CN102629229A - Co-diversified random testing (CAT) method - Google Patents
Co-diversified random testing (CAT) method Download PDFInfo
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- CN102629229A CN102629229A CN2012100526910A CN201210052691A CN102629229A CN 102629229 A CN102629229 A CN 102629229A CN 2012100526910 A CN2012100526910 A CN 2012100526910A CN 201210052691 A CN201210052691 A CN 201210052691A CN 102629229 A CN102629229 A CN 102629229A
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Abstract
The invention discloses a co-diversified random testing (CAT) method. The diversity among internal program structures of randomly generated test cases is recognized through predicate interpretation (PI); the diversity among input domains of the test cases is recognized through Euclidean distances among the test cases; and then the diversity in the two aspects are combined to efficiently generate an effective test case set, so that the co-diversified random testing method is an automatic random testing technology integrating a black-box testing technology and a white-box testing technology. By adopting the CAT method, the problem of test case redundancies resulted from program structure ignorance in an adaptive random testing (ART) method is improved, the problem of complexity of a path condition (PC) used during symbolic execution is alleviated, and high efficiency and high effectiveness of test case generation are also realized during software testing.
Description
Technical field
The invention belongs to especially automatic test field, software test field; Relate to associating otherness random test technology CAT (Co-diversified rAndom Testing); Be used for high efficiency generation test use cases; Thereby realize Validity Test, be the associating otherness random testing method of the white box of a kind of joint program and the black box information gap opposite sex program.
Background technology
Random test RT is a simple robotization Black-box testing Cases generation technique, realizes easily, is widely used in the sight of source code and specification disappearance.RT produces test case because completely random lacks purpose, has only guaranteed the efficient (Efficiency) of test and can't guarantee validity (Effectiveness).In numerous raisings or improved method to RT, the self-adapting random test ART that more famous is has been proposed in 2004 by T.Y.Chen etc.Be the Black-box Testing technology equally; ART introduces test case and weighs the otherness between test case in the Euclidean distance on the input domain; And select the test case that generates at random through this otherness and construct whole test use cases, with the validity of enhanced random test.
Symbol is carried out by proposing in 1976 from J.C.King, and its basic thought just is to use value of symbol rather than actual value to be used as the input of program.A basic notion was exactly path condition PC (Path Condition) during symbol was carried out, and path condition PC is extract (conjunction) of a series of predicates, and input variable has only the PC condition that satisfies a paths, could pass through this paths.Wherein, it is very challenging to make up PC, even a branch statement only comprises a complex data structures.(Predicate Interpretation PI) is the subexpression of a PC, and the PI of a branch can skip other branch statements and simply obtain and predicate is expressed PI.
Summary of the invention
Technical matters to be solved by this invention is: the Black-box Testing method has very high efficient in the existing method for generating test case, but can not guarantee the validity of test case; Though the white-box testing method can generate effective test case, because complexity problem can't guarantee the efficient that test case generates.Therefore need a kind of test case generation technique of taking into account high-level efficiency and high-efficiency.
Technical scheme of the present invention is: a kind of associating otherness random testing method, treat test procedure, and generate test case at random, select test case therein, obtain the final test set of uses case, may further comprise the steps:
1) generates initial test case collection Tu={t at random
0;
2) the predicate expression set PI:{p of measuring program is treated in acquisition
1, p
2..., p
n}:
At first use the instrument Soot that increases income to generate the DFD of intermediate code; And use reverse data flow analytical calculation predicate to express set PI; Promptly oppositely analyze each fundamental block or each statement successively along the data stream limit from the outlet of DFD; PI is initially empty set, in analytic process according to the transport function f of each statement
sIncrease or modification predicate expression set PI corresponding the processing as follows:
A) if statement s is not that assignment statement neither conditional statement, then f
sBe unit function, PI is constant in predicate expression set;
B) if statement s is a condition statement, then predicate statement is wherein joined among the predicate expression set PI: f
s(x)=x ∪ gen
s, gen wherein
sRepresent the predicate set in this conditional statement, x representes former predicate expression set PI;
C) if statement s is the assignment statement v=g (y to variable v
1, y
2...), so with all the variable v:f in the expression formula replacement predicate set of v
s(pi (v))=pi (g (y
1, y
2... )), assignment statement v=g (y wherein
1, y
2...) variable v is given about variable y
i(i=1,2 ...) expression formula, g is about variable y
i(i=1,2 ...) any function, pi (v) representes the predicate expression formula about v, pi (g (y
1, y
2... )) represent v is replaced with g (y
1, y
2...) after the predicate expression formula;
3) if do not reach predefined testing standard, then continue step 4), otherwise finish T
uBe exactly final test use cases, said testing standard be the tester according to the predefined level of coverage of test request, represent with number percent;
4) generate k candidate's test case t at random
1, t
2..., t
k, form candidate's test use cases Tr, Tr={t
1, t
2..., t
k, to each candidate's test case t
i∈ Tr, i=1,2 ... K calculates bd
i=min{B (t
i, t
j) and ed
i=min{E (t
i, t
j), t wherein
j∈ Tu, bd
iExpression test case t
iEach test case branch minimum value and value in the Tu, ed
iExpression test case t
iThe minimum Euclideam distance of each test case in the Tu;
5) according to candidate's test case t
iTo branch's distance and the Euclidean distance of test use cases Tu, from candidate's test use cases Tr, select a test case and be used as new test case and join among the test use cases Tu, be specially: at first calculate each candidate's test case t
iThe minimum distance b d of branch with test case among the test use cases Tu
i, preferentially select the test case of minimum branch apart from maximum, even do not comprise the minimum distance b d of branch among the test use cases Tu
iMaximum test case is then selected this use-case for use, if comprise among the test use cases Tu, then selects the minimum distance b d of branch
iSecond largest test case, by that analogy; If have the minimum distance b d of branch
iPeaked test case has more than two, then judges minimum Euclideam distance ed
i, preferentially select minimum Euclideam distance ed
iMaximum candidate's test case t
i,, then select the minimum distance b d of branch if comprised this test case among the test use cases Tu
iSecond largest test case, by that analogy; The test case of selecting is added among the Tu, get back to step 3), constantly select new test case according to this, up to satisfying testing standard.
In the step 5), test case t
iThe minimum value and value bd of each test case branch in the Tu
iCalculating following:
With candidate's test case t
iBring predicate into and express each the predicate expression formula among the set PI, if its result is true, then is 1, otherwise is 0, it is 01 sequence that predicate is expressed the element number of set PI that each test case obtains a length; Each test case among the test use cases Tu is done same calculating, obtains 01 sequence of each test case among the test use cases Tu, and the branch between two different test cases is apart from B (t
i, t
j) be the Hamming distance of its 01 sequence, calculate bd thus
i
Test case t
iThe minimum Euclideam distance ed of each test case in the Tu
iCalculating following:
If test case t
i=(v
1, v
2..., v
q), v
qCorrespondence is treated q function parameter of measuring program, the test case t among the test use cases Tu
j=(a
1, a
2..., a
q), a
qCorrespondence is treated q function parameter of measuring program,
Then the Euclidean distance of two test cases is:
Calculate ed thus
i
The present invention discerns the otherness on the program inner structure between the test case that generates at random through PI; Discern the otherness of test case on input domain through the Euclidean distance between test case; Unite this two difference then, thereby generate effective test use cases expeditiously.For a test use cases Tr who generates at random, to t arbitrarily
i∈ Tr is with each t
j∈ Tu compares, and calculates the value of each PI, and the B (t that obtains thus
i, t
j); Calculate E (t in addition
i, t
j), associating B (t
i, t
j) and E (t
i, t
j) measurement test case t
iAnd the difference between the test case of having used, thereby select more effectively test case.
The present invention is a kind of associating otherness random test technology CAT; With existing random test compared with techniques; The predicate information that when selecting test case, has added program, thus more effectively test case in the substantive test use-case that generates at random, selected, avoided blindly selecting.Processing of the present invention is in addition calculated simple; Though added program predicate information, do not add all program informations, avoided in the white-box testing macromethod to program; Like the calculating of path constraint in the symbol execution, thereby improved the efficient that generates test case.In sum, the present invention is avoiding under the situation of great amount of calculation, and the random test use-case of high efficiency selection high-efficiency combines the advantage of Black-box Testing and white-box testing, for existing measuring technology provides new thinking.
Description of drawings
Fig. 1 is a schematic flow sheet of the present invention.
Embodiment:
Like Fig. 1, step of the present invention is following:
1) generates initial test case collection Tu={t at random
0;
2) predicate that obtains program is expressed set PI{p
1, p
2..., p
n}
At first use the instrument Soot (http://www.sable.mcgill.ca/soot/) that increases income to generate the DFD of intermediate code; And use the back to calculate predicate to data-flow analysis and express set PI, promptly oppositely analyze each fundamental block or each statement successively along the data stream limit from the outlet of DFD.
In the program, the transport function of every statement has been described the effect of this statement, the transport function f of every statement
s, predicate is expressed set PI and is initially empty set, in analytic process according to the transport function f of each statement
sIncrease or modification predicate expression set PI corresponding the processing as follows:
A) if statement s is not that assignment statement neither conditional statement, then f
sBe unit function, PI is constant in predicate expression set;
B) if statement s is a condition statement, then predicate statement is wherein joined among the predicate expression set PI: f
s(x)=x ∪ gen
s, gen wherein
sRepresent the predicate set in this conditional statement, x representes former predicate expression set PI;
C) if statement s is the assignment statement v=g (y to variable v
1, y
2...), so with all the variable v:f in the expression formula replacement predicate set of v
s(pi (v))=pi (g (y
1, y
2... )), assignment statement v=g (y wherein
1, y
2...) variable v is given about variable y
i(i=1,2 ...) expression formula, g is about variable y
i(i=1,2 ...) any function, pi (v) representes the predicate expression formula about v, pi (g (y
1, y
2... )) represent v is replaced with g (y
1, y
2...) after the predicate expression formula;
3) if do not reach predefined testing standard, then continue step 4), otherwise finish; Tu is exactly final test use cases; Said testing standard be the tester based on the predefined level of coverage of test request, represent with percentage, cover or 50% condition covering etc. like 50% path;
4) generate k candidate's test case t at random
1, t
2..., t
k, form candidate's test use cases Tr, Tr={t
1, t
2..., t
k, to each candidate's test case t
i∈ Tr, i=1,2 ... K calculates bd
i=min{B (t
i, t
j) and ed
i=min{E (t
i, t
j), t wherein
j∈ Tu, bd
iExpression test case t
iEach test case branch minimum value and value in the Tu, ed
iExpression test case t
iThe minimum Euclideam distance of each test case in the Tu; Computing method are following:
With each candidate's test case t
iBring predicate into and express each the predicate expression formula among the set PI, if its result is true, then is 1, otherwise is 0, it is 01 sequence that predicate is expressed set PI element numerical value that each test case obtains a length; In like manner, each test case among the Tu also has 01 such sequence;
Branch's distance between two different test cases be the Hamming distance of its 01 sequence, and the size of Euclidean distance is through following formula calculating:
If test case t
i=(v
1, v
2..., v
q), v
qCorrespondence is treated q function parameter of measuring program,
Another test case t
j=(a
1, a
2..., a
q), a
qCorrespondence is treated q function parameter of measuring program,
Then the Euclidean distance of two test cases is:
6), concentrate from candidate's test case according to branch's distance and Euclidean distance and to select a test and be used as new test case, and join among the set Tu:
At first calculate each candidate's test case t
iThe minimum distance b d of branch with test case among the test use cases Tu
i, preferentially select the test case of minimum branch apart from maximum, even do not comprise the minimum distance b d of branch among the test use cases Tu
iMaximum test case is then selected this use-case for use, if comprise among the test use cases Tu, then selects the minimum distance b d of branch
iSecond largest test case, by that analogy; If have the minimum distance b d of branch
iPeaked test case has more than two, then judges minimum Euclideam distance ed
i, preferentially select minimum Euclideam distance ed
iMaximum candidate's test case t
i,, then select the minimum distance b d of branch if comprised this test case among the test use cases Tu
iSecond largest test case, by that analogy; The test case of selecting is added among the Tu, get back to step 3), constantly select new test case according to this, up to satisfying testing standard.
Through specific embodiment effect of the present invention is described below.
That this experiment is chosen is simple in structure, four mathematical program bessj0 of numerical value input, julday, and plgndr and simple are as experimental subjects; Then through constant replacement (Constant Replacement, CRP), relational operator replacement (Relational Operator Replacement; ROR); (Arithmetic Operator Replacement AOR) injects the wrong wrong version that obtains, and wherein each wrong version only contains a mistake in the arithmetic operator replacement.
This experiment chooses that commonly used the testing measurement criterion---F-measure is as module.Its computing method are, constantly the use test use-case tests and will find for the first time that employed test case number scale is F-count when wrong, and the number percent that then F-count is accounted for all test cases is designated as F-measure.In practice, repeatedly calculate F-count and get its mean value as final F-count.
Experimental result is as shown in table 1, the number percent that CAT of the present invention is significantly improved with respect to ART (see form the 5th row), some in addition surpassed 50%, more credible in order to make the result, experiment is understood this result through the monolateral t certificate of inspection.On the specialty, the p value is a declining indicator of credible result level, and the p value is big more, can not think more the association of variable in the sample be overall in the related reliability index of each variable.On the statistics, 0.05 p value is thought the border degree that can receive mistake usually.Therefore can find out that from last row of table 1 experimental data is a science, believable.
Table 1 CAT test of the present invention and the test effect that has the ART test now
Claims (2)
1. an associating otherness random testing method is characterized in that treating test procedure, generates test case at random, selects test case therein, obtains the final test set of uses case, may further comprise the steps:
1) generates initial test case collection Tu={t at random
0;
2) the predicate expression set PI:{p of measuring program is treated in acquisition
1, p
2..., p
n}:
At first use the instrument Soot that increases income to generate the DFD of intermediate code; And use reverse data flow analytical calculation predicate to express set PI; Promptly oppositely analyze each fundamental block or each statement successively along the data stream limit from the outlet of DFD; PI is initially empty set, in analytic process according to the transport function f of each statement
sIncrease or modification predicate expression set PI corresponding the processing as follows:
A) if statement s is not that assignment statement neither conditional statement, then f
sBe unit function, PI is constant in predicate expression set;
B) if statement s is a condition statement, then predicate statement is wherein joined among the predicate expression set PI: f
s(x)=x ∪ gen
s, gen wherein
sRepresent the predicate set in this conditional statement, x representes former predicate expression set PI;
C) if statement s is the assignment statement v=g (y to variable v
1, y
2...), so with all the variable v:f in the expression formula replacement predicate set of v
s(pi (v))=pi (g (y
1, y
2... )), assignment statement v=g (y wherein
1, y
2...) variable v is given about variable y
i(i=1,2 ...) expression formula, g is about variable y
i(i=1,2 ...) any function, pi (v) representes the predicate expression formula about v, pi (g (y
1, y
2... )) represent v is replaced with g (y
1, y
2...) after the predicate expression formula;
3) if do not reach predefined testing standard, then continue step 4), otherwise finish T
uBe exactly final test use cases, said testing standard be the tester according to the predefined level of coverage of test request, represent with number percent;
4) generate k candidate's test case t at random
1, t
2..., t
k, form candidate's test use cases Tr, Tr={t
1, t
2..., t
k, to each candidate's test case t
i∈ Tr, i=1,2 ... K calculates bd
i=min{B (t
i, t
j) and ed
i=min{E (t
i, t
j), t wherein
j∈ Tu, bd
iExpression test case t
iEach test case branch minimum value and value in the Tu, ed
iExpression test case t
iThe minimum Euclideam distance of each test case in the Tu;
5) according to candidate's test case t
iTo branch's distance and the Euclidean distance of test use cases Tu, from candidate's test use cases Tr, select a test case and be used as new test case and join among the test use cases Tu, be specially: at first calculate each candidate's test case t
iThe minimum distance b d of branch with test case among the test use cases Tu
i, preferentially select the test case of minimum branch apart from maximum, even do not comprise the minimum distance b d of branch among the test use cases Tu
iMaximum test case is then selected this use-case for use, if comprise among the test use cases Tu, then selects the minimum distance b d of branch
iSecond largest test case, by that analogy; If have the minimum distance b d of branch
iPeaked test case has more than two, then judges minimum Euclideam distance ed
i, preferentially select minimum Euclideam distance ed
iMaximum candidate's test case t
i,, then select the minimum distance b d of branch if comprised this test case among the test use cases Tu
iSecond largest test case, by that analogy; The test case of selecting is added among the Tu, get back to step 3), constantly select new test case according to this, up to satisfying testing standard.
2. a kind of associating otherness random testing method according to claim 1 is characterized in that in the step 5) test case t
iThe minimum value and value bd of each test case branch in the Tu
iCalculating following:
With candidate's test case t
iBring predicate into and express each the predicate expression formula among the set PI, if its result is true, then is 1, otherwise is 0, it is 01 sequence that predicate is expressed the element number of set PI that each test case obtains a length; Each test case among the test use cases Tu is done same calculating, obtains 01 sequence of each test case among the test use cases Tu, and the branch between two different test cases is apart from B (t
i, t
j) be the Hamming distance of its 01 sequence, calculate bd thus
i
Test case t
iThe minimum Euclideam distance ed of each test case in the Tu
iCalculating following:
If test case t
i=(v
1, v
2..., v
q), v
qCorrespondence is treated q function parameter of measuring program, the test case t among the test use cases Tu
j=(a
1, a
2..., a
q), a
qCorrespondence is treated q function parameter of measuring program,
Then the Euclidean distance of two test cases is:
Calculate ed thus
i
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Cited By (6)
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| CN102855191A (en) * | 2012-08-31 | 2013-01-02 | 中国人民解放军63928部队 | Embedded basic software code branch coverage testing data inheritance searching method |
| CN103279422A (en) * | 2013-06-17 | 2013-09-04 | 东南大学 | Self-adaptive random test method based on rejecting area |
| CN105446882A (en) * | 2015-11-27 | 2016-03-30 | 合肥通用机械研究院 | Testing method of black-box testing system for software evaluation of household and similar electric appliances |
| CN105468526A (en) * | 2015-11-27 | 2016-04-06 | 合肥通用机械研究院 | Black box test system for software evaluation of household and similar appliances |
| CN106339256A (en) * | 2015-07-06 | 2017-01-18 | 阿里巴巴集团控股有限公司 | Simulator automatic distribution method and device |
| CN108762749A (en) * | 2018-05-24 | 2018-11-06 | 福州大学 | System object figure automatic generation method based on code analysis |
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Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102855191A (en) * | 2012-08-31 | 2013-01-02 | 中国人民解放军63928部队 | Embedded basic software code branch coverage testing data inheritance searching method |
| CN102855191B (en) * | 2012-08-31 | 2015-04-15 | 中国人民解放军63928部队 | Embedded basic software code branch coverage testing data inheritance searching method |
| CN103279422A (en) * | 2013-06-17 | 2013-09-04 | 东南大学 | Self-adaptive random test method based on rejecting area |
| CN103279422B (en) * | 2013-06-17 | 2015-09-09 | 东南大学 | A kind of method that self-adapting random based on repulsive area is tested |
| CN106339256A (en) * | 2015-07-06 | 2017-01-18 | 阿里巴巴集团控股有限公司 | Simulator automatic distribution method and device |
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| CN105446882A (en) * | 2015-11-27 | 2016-03-30 | 合肥通用机械研究院 | Testing method of black-box testing system for software evaluation of household and similar electric appliances |
| CN105468526A (en) * | 2015-11-27 | 2016-04-06 | 合肥通用机械研究院 | Black box test system for software evaluation of household and similar appliances |
| CN105446882B (en) * | 2015-11-27 | 2017-11-07 | 合肥通用机械研究院 | The method of testing of family expenses and similar applications electrical equipment software evaluation Black-box Testing system |
| CN105468526B (en) * | 2015-11-27 | 2018-01-12 | 合肥通用机械研究院 | Family expenses and similar applications electrical equipment software evaluation Black-box Testing system |
| CN108762749A (en) * | 2018-05-24 | 2018-11-06 | 福州大学 | System object figure automatic generation method based on code analysis |
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