CN105117331B - Coincidence correctness test case recognition methods and device towards location of mistake - Google Patents
Coincidence correctness test case recognition methods and device towards location of mistake Download PDFInfo
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Abstract
The embodiment of the invention discloses a kind of coincidence correctness test case recognition methods towards location of mistake and device, including：Collect the Cover Characteristics information that test case concentrates each test case operation pre-set programs；Each test case is run to tag along sort of the implementing result as each test case of the pre-set programs；According to the Cover Characteristics information and the test use cases of carrying tag along sort, regression model is trained, obtains disaggregated model；According to the disaggregated model, correct test case is predicted, the coincidence correctness test case in the correct test case is filtered out, so as to improve the efficiency of test case Wrong localization position.
Description
Technical field
The present invention relates to computer software to debug field, more specifically to a kind of coincidence towards location of mistake just
True property test case recognition methods and device.
Background technology
Current most of location of mistake methods utilize program coverage information, and journey is contrasted and analyzed based on specific statistical model
Statistical discrepancy of the sequence sentence between correct test case and failure testing use-case infers in program mistake position that may be present
Put.In order to which more effectively Wrong localization, researcher propose a variety of statistical models.This kind of statistical model utilizes program statement
Coverage information and program it is correct/unsuccessfully perform existing for relevance Wrong localization, they are generally based on two following vacations
If condition carrys out Wrong localization：
The 1st, if a program statement is performed by more failure testing use-cases, then the sentence may more include mistake；
The 2nd, if a program statement is performed by more correct test cases, then the sentence is more probably correct language
Sentence.
From above-mentioned assumed condition can be seen that this kind of statistical model assume wrong sentence and failure testing use-case there is
High relevancy, that is to say, that when the sentence of mistake is performed, program can produce the output of mistake.When this condition meets,
This kind of statistical method can will relatively efficiently find out position wrong in program.However, this during the test assume not
It can always meet, such as wrong sentence has been performed but program does not produce error message.Researcher proposes a kind of PIE
Model can be observed three conditions for needing to meet to study error message：
1st, mistake sentence is performed to；
2nd, mistake sentence makes program produce wrong program state；
3rd, the program state of mistake is propagated and influences the output of program.
Only when these three conditions meet at the same time, program could produce the output of mistake.As long as however, two bars below
When any one in part is not satisfied, even if mistake sentence is performed to, program remains to produce correct output.This situation
Under, test case covers wrong sentence in the process of implementation, is but marked as correct test case.It is false according to above-mentioned two
If condition understands that this class testing-case may reduce the suspicious degree of wrong sentence, so that unfavorable to the generation of location of mistake technology
Influence.
Therefore, the efficiency of test case Wrong localization position how is improved, is present problem to be solved.
The content of the invention
It is an object of the invention to provide a kind of coincidence correctness test case recognition methods towards location of mistake and dress
Put, to improve the efficiency of test case Wrong localization position.
To achieve the above object, an embodiment of the present invention provides following technical solution：
A kind of coincidence correctness test case recognition methods towards location of mistake, including：
Collect the Cover Characteristics information that test case concentrates each test case operation pre-set programs；
Each test case is run to point of the implementing result as each test case of the pre-set programs
Class label；
According to the Cover Characteristics information and the test use cases of carrying tag along sort, regression model is trained, is obtained
To disaggregated model；
According to the disaggregated model, correct test case is predicted, is filtered out skilful in the correct test case
Close correctness test case；Wherein, the coincidence correctness test case is that the language of mistake is performed in program process
Sentence still generates the test case correctly exported.
Preferably, it is described draw coincidence correctness test case after, further include:
Heavy label strategy is performed to the coincidence correctness test case.
Preferably, it is described draw coincidence correctness test case after, further include:
Drop policy is performed to the coincidence correctness test case.
Preferably, it is described according to the disaggregated model, correct test case is predicted, filters out the correct test
Coincidence correctness test case in use-case, including：
According to the disaggregated model, correct test case is predicted, draws the correct test case for coincidence just
The probability sorting of true property test case；
According to the probability sorting that the correct test case is coincidence correctness test case, the correct test is filtered out
Coincidence correctness test case in use-case.
Preferably, the regression model is logistic regression models.
A kind of coincidence correctness test case identification device towards location of mistake, including：
Collection module, the Cover Characteristics information of each test case operation pre-set programs is concentrated for collecting test case；
Tag along sort setup module, for each test case is run the implementing results of the pre-set programs as
The tag along sort of each test case；
Training module, for the test use cases according to the Cover Characteristics information and carrying tag along sort, to returning mould
Type is trained, and obtains disaggregated model；
Screening module, for according to the disaggregated model, being predicted to correct test case, filtering out the correct survey
Coincidence correctness test case in example on probation；Wherein, the coincidence correctness test case is to be held in program process
Wrong sentence of having gone still generates the test case correctly exported.
Preferably, further include:
Heavy label module, for performing heavy label strategy to the coincidence correctness test case；
Preferably, further include:
Discard module, for performing drop policy to the coincidence correctness test case.
Preferably, the screening module includes：
Probability sorting unit, for according to the disaggregated model, being predicted, drawing described correct to correct test case
Test case is the probability sorting of coincidence correctness test case；
Screening unit, for according to the probability sorting that the correct test case is coincidence correctness test case, screening
Go out the coincidence correctness test case in the correct test case.
Preferably, the regression model is logistic regression models.
By above scheme, a kind of coincidence correctness towards location of mistake provided in an embodiment of the present invention, which is tested, to be used
Example recognition methods and device, including：Collect the Cover Characteristics information that test case concentrates each test case operation pre-set programs；
Each test case is run to tag along sort of the implementing result as each test case of the pre-set programs；Root
According to the Cover Characteristics information and the test use cases of carrying tag along sort, regression model is trained, obtains disaggregated model；
According to the disaggregated model, correct test case is predicted, filters out the coincidence correctness in the correct test case
Test case, so as to improve the efficiency of test case Wrong localization position.
Brief description of the drawings
In order to illustrate more clearly about the embodiment of the present invention or technical scheme of the prior art, below will be to embodiment or existing
There is attached drawing needed in technology description to be briefly described, it should be apparent that, drawings in the following description are only this
Some embodiments of invention, for those of ordinary skill in the art, without creative efforts, can be with
Other attached drawings are obtained according to these attached drawings.
Fig. 1 is a kind of coincidence correctness test case recognition methods stream towards location of mistake disclosed by the embodiments of the present invention
Cheng Tu；
Fig. 2 is a kind of coincidence correctness test case identification device knot towards location of mistake disclosed by the embodiments of the present invention
Structure schematic diagram.
Embodiment
Below in conjunction with the attached drawing in the embodiment of the present invention, the technical solution in the embodiment of the present invention is carried out clear, complete
Site preparation describes, it is clear that described embodiment is only part of the embodiment of the present invention, instead of all the embodiments.It is based on
Embodiment in the present invention, those of ordinary skill in the art are obtained every other without creative efforts
Embodiment, belongs to the scope of protection of the invention.
The embodiment of the invention discloses a kind of coincidence correctness test case recognition methods towards location of mistake and device,
To improve the efficiency of test case Wrong localization position.
Referring to Fig. 1, a kind of coincidence correctness test case identification side towards location of mistake provided in an embodiment of the present invention
Method, including：
S101, collect the Cover Characteristics information that test case concentrates each test case operation pre-set programs；
Specifically, collecting the coverage information of test use cases in the process of running, and extract appropriate coverage information conduct
The Cover Characteristics information of test use cases.
Specifically, coincidence correctness test case necessarily covers error code in program process.These tests
Use-case is distributed on all Program paths that can reach error code position.In the angle performed from path, in order to
The error code in program is reached, branch of these test cases on execution route must is fulfilled for certain value condition.Cause
This, for the paths for covering error code, is distributed in the test case on this paths on execution route
Branch's value will show similar value characteristic, so the present embodiment extracting branch coverage information is as wall scroll test case
Cover Characteristics information.
Specifically, assume that program P contains m branch<p1,p2,...,pm>, the present embodiment collected using following steps
The Cover Characteristics information of test case.
(1) extraction and the relevant branch of mistake
Due to can only be covered in program P there is some branches by current correct test case, these branches hold
It is very weak with the relevance of error code whether row, if these branches not by current any one failure testing use-case covering make
Classification results will be influenced by being characterized a part for information, thus we using following rule-based filtering those incoherent point with mistake
Branch, so that the correlation properties of mistake can more be reflected in the branch retained.
Wherein, tps (tj) represent test case tjThe branch's set covered.From formula (1) as can be seen that appointing in RP
One branch is all at least covered by a test case of failure testing set of uses case Tr, that is to say, that the execution of branch may
Influence the infection and propagation of error code.Follow-up discussion for convenience, branch's set that RP is represented are expressed as { rp1,
rp2,...,rpw, w ＜=m.
(2) branch's Cover Characteristics information is collected
When each test case in testing results set of uses case T on program P, for each branch rp in RPj,
We collect following informationWithIt is illustrated respectively in a test case tjBranch rp during executionjImplementing result is genuine
Number and implementing result are false number.There may be circulation in being performed due to program, so it, which performs number, is likely larger than 1.
Work as rpjTrue or false perform be not triggered, then corresponding value be 0.For any one test case tj, its branch's covering
Feature can represent as follows：
S102, using each test case run the implementing result of the pre-set programs as each test case
Tag along sort；
After branch's Cover Characteristics information of all test cases has been collected, while by the implementing result of every test case
It is correct as class label, such as implementing result, then class label is 0, is otherwise 1.
S103, according to the Cover Characteristics information and carry tag along sort test use cases, regression model is instructed
Practice, obtain disaggregated model；
Specifically, the regression model is logistic regression models.
Specifically, the branch of the present embodiment all test cases using in test set is covered as characteristic information, to test use
The implementing result of example is trained logistic regression models as class label and draws disaggregated model.
S104, according to the disaggregated model, correct test case is predicted, is filtered out in the correct test case
Coincidence correctness test case；
Wherein, the coincidence correctness test case is that the sentence of mistake is performed in program process but is produced
The test case of correct output.
Performed specifically, coincidence correctness is tested use-case for the wrong sentence in program, but without generation mistake
Output.Coincidence correctness test case is that coincidence correctly meets following two at the same time and if only if it for a test case
Part：
1st, this test case performs the sentence of mistake in program process；
2nd, this test case generates correct output.
Specifically, it is described according to the disaggregated model, correct test case is predicted, filters out the correct test
Coincidence correctness test case in use-case, including：
According to the disaggregated model, correct test case is predicted, draws the correct test case for coincidence just
The probability sorting of true property test case；
According to the probability sorting that the correct test case is coincidence correctness test case, the correct test is filtered out
Coincidence correctness test case in use-case.
Specifically, working as according to the disaggregated model, after being predicted to correct test case, can obtain in correct test case
For the possibility size of coincidence correctness test case, and sort by size.Finally, the possibility coincidence correctness based on estimation is surveyed
Example on probation selects suitable doubtful coincidence correctness test case from big to small.
A kind of coincidence correctness test case recognition methods towards location of mistake provided in an embodiment of the present invention, including：
Collect the Cover Characteristics information that test case concentrates each test case operation pre-set programs；Each test case is run
Tag along sort of the implementing result of the pre-set programs as each test case；According to the Cover Characteristics information and take
Test use cases with tag along sort, are trained regression model, obtain disaggregated model；According to the disaggregated model, align
True test case is predicted, and the coincidence correctness test case in the correct test case is filtered out, so as to improve test
The efficiency of use-case Wrong localization position.
Specifically, in order to efficiently and rapidly identify coincidence correctness test case, returned in the present invention using logistic
Model.Logistic regression models belong to probabilistic type nonlinear regression, it is that the classification observation result of research two influences it with some
Between relation a kind of multivariable technique.After logistic regression models complete training, which is predicting new example
During generic, the probability that new example is a certain classification can quickly be calculated.Since its calculating cost is not high, should be readily appreciated that
And realization, and there is good classifying quality, thus extensive utilization has been obtained in classification problem.
The present embodiment is after the characteristic information of all test cases in obtaining T and class label, in this, as input pair
Logistic regression models are trained, and are obtained a disaggregated model, are denoted as LG.It is described below based on LG models to identify coincidence
Correctness test case.
The present embodiment proposes coincidence correctness test case number that may be present in a kind of estimation test set T first, with
This number instructs follow-up selection quantity.
The present invention infers possible coincidence correctness test case using the Cover Characteristics and implementing result of test case
Number.First, the suspicious angle value of error code is included, is denoted as S for each executable statement e in program, computing statement e
(e).Here we use Oichai measure formulas, because it shows relatively stable performance on Wrong localization.S's (e)
It is defined as follows:
Symbol<anp,anf,aep,aef>The test case quantity for meeting corresponding subscript condition is represented, wherein the of each symbol
One subscript represents that the sentence performs (e) by corresponding test case or is not performed (n), and second subscript represents to correspond to
Test case implementing result be correct (p) or wrong (f).It is that any bar be directed to when the context of symbol not can indicate that
During sentence, we can add the correlation that " (e) " represents sentence e behind symbol.For example, aep(e) represent to perform sentence e
And result is correct test case number.
Assuming that there is a wrong sentence e in single wrong version programf.According to determining for coincidence correctness test case
Justice, it is known that aep(ef) represent among test use cases actual coincidence correctness test case number.Due to wrong sentence ef
Location-Unknown in a program, so it is difficult to determining aep(ef) value.However, it is possible to it is readily derived the language for meeting following condition
Sentence e*：
arg maxe'∈p{S(e')|aef(e')=| Tf|} (3)
Sentence e*It is by T in all sentencesfIn all failure testing use-cases cover and the language of suspicious angle value S (e) maximum
Sentence.Due to wrong sentence efCorresponding aefIt is equal to | Tf|, thus at least there are a sentence to meet above-mentioned formula.But such language
Sentence e*There may be multiple, due to e*Corresponding aefAlways it is equal to | Tf|, and according to formula, because TfFor known quantity, so S (e)
Value only with aefAnd aepIt is related.Thus for any e for meeting formula (3)*For, its corresponding aepAlso it is always equal.Take
An arbitrary e*To derivation result below all without having an impact.
According to formula (3), due to aep(ef)=| Tf|, wherein sentence efBe probably meet formula (3) candidate sentence it
One, thus it is readily obtained S (e*)≥S(ef).Simultaneously as aep(e*)=| Tf| so easily drawing equation aef(e*)=aef
(ef) also set up.Therefore, a can be derivedep(e*)≤aep(ef).Proof procedure is as follows：
Due to aep(ef) represent the number of actual coincidence correctness test case among test use cases, thus according to formula
(3) e obtained*Corresponding aep(e*) value always be less than or equal to test use cases T in physical presence coincidence correctness survey
Number of cases mesh on probation.Aep(e*) value be denoted as CCN.
Then, doubtful coincidence correctness test case is calculated based on disaggregated model LG and CCN.In order to facilitate narration,
Note count is equal to Ticc, i.e., identified doubtful coincidence correctness test case quantity.Count is apparent from when initial as 0.This hair
It is bright that coincidence correctness test case is identified using the steps：
(1) for TpAny one test case t, calculate LG (T=1) value, i.e. the test case is coincidence correctness
The probability of test case.And LG (T=1) is sorted from big to small and forms ranking list RankPro.
(2) if count<CCN and RankPro are not sky, then by the test case addition of probable value maximum in RankPro
Ticc, while removed from RankPro, and count=count+1 is put, continue step (2)；Otherwise, step (3) is performed.
(3) returned Ticc as the doubtful coincidence correctness test case of identification.
Preferably, in another embodiment of the invention, it is described draw coincidence correctness test case after, further include:
Heavy label strategy is performed to the coincidence correctness test case.
Preferably, in another embodiment of the invention, it is described draw coincidence correctness test case after, further include:
Drop policy is performed to the coincidence correctness test case.
Specifically, in the present embodiment, after doubtful coincidence correctness test case is identified, propose two kinds of strategies respectively
Handled to verify the influence to location of mistake technology.
Specifically, first from correct test use cases TpIn have chosen no more than CCN test case as doubtful coincidence
Correctness test case is added in Ticc.For these test cases, the present invention using two kinds of different processing policy validations its
Influence to location of mistake technical efficiency.
(1) drop policy：The coincidence correctness test case of identification is removed among test use cases T, remaining survey
Example on probation is using as the input of location of mistake technology.This strategy reduces available test information to a certain extent.Using this
During kind strategy, the T=Tf+Tp-Ticc after renewal.
(2) heavy label strategy：It is use-case that the coincidence correctness test case of identification, which is all marked, and will more
Input of the test use cases as location of mistake technology after new.This strategy adds failure testing among test use cases and uses
The quantity of example, but thus may provide more misleading informations.During using this strategy, the T=Tf+Tp+Ticc after renewal.
Update to obtain test use cases T using the above method, when for location of mistake method, coincidence correctness can be alleviated
Influence of the test case to location of mistake result, and then improve the efficiency of location of mistake.
Referring to Fig. 2, a kind of coincidence correctness test case towards location of mistake provided in an embodiment of the present invention identifies dress
Put, including：
Collection module 100, the Cover Characteristics letter of each test case operation pre-set programs is concentrated for collecting test case
Breath；
Tag along sort setup module 200, for each test case to be run to the implementing result of the pre-set programs
Tag along sort as each test case；
Training module 300, for the test use cases according to the Cover Characteristics information and carrying tag along sort, to returning
Model is trained, and obtains disaggregated model；
Screening module 400, for according to the disaggregated model, being predicted to correct test case, filter out it is described just
Coincidence correctness test case in true test case；Wherein, the coincidence correctness test case is in program process
In perform mistake sentence still generate the test case correctly exported.
A kind of coincidence correctness test case identification device towards location of mistake provided in an embodiment of the present invention, including：
Collect the Cover Characteristics information that test case concentrates each test case operation pre-set programs；Each test case is run
Tag along sort of the implementing result of the pre-set programs as each test case；According to the Cover Characteristics information and take
Test use cases with tag along sort, are trained regression model, obtain disaggregated model；According to the disaggregated model, align
True test case is predicted, and the coincidence correctness test case in the correct test case is filtered out, so as to improve test
The efficiency of use-case Wrong localization position.
Preferably, in another embodiment of the invention, further include:
Heavy label module, for performing heavy label strategy to the coincidence correctness test case.
Preferably, in another embodiment of the invention, further include:
Discard module, for performing drop policy to the coincidence correctness test case.
Preferably, in another embodiment of the invention, the screening module 400 includes：
Probability sorting unit, for according to the disaggregated model, being predicted, drawing described correct to correct test case
Test case is the probability sorting of coincidence correctness test case；
Screening unit, for according to the probability sorting that the correct test case is coincidence correctness test case, screening
Go out the coincidence correctness test case in the correct test case.
Preferably, in another embodiment of the invention, the regression model is logistic regression models.
Each embodiment is described by the way of progressive in this specification, what each embodiment stressed be and other
The difference of embodiment, between each embodiment identical similar portion mutually referring to.
The foregoing description of the disclosed embodiments, enables professional and technical personnel in the field to realize or use the present invention.
A variety of modifications to these embodiments will be apparent for those skilled in the art, as defined herein
General Principle can be realized in other embodiments without departing from the spirit or scope of the present invention.Therefore, it is of the invention
The embodiments shown herein is not intended to be limited to, and is to fit to and the principles and novel features disclosed herein phase one
The most wide scope caused.
Claims (8)
- A kind of 1. coincidence correctness test case recognition methods towards location of mistake, it is characterised in that including：Collect the Cover Characteristics information that test case concentrates each test case operation pre-set programs；Each test case is run to contingency table of the implementing result as each test case of the pre-set programs Label；According to the Cover Characteristics information and the test use cases of carrying tag along sort, regression model is trained, is divided Class model；According to the disaggregated model, correct test case is predicted, show that the correct test case is coincidence correctness The probability sorting of test case；Determine that coincidence correctness is tested using the Cover Characteristics information of each test case and the implementing result of the pre-set programs Use-case number；Used according to the correct test case for the probability sorting of coincidence correctness test case and coincidence correctness test Number of cases mesh, filters out the coincidence correctness test case in the correct test case；Wherein, the coincidence correctness test case is that the sentence that mistake is performed in program process still generates just The test case of true output.
- 2. coincidence correctness test case recognition methods according to claim 1, it is characterised in that described to draw coincidence just After true property test case, further include:Heavy label strategy is performed to the coincidence correctness test case.
- 3. coincidence correctness test case recognition methods according to claim 1, it is characterised in that described to draw coincidence just After true property test case, further include:Drop policy is performed to the coincidence correctness test case.
- 4. the coincidence correctness test case recognition methods according to any one in claim 1-3, it is characterised in that institute It is logistic regression models to state regression model.
- A kind of 5. coincidence correctness test case identification device towards location of mistake, it is characterised in that including：Collection module, the Cover Characteristics information of each test case operation pre-set programs is concentrated for collecting test case；Tag along sort setup module, for each test case to be run the implementing result of the pre-set programs as described in The tag along sort of each test case；Training module, for according to the Cover Characteristics information and carry tag along sort test use cases, to regression model into Row training, obtains disaggregated model；Screening module, for according to the disaggregated model, being predicted to correct test case, filtering out the correct test and use Coincidence correctness test case in example；The screening module includes：Probability sorting unit, for according to the disaggregated model, being predicted to correct test case, drawing the correct test Use-case is the probability sorting of coincidence correctness test case；Screening unit, for being determined ingeniously using the Cover Characteristics information of each test case and the implementing result of the pre-set programs Close correctness test case number；It is the probability sorting of coincidence correctness test case and described according to the correct test case Coincidence correctness test case number, filters out the coincidence correctness test case in the correct test case；Wherein, it is described Coincidence correctness test case is that the sentence that mistake is performed in program process still generates the survey correctly exported Example on probation.
- 6. coincidence correctness test case identification device according to claim 5, it is characterised in that further include:Heavy label module, for performing heavy label strategy to the coincidence correctness test case.
- 7. coincidence correctness test case identification device according to claim 5, it is characterised in that further include:Discard module, for performing drop policy to the coincidence correctness test case.
- 8. the coincidence correctness test case identification device according to any one in claim 5-7, it is characterised in that institute It is logistic regression models to state regression model.
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