases containing the mutual exclusion constraint condition parameters, and generating a final test case set.
4. An automated test system based on ant colony algorithm, which applies the automated test method based on ant colony algorithm according to any one of claims 1 to 3, and is characterized by comprising the following steps:
the Cartesian product module is used for generating a Cartesian product complete combination for the core parameters selected from the preset test scene;
the initial case module is used for forming a test case initial set of the core parameters according to the full Cartesian product combination;
the extended case module is used for adding the residual parameters except the core parameters into the initial test case set one by one to perform transverse extension and longitudinal extension respectively by adopting a parameter-by-parameter extended algorithm based on an ant colony algorithm to form an extended test case set;
the final case module is used for acquiring parameter constraint conditions, traversing the extended test case set and deleting the test cases containing the parameter constraint conditions so as to generate a final test case set;
and the automatic test module is used for generating an automatic test task according to the final test case set so as to complete the test.
5. The ant colony algorithm-based automated testing system of claim 4, wherein the extended use case module is specifically configured to:
determining the remaining parameters except the core parameters under the test scene;
adding the residual parameters item by item along the transverse direction aiming at the full Cartesian product combination generated by the core parameters, and performing transverse expansion to generate a transverse test case set;
and after the transverse expansion is finished, longitudinally expanding the uncovered residual parameter combination form to the transverse test case set until all the parameter combinations are covered to generate the expanded test case set.
6. The ant colony algorithm-based automated testing system of claim 4, wherein the end-use case module is specifically configured to:
acquiring parameters with mutual exclusion constraint conditions according to the test scene, and determining a test case containing the parameters of the mutual exclusion constraint conditions;
and traversing and searching in the extended test case set, deleting the test cases containing the mutual exclusion constraint condition parameters, and generating a final test case set.
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