CN104899141A - Test case selecting and expanding method facing network application system - Google Patents
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Abstract
The invention relates to a test case selecting and expanding method facing a network application system. A novel test case set with a relatively high fault detecting efficiency is finally generated by virtue of a TTCN-3 testing standard by the following steps: analyzing the test case set; carrying out elimination of redundancy; and training the test data by virtue of a machine learning method and the like. By the aid of the method provided by the invention, the problems that the test cases have a lot of redundancy, the test cases in a regression test are multiplexed, the test behaviors in the test cases are compactly coupled with the test data and the test cases have a scarce capacity of enhancing the performance of the network application system are solved.
Description
Technical field
The present invention relates to net application technology field, be specifically related to a kind of test cases selection and extending method of network-oriented application system.
Background technology
Along with the development of network application system, there is the research that a large amount of being directed to promotes the measuring technology of the aspect performances such as it is functional, stability, reliability both at home and abroad.Have and automatically generate testing script technique by recording/replaying technology, and model-driven testing technology.Wherein model-driven testing technology becomes the main aspect of research gradually, comprise: adopt BPEL specification to describe business process model, by expanding the test case meta-model of U2TP specification and TTCN-3 language definition, in order to support the conversion of two models, define the business process meta-model describing service interaction and steering logic based on U2TP activity diagram.Adopt Eclipse platform development operation flow test modeling platform, support that tester is to the visual modeling of operation flow and test case, realizes the automatic conversion of business process model to test case model, achieves the robotization generation of test script.
Except based on except UTP, use the extended capability extensible SQL of UML also can generating test use case.C.Crichton, A.Cavarra and J.Davie adopt in this way exactly.They create two kinds of expansions, and one is the testing attribute being described system under test (SUT) by extension class figure, object diagram and constitutional diagram.Another expansion be by object diagram and constitutional diagram be used for test target is described.
The people such as F.Bouquet propose model-based testing method, the method based on uml diagram, as class figure, object diagram and constitutional diagram, with the automatic generating test use case of OCL expression formula.
A principal feature in above model-driven testing method is based on system model, by definition test design setting model language, carries out Design of Test System, and by certain test philosophy, generating test use case.The subject matter of existence common in the middle of these methods has: the test case of generation has diversity, fault discovery for tested system lacks specific aim, the test case redundancy of the Space Explosion problem generation of test use cases, the problems such as test data is insufficient.
Summary of the invention
In view of above-mentioned analysis, the present invention aims to provide a kind of test cases selection and extending method of network-oriented application system, in order to solve the test case generated in prior art, there is diversity, there is redundancy in test use cases, fault discovery for tested system lacks specific aim, and the problem such as test data is insufficient.
Object of the present invention is mainly achieved through the following technical solutions:
The test cases selection of network-oriented application system and an extending method, the method comprises the steps:
Step 1, from database, read the test use cases data of tested network application system;
Step 2, the test use cases data in step 1 are sent in resolution server and resolve, generate test macro model, and be transferred to display device and present with patterned form; Wherein, test macro model comprises: behavioral test model, test data model and test configurations model;
Step 3, the behavioral test model of resolving in step 2 in the test macro model of generation is sent in de-redundancy server and carries out de-redundancy process;
Step 4, the test data model of resolving in step 2 in the test macro model of generation is sent in training server, first carries out test data acquisition, then utilize machine learning method to train test data, thus generate new test data;
Step 5, the new test data will obtained in the behavioral test model after the de-redundancy obtained in step 3 and step 4, the test configurations model of the test macro model of integrating step 2, be sent to together in new test use cases generation server, and then adopt TTCN-3 testing standard to generate the new test use cases with higher fault finding usefulness.
Further, the described test use cases described in step 1 is test script.
Further, the resolving described in step 2 realizes mainly through following three steps:
(1) data are extracted: by the static or dynamic metadata analyzing acquisition source file;
(2) model is built: the metadata of extraction carried out classifying and storing, use predefined model to recombinate to metadata, the structure of implementation model;
(3) presenting information: after obtaining the model built, by meta-model technology, abstract process is carried out to model information, and represent with patterned form.
Further, step 3 is specially: according to the test system and test behavior model parsed, and adopts calculating formula of similarity, the similarity respectively between contrastive test behavior; Adopt genetic search algorithm, search is optimized to whole behavioral test model, thus draws the minimum behavioral test model set that can reach test target.
Further, the computing formula of described similarity is as follows:
SimilarityFunction(i,j)＝nit/avg(|i|,|j|)
Wherein, nit represents the identical operand between two behavioral tests, and avg (| i|, | j|) represents the average operation number length of two test cases.
Further, utilize machine learning method to carry out training to test data to be specially described in step 4: after obtaining test data, by the test thinking of simulation test personnel, original test data is trained, learn and adjusts, thus generate new test data.
Further, step 5 is specially: by setting up the mapping between test macro model and TTCN-3 test macro model, realize the conversion of test macro model to TTCN-3 abstract test case, and then generate TTCN-3 can implementation of test cases script, by TTCN-3 test execution platform, realize the automatic test to network application system.
Beneficial effect of the present invention is as follows:
The invention provides a kind of test cases selection and extending method of network-oriented application system, by resolving test use cases, de-redundancy process, utilize machine learning method to train test data and adopt TTCN-3 testing standard generate there is the technological means such as the new test use cases of higher fault finding usefulness, solve following technical matters:
1, the problem that test case also exists bulk redundancy is solved;
2, the problem of the Reuse of Test Cases in the middle of regression test is solved;
3, behavioral test and the tightly coupled problem of test data in test case is solved;
4, test case is solved for the problem promoting network application system performance capability deficiency.
Other features and advantages of the present invention will be set forth in the following description, and, becoming apparent from instructions of part, or understand by implementing the present invention.Object of the present invention and other advantages realize by structure specifically noted in write instructions, claims and accompanying drawing and obtain.
Accompanying drawing explanation
Accompanying drawing only for illustrating the object of specific embodiment, and does not think limitation of the present invention, and in whole accompanying drawing, identical reference symbol represents identical parts.
Fig. 1 is method flow diagram of the present invention;
Fig. 2 is test data model schematic of the present invention;
Fig. 3 is test configurations model schematic of the present invention;
Fig. 4 is behavioral test model schematic of the present invention;
Fig. 5 is test data model analyzing process flow diagram of the present invention;
Fig. 6 is that test data self study of the present invention generates Optimizing Flow figure.
Embodiment
Specifically describe the preferred embodiments of the present invention below in conjunction with accompanying drawing, wherein, accompanying drawing forms the application's part, and together with embodiments of the present invention for explaining principle of the present invention.
The test cases selection of network-oriented application system and an extending method, the method comprises the steps:
Step 1, from database, read the test use cases of tested network application system.
Wherein, described test use cases is test script.Test script comprises Dynamic System and performs sequence, operating parameter and system configuration information etc.
Step 2, the test use cases in step 1 is sent in resolution server and resolves, generate test macro model, and present with patterned form; Wherein, test macro model comprises: behavioral test model, test data model and test configurations model.
According to test correlation theory and network application system test feature, set up test macro model, obtain test key message for concentrating from the test case of input, and modeling is carried out to test macro.
(1) test data model
Test data model defines one group of concept in order to describe test data, mainly comprises the data structure definition of the various complexity such as data pool (DataPool), Data Placement (DataPartition), data instance (DataInstance).
In test data model, introduce the basic data type (SimpleType) of UML, further comprises enumeration type (Enum) and record type (Record), and ordered set (RecordOf) and unordered set (SetOf).The corresponding data type of a territory (field) of record type, can be arranged by association class (Attribute) and ignore this territory (omit) and optional (optional) two attributes.The example of data type (DataType) is data instance (DataInstance), defines the example of a record type or simple data type or set.Data pool (DataPool) comprises multiple data instance, and data pool has the equivalence class partition (DataPartition) of data template.Specifically see Fig. 2.
(2) test configurations model
Test configurations model is for describing structure and the configuration of test suite and system under test (SUT) in testing.Test configurations model is the description to test macro static state composition structure.Test configurations model mainly comprises: the connection (TestInterfaceConnection) between test suite (TestComponent), test interface (TestInterface), test macro, system under test (SUT) (SUT).Add the data type that test interface type (TestInterfaceType) defines test interface input and output permission, a test interface type can be quoted by multiple test interface.Can define timer (Timer) in test suite, timer can be cited at test scene (TestScenario).Specifically see Fig. 3.
(3) behavioral test model
Behavioral test model is for being described in the various actions in test case.The definition of behavioral test model comprises timer action (Start Timer Action and Stop Timer Action), massage stimulus (StimuliAction), message response (ResponseEvaluateAction) and test result classification (Verdict) etc., also comprise do action (WhileAction and ForAction) and if action (IfAction and ForAction) and branch operation (AltAction), these elements are inherited from movable block (ActionBlock), movable block is inherited from abstract action (Action).Test case (TestCase) inherits self-test scene (TestScenario).Wherein define the input data of needs and intended response data type and name information in massage stimulus and message response.Specifically see Fig. 4.
By test macro model, field is one by one carried out to test case and resolves, from test case, extract test model, be stored as test data model respectively, behavioral test model and test configurations model.
Described resolving can be divided into three parts, and Part I identifies associated components, and Part II is analyzed the dependence between component, and Part III is the abstract expression form setting up system same level or more high-level.Realize mainly through following three steps:
(1) data are extracted: data are now undressed, by the static or dynamic metadata analyzing acquisition source file; Wherein said meta-data pack is contained in Dynamic System and performs in the data such as sequence, operating parameter and system configuration information.
(2) model is built: the metadata of extraction carried out classifying and storing, use predefined model metadata to be recombinated, the structure of implementation model;
(3) presenting information: after obtaining the model built, by meta-model technology, abstract process is carried out to model information, and represent with patterned form.Meta-model is the one " abstract language " defined in order to descriptive model, is abstract further to model.Meta-model technology is by the basic comprising element to things, and the relation between element defines, and specifies that it indicates to reach a kind of technology of things being carried out to modeling.
Step 3, the behavioral test model of resolving in step 2 in the test macro model of generation is sent in de-redundancy server and carries out de-redundancy process.
Further, according to the test system and test behavior model parsed, adopt calculating formula of similarity, the similarity respectively between contrastive test behavior; Adopt genetic search algorithm, search is optimized to whole behavioral test model, thus draws the minimum behavioral test model set that can reach test target.
The computing formula of described similarity is as follows:
SimilarityFunction(i,j)＝nit/avg(|i|,|j|)
Wherein, nit represents the identical operand between two behavioral tests, and avg (| i|, | j|) represents the average operation number length of two test cases.
By step 3, eliminate the redundancy testing use-case in the middle of test use cases, decrease the test cost produced because of redundancy testing, greatly saved testing cost.
Step 4, the test data model of resolving in step 2 in the test macro model of generation is sent in training server and carries out test data acquisition, and utilize machine learning method to train test data, thus generate new test data.
By resolving test data model file, obtain dtd-data type definition information, thus set up the tables of data of corresponding types before data store.As shown in Figure 5, the concrete step of test data model analyzing is as follows:
First, judge whether selected test macro model is test data model, if not, then terminate resolving.
Secondly, if, then locking and acquiring domain model file, and circulation obtains and judges the definition of each test data type: (1) is if type definition is basic data type, then do not preserve its relevant information, because pre-defined all basic data types in the tables of data set up; (2) if type definition is complex data type, then according to the basic data type that data structure and the type of its definition comprise, set up Service Data Object model, preserve this dtd-data type definition information.The complex data type of each definition is preserved in circulation like this, until all data types are all preserved, for setting up tables of data in a database with the form of Service Data Object model.
After resolving test data model and obtain the definition information of data type, test data is built up tables of data according to dtd-data type definition, and store in a database.Certain data type defined in the corresponding test data model of each tables of data set up in a database.In order to process Various types of data in a uniform manner, the data agent service (Data Mediator Service) that the present invention is based on service object connects and the service of operating database SDO data agent provides access to data source.Data agent service creates data plot by reading data from back-end data source, can also upgrade data source based on the change done data plot.Data agent service can occur with size in a variety of manners, but also the XML intermediary that XML data source is read and write, the relation intermediary that the data source based on JDBC is read and write, solid model according to application definition can be comprised, or even accept XML and inquire about and the intermediary reading and writing various XML and non-XML data source.The data plot framework that data agent service support disconnects.
A data plot for loading a data plot from a data-carrier store, or is saved in a data-carrier store by data access service (Data Access Service, DAS).Function as an XML DAS loads a data plot from XML file or is saved in XML file, and a JDBC DAS loads or preserves data plot from relational database.
Described training step is specially: after obtaining test data, by the test thinking of simulation test personnel, trains, learns and adjust original test data, thus generate new test data.As shown in Figure 6.This training step comprises two aspects, corresponds respectively to two class behaviors of manual testing:
One, meet the Test data generation algorithm of given cover up rule, corresponding manual testing is according to the test data design behavior of established rule.
Two, according to testing feedback to the study of Test coverage rule and adjustment, thus the test case made new advances is produced, corresponding to manual testing according to testing effect to the adjustment behavior of Test data generation rule.
By step 4, the robotization achieving test data generates, and decreases Test data generation cost, and is trained and learning method by high-caliber data, generate the data test of fault finding higher level, improve testing efficiency.
The new test data obtained in behavioral test model after step 5, de-redundancy that step 3 is obtained and step 4, the test configurations model of the test macro model of integrating step 2, be sent in the lump in new test use cases generation server, and then adopt TTCN-3 testing standard to generate the new test use cases with higher fault finding usefulness.
By setting up the mapping between test macro model and TTCN-3 test macro model, realize the conversion of test macro model to TTCN-3 abstract test case, and then generate TTCN-3 can implementation of test cases script, by TTCN-3 test execution platform, realize the automatic test to network application system.
By step 5, achieve the expansion to test use cases, generate executable test use cases, achieve the robotization of test, reach the object of saving testing cost.
In sum, embodiments provide a kind of test cases selection and extending method of network-oriented application system, by resolving test use cases, de-redundancy process, utilizing machine learning method to carry out the step such as training to test data, finally adopting TTCN-3 testing standard to generate the new test use cases with higher fault finding usefulness.Method of the present invention solves test case and to there is in the problem of the Reuse of Test Cases in the middle of the problem of bulk redundancy, regression test, test case behavioral test and the tightly coupled problem of test data and test case for the problem promoting network application system performance capability deficiency.
It will be understood by those skilled in the art that all or part of flow process realizing above-described embodiment method, the hardware that can carry out instruction relevant by computer program has come, and described program can be stored in computer-readable recording medium.Wherein, described computer-readable recording medium is disk, CD, read-only store-memory body or random store-memory body etc.
The above; be only the present invention's preferably embodiment, but protection scope of the present invention is not limited thereto, is anyly familiar with those skilled in the art in the technical scope that the present invention discloses; the change that can expect easily or replacement, all should be encompassed within protection scope of the present invention.
Claims (7)
1. the test cases selection of network-oriented application system and an extending method, it is characterized in that, the method comprises the steps:
Step 1, from database, read the test use cases data of tested network application system;
Step 2, the test use cases data in step 1 are sent in resolution server and resolve, generate test macro model, and be transferred to display device and present with patterned form; Wherein, test macro model comprises: behavioral test model, test data model and test configurations model;
Step 3, the behavioral test model of resolving in step 2 in the test macro model of generation is sent in de-redundancy server and carries out de-redundancy process;
Step 4, the test data model of resolving in step 2 in the test macro model of generation is sent in training server, first carries out test data acquisition, then utilize machine learning method to train test data, thus generate new test data;
Step 5, the new test data will obtained in the behavioral test model after the de-redundancy obtained in step 3 and step 4, the test configurations model of the test macro model of integrating step 2, be sent to together in new test use cases generation server, and then adopt TTCN-3 testing standard to generate the new test use cases with higher fault finding usefulness.
2. the method for claim 1, is characterized in that, the described test use cases described in step 1 is test script.
3. the method for claim 1, is characterized in that, the resolving described in step 2 realizes mainly through following three steps:
(1) data are extracted: by the static or dynamic metadata analyzing acquisition source file;
(2) model is built: the metadata of extraction carried out classifying and storing, use predefined model to recombinate to metadata, the structure of implementation model;
(3) presenting information: after obtaining the model built, by meta-model technology, abstract process is carried out to model information, and represent with patterned form.
4. the method for claim 1, is characterized in that, step 3 is specially: according to the test system and test behavior model parsed, and adopts calculating formula of similarity, the similarity respectively between contrastive test behavior; Adopt genetic search algorithm, search is optimized to whole behavioral test model, thus draws the minimum behavioral test model set that can reach test target.
5. method as claimed in claim 4, it is characterized in that, the computing formula of described similarity is as follows:
SimilarityFunction(i,j)＝nit/avg(|i|,|j|)
Wherein, nit represents the identical operand between two behavioral tests, and avg (| i|, | j|) represents the average operation number length of two test cases.
6. the method according to any one of claim 1-5, it is characterized in that, utilize machine learning method to carry out training to test data to be specially described in step 4: after obtaining test data, by the test thinking of simulation test personnel, original test data is trained, learn and adjusts, thus generate new test data.
7. the method according to any one of claim 1-6, it is characterized in that, step 5 is specially: by setting up the mapping between test macro model and TTCN-3 test macro model, realize the conversion of test macro model to TTCN-3 abstract test case, and then generate TTCN-3 can implementation of test cases script, by TTCN-3 test execution platform, realize the automatic test to network application system.
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