CN104461861A - EFSM model-based path testing data generation method - Google Patents
EFSM model-based path testing data generation method Download PDFInfo
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Abstract
The invention discloses an EFSM model-based path testing data generation method. The EFSM model-based path testing data generation method comprises the following steps that 1, symbolic execution and data flow dependence analysis are carried out on an EFSM model, and a path constraint of each EFSM path is obtained; 2, testing data meeting the path constraints are generated through a genetic algorithm, and initial individuals are given; 3, the fitness values of the initial individuals are estimated according to a fitness function, and if the fitness values of the individuals are zero, the individuals are the testing data meeting the path constraints, and the algorithm stops; 4, if the fitness values in the third step do not exceed the set maximum algebra, new-generation individuals are generated by selecting two individuals with the minimum fitness value from the current generation as father individuals for interlace operation or mutation operation, and the third step and the fourth step are carried out on the new-generation individuals repeatedly. For the paths with complex constraints or involving multiple variables, the success rate of generating the testing data meeting all the constraint conditions by the method is much higher than that by an S. Kalaji method.
Description
Technical field
The present invention relates to software test field, be specifically related to the path test data generation method based on EFSM model.
Background technology
Have at present much about the research of the Test data generation of program, also had Many researchers that the method based on search has been applied to this field.But the article about the test data generating method of EFSM model is not a lot, and method main is at present as follows:
J.Zhang etc. propose a kind of test data generating method of path-oriented, first semiology analysis acquisition approach constraint, then adopt constraint solver to solve this path constraint, obtain the input value meeting constraint condition.But the limitation of semiology analysis and constraint solving to solve non-linear constrain.
R.Lefticaru etc. define the list entries that a fitness evaluation method carrys out acquisition approach, the method has arrived each migration in path the adaptive value function application of Tracey, each function in path, the adaptive value in path is by regarding that a key event defines as.The limitation of the method is that it requires that each function can not comprise inner track and nested IF statement, otherwise just can not use the method for Tracy.
S.Kalaji etc. propose to use genetic algorithm to test EFSM, and he combines fitness function as genetic algorithm using branch's Distance geometry close to level, and this fitness function is not high for the success ratio of the EFSM coordinates measurement test data with Complex Constraints.For the EFSM path comprising equality constraint, can not generate and meet constrained test data, the average constraint rate that the test data of generation is violated is higher, and the of low quality of test data is described.
R.Lefticaru etc. improve the fitness function of S.Kalaji, and they become many independent subpaths path decomposing, and calculate the adaptive value of every single sub path according to the method for S.Kalaji, and the adaptive value in whole piece path is the adaptive value sum of each single sub path.They give those and meet compared with the good adaptive value of the individuality of many condition, and this method with us is similar.But their method still can encounter the same problem of S.Kalaji method, and we are not the independent subpaths that can find path.
Summary of the invention
The technical problem to be solved in the present invention is to provide the path test data generation method based on EFSM model, to expand finite-state machine (EFSM) model as research object, use genetic algorithm generates the test data towards EFSM path, considers its unlapped condition ratio of branch's Distance geometry when calculating individual fitness simultaneously.In an experiment, the method for our method and the S.Kalaji of prior art compares, and result shows that our method has good effect and can obtain the good test data of quality.
For achieving the above object, technical scheme of the present invention is as follows:
Based on the path test data generation method of EFSM model, comprise the following steps:
Step one, semiology analysis and data stream dependency analysis are carried out to EFSM model, obtain the path constraint of every bar EFSM model;
Step 2, use genetic algorithm generate the test data meeting path constraint, given initial individuals;
Step 3, according to fitness function assessment initial individuals fitness value, if the adaptive value of individuality is 0, so such initial individuals is exactly the test data meeting path constraint, algorithm stop;
If the adaptive value in step 4 step 3 does not exceed the maximum algebraically of setting, interlace operation or mutation operation can carried out as father's individuality when choosing two individualities with minimum adaptive value in former generation, generate a new generation individual, and repeat step 3 and step 4 to a new generation is individual.
In a preferred embodiment of the invention, the adaptive value in described step 4 has exceeded the maximum algebraically of setting, and algorithm stops.
In a preferred embodiment of the invention, the path constraint in described step one adopts parallel IF statement to represent.
In a preferred embodiment of the invention, only input variable is comprised in described path constraint.
In a preferred embodiment of the invention, described fitness function is branch's distance of initial individuals and the ratio sum of the condition in the unlapped path constraint of individuality.
By technique scheme, the invention has the beneficial effects as follows:
For having Complex Constraints or relating to multivariable path, it is more much higher than S.Kalaji method that our method generates the success ratio meeting the test data of institute's Prescribed Properties;
For the path that can only generate the path that can not meet the test data of all conditions completely and namely comprise equation, we weigh with the average constraint rate that the test data generated is violated and generate the quality of test data, and the test data quality that our method generates is than the height of S.Kalaji method.
Accompanying drawing explanation
In order to be illustrated more clearly in the embodiment of the present invention or technical scheme of the prior art, be briefly described to the accompanying drawing used required in embodiment or description of the prior art below, apparently, accompanying drawing in the following describes is only some embodiments of the present invention, for those of ordinary skill in the art, under the prerequisite not paying creative work, other accompanying drawing can also be obtained according to these accompanying drawings.
Fig. 1 is workflow diagram of the present invention.
Fig. 2 a is the EFSM model of embodiments of the invention 1.
Fig. 2 b is the EFSM model of embodiments of the invention 2.
Fig. 2 c is the EFSM model of embodiments of the invention 3.
Fig. 3 a is the average algebraically of the extension state machine model generation test data of simple Aircraft Security System of the present invention.
Fig. 3 b is the success ratio of the extension state machine model generation test data of simple Aircraft Security System of the present invention.
Fig. 3 c is the average algebraically of the extension state machine model generation test data of host-host protocol of the present invention.
Fig. 3 d is the average algebraically of the test data of the extension state machine model generation of elevator device of the present invention.
Fig. 3 e is the average constraint rate of the test data violation that the extension state machine model of elevator device of the present invention is produced.
embodiment
The technological means realized to make the present invention, creation characteristic, reaching object and effect is easy to understand, below in conjunction with concrete diagram, setting forth the present invention further.
With reference to Fig. 1, based on the path test data generation method of EFSM model, comprise the following steps:
Step one, semiology analysis and data stream dependency analysis are carried out for EFSM model, obtain the path constraint in every bar EFSM path.
Step 2, use genetic algorithm generate the test data meeting path constraint, given initial individuals.
Step 3, the fitness value individual according to fitness function assessment, fitness function is here individual branch's distance and the ratio sum of the condition in the unlapped path constraint of individuality.If the adaptive value of individuality is 0, so such individuality is exactly the test data meeting path constraint, and algorithm stops.
Step 4 otherwise, if do not exceed the maximum algebraically of setting, interlace operation can carried out as father's individuality when choosing two individualities with minimum adaptive value in former generation, generate the individuality of a new generation; Or carrying out mutation operation when choosing an individuality with minimum adaptive value in former generation as father's individuality, generating the individuality of a new generation.If exceeded the maximum algebraically of setting, algorithm has stopped.Repeat step 3-step 4.
The concept relevant with the present invention is as follows:
The finite-state machine (EFSM) of 1.1 expansions
Finite-state machine (FSM) is a kind of Mealy automat, and it contains finite state set, migration collection and input and output collection.Because it can not the data division of modeling, so adopt the finite-state machine of expansion as system model herein.The finite-state machine of expansion extends context variable, predicate and operation on the basis of FSM.
EFSM is a hexa-atomic group of (S; s
0; V; I; O; T), wherein S is finite state set; s
0it is original state; V is the set of context variable; I is finite input massage set; O is finite output message set; T is finite migration set.
Migration t ∈ T is a five-tuple (s
s; I; G; Op; s
e), wherein s
sit is the source state of t; I ∈ I is input, and it may be relevant with input parameter; G is the logical expression being called guard condition; The operation that op is made up of assignment statement or output statement etc.; s
eit is the dbjective state of t.
When at state s
stime, if receive be input i and guard condition g meets, so trigger migration t=(s
s; I; G; Op; s
e), perform the operation in op and State Transferring is s simultaneously
e.G and op can comprise input parameter and context variable.Here only consider the EFSMs determined.If for the multiple migrations with identical input from same state, once only have a guard condition moved to meet, namely once can only trigger a migration, so EFSM determines.
In EFSM, the guard condition of migration can be connected with OR by logical operator AND.The guard condition connected by AND we be expressed as nested IF statement.The guard condition connected by OR, we are divided into+1 migration of OR operational character number it, calculate their adaptive value respectively, finally using the adaptive value of the minimum value between them as whole guard condition.
1.2 semiology analysis
Semiology analysis is a kind of program analysis method of static state, and it uses value of symbol instead of actual value to carry out executive routine, and the result of execution is an expression formula about symbol.This is very useful for the relation analyzed between input and output.
When semiology analysis is applied in the Test data generation in path, it has just been portrayed as Test data generation problem the problem solved with the character expression obtained after value of symbol execution route.Such as, migration path t is considered
1t
2t
3, the predicate in each migration is x>0, y<15, z>=10 respectively, and we use value of symbol a respectively, and b, c substitute into variable x, and y, z, with value of symbol execution route t
1t
2t
3after obtain character expression (a>0ANDb<15ANDc>=10), generation pass t
1t
2t
3the problem of test data is just converted to the problem found character expression (a>0ANDb<15ANDc>=10) and separate.
1.3 data stream rely on
Some input variables and context variable is there is in EFSM.Usually we only represent by input variable the character expression that needs solve.So for the context variable in path, we need usage data flow analysis technology that they are replaced to corresponding value and input variable.
Given variable v, if v migration t in as input parameter or the operation at t in be assigned, so claim v be defined in t, be designated as def (t).If v quotes in (p-use) or in operation and quotes (c-use) in the predicate of migration t, so claim v to be used in t, be designated as use (t).
Given from migration t
ito t
jmigration path, v ∈ def (t
i) and v ∈ use (t
j), if v is at t
iand t
jbetween migration on all do not redefine, so we claim from t
ito t
jpath be the clear path of definition of v.(t
i; t
j) be called v definition-it is right to quote, t
jdata dependence is in t
i.
After the data stream obtaining context variable in each migration relies on, we adopt backward replacement technology that these context variables are replaced to corresponding value and input variable.So-called backward replacement, processes from back to front from last migration in path exactly, and replace to moving the variable quoted the definition that it relies on, the path expression finally obtained is the character expression that contains only input variable.
1.4 genetic algorithm
Method of testing based on search has been expressed as optimization problem the Generating Problems of test data.Must select a kind ofly to represent the mode of candidate solution and a kind of method assessing candidate solution in the method for testing based on search.The representation of candidate solution has scale-of-two, integer and full mold to encode usually.And assess candidate solution method we be commonly referred to fitness function.Fitness function is used to the method weighing candidate solution quality, and it composes a positive number to each candidate solution, and how far this positive number is used for assessing candidate solution also has from acceptable solution.Because optimization problem is minimization problem normally, so the candidate solution with lower adaptive value is better, have adaptive value be 0 solution be then acceptable solution.
After we choose coded system and define fitness function, just can use meta-heuristic search technique.Genetic algorithm is then a kind of powerful meta-heuristic technology.In genetic algorithm each solution we be called chromosome, they are by genomic constitution.The main flow of genetic algorithm uses fitness function to assess to the individuality in population, selects the solution with better adaptive value individual as father, then generate follow-on new individuality by crossover and mutation operation.
Hereafter comparative illustration is carried out to S.Kalaji method and embodiments of the invention:
We analyze from three aspects.
With reference to the embodiment 1 (the extension state machine model of Simple in-flight safety system EFSM-simple Aircraft Security System) that Fig. 2 a is EFSM model of the present invention, be first aspect be the average algebraically generating test data.
With reference to the embodiment 2 (the extension state machine model of Class II transport protocol EFSM-host-host protocol) that Fig. 2 b is EFSM model of the present invention, to be second aspect be when can not 100% generate test data, consider the success ratio of two kinds of methods.
With reference to the embodiment 3 (the extension state machine model of Lift system EFSM-elevator device) that Fig. 2 c is EFSM model of the present invention, be the 3rd aspect be when the test data meeting constraint condition can not be generated completely, consider that the test data generated violates the average ratio retrained in path.
Our method and the method for S.Kalaji all use Genetic Algorithm and Direct Search Toolbox for Mathlab 7.0 to realize.We use full mold coded representation individual, adopt random uniform design, selection scalar intersects, crossover probability is 0.8, use Gaussian mutation, selected population size is 20, and the initial span of each variable is [0 ... 100], the condition that search stops be adaptive value is 0 or arrives maximum algebraically 1000.For every bar migration path performs search 10 times.
With reference to 3a, Fig. 3 b, Fig. 3 c and Fig. 3 d; According to migration coverage criterion, Simple in-flight safety system EFSM always has 20 migration paths, and the average algebraically generating test data for this 20 paths is shown in Fig. 3 (a).
Two kinds of methods are to except path p
2; p
17; p
18; p
19; p
20; p
21during other coordinates measurement test data outer, their average algebraically is roughly the same, all about 52.This is possible because in these paths, and their constraint is all very simple, and only relates to single variable.For such situation, the performance of two kinds of methods is identical.To path p
2; p
17; p
18; p
19; p
20; p
21when generating test data, the average algebraically of the method that we propose is than the height of the method for S.Kalaji.
Two kinds of methods all can not all successfully generate the test data meeting path constraint at every turn in 10 search, but the probability of success that our method generates test data is more much higher than the method for S.Kalaji, two kinds of methods are that the success ratio that this 6 paths generates test data is shown in Fig. 3 (b).
This be possible the constraint of this 6 paths more complicated, and relate to multiple variable, the method of S.Kalaji just composes higher adaptive value to it when individuality does not meet more outer field constraint, although but some individual satisfied indivedual skin retrains but meets the constraint of multiple internal layer, selects such individuality to generate optimum solution.Such individuality generates in the process of test data at S.Kalaji and has but been rejected, and causes S.Kalaji method to restrain faster and namely obtains poor optimum solution faster, so cause him can not for having the coordinates measurement test data of Complex Constraints.
And the method that we propose considers the individual coverage condition for constraint, the individual fitness that coverage rate is higher is lower.Our method can select the individuality that may generate optimum solution, does not namely meet skin and retrains but the individuality meeting the constraint of multiple internal layer.So when causing generating test data, average algebraically is higher than S.Kalaji method, but the success ratio generating test data is more much higher than S.Kalaji method.
Similarly, according to migration coverage criterion, Transport Protocol EFSM always has 12 paths, and the average algebraically generating test data for this 12 paths is shown in Fig. 3 (c).Two kinds of methods can be except p
8coordinates measurement test data in addition, and average algebraically is almost identical.Can not be p
8the reason generating test data is p
8path constraint in comprise equation, and equation is very inappeasable.
There are 24 paths in Lift System EFSM, wherein only have p1 and p2 two paths can obtain data by genetic algorithm.For other path, two kinds of methods all can not generate the test data meeting constraint condition, but the average algebraically that our method generates optimum solution is higher than the method for S.Kalaji, causes the reason of this result with the explanation in In Flight Safety System EFSM.Fail to generate the path of test data for these, in order to analyze the quality of two kinds of methods, the average bound rate that we violate from the test data generated compares.The average bound rate of the method violation of S.Kalaji is about 80%, and the average bound rate that our method is violated is about 10%, and test data other constraint except violating equation that is our method generates is all satisfied.We weigh the quality of test data with the average bound rate that test data is violated, and the average bound rate of violation is lower, then test data quality is higher.From Fig. 3 e, the quality of the test data of our method generation is higher than S.Kalaji method a lot.
More than show and describe ultimate principle of the present invention and principal character and advantage of the present invention.The technician of the industry should understand; the present invention is not restricted to the described embodiments; what describe in above-described embodiment and instructions just illustrates principle of the present invention; without departing from the spirit and scope of the present invention; the present invention also has various changes and modifications, and these changes and improvements all fall in the claimed scope of the invention.Application claims protection domain is defined by appending claims and equivalent thereof.
Claims (5)
1., based on the path test data generation method of EFSM model, it is characterized in that, comprise the following steps:
Step one, semiology analysis and data stream dependency analysis are carried out to EFSM model, obtain the path constraint of every bar EFSM model;
Step 2, use genetic algorithm generate the test data meeting path constraint, given initial individuals;
Step 3, according to fitness function assessment initial individuals fitness value, if the adaptive value of individuality is 0, so such initial individuals is exactly the test data meeting path constraint, algorithm stop;
If the adaptive value in step 4 step 3 does not exceed the maximum algebraically of setting, interlace operation or mutation operation can carried out as father's individuality when choosing two individualities with minimum adaptive value in former generation, generate a new generation individual, and repeat step 3 and step 4 to a new generation is individual.
2. the path test data generation method based on EFSM model according to claim 1, is characterized in that, the adaptive value in described step 4 has exceeded the maximum algebraically of setting, and algorithm stops.
3. the path test data generation method based on EFSM model according to claim 1, is characterized in that, the path constraint in described step one adopts parallel IF statement to represent.
4. the path test data generation method based on EFSM model according to claim 1, is characterized in that, only comprise input variable in described path constraint.
5. the path test data generation method based on EFSM model according to claim 1, is characterized in that, described fitness function is branch's distance of initial individuals and the ratio sum of the condition in the unlapped path constraint of individuality.
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