CN109190901A - The credible evaluation method of reliability assessment result based on multi objective measurement - Google Patents
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Abstract
It proposes and a kind of integrates the several softwares reliability assessment evaluation of result index such as accurate, stable, steady, utilize tournament method, form a kind of reliability of component software system assessment result assessment technique of multi-angle comprehensive multi-index, it can effectively, easily measure reliability of component software system assessment result, judge whether assessment result is credible, selection for reliability assessment scheme provides foundation, solves the problems, such as a variety of component software reliability assessment scheme choices which is better and which is worse.
Description
Technical field
The present invention relates to software reliability evaluation fields, and in particular to a kind of reliability assessment knot based on multi objective measurement
The credible evaluation method of fruit.
Background technique
The main target of software reliability evaluation be according to the existing information about software systems, to software systems can
It is analyzed and is assessed by property, determine if to meet exploitation, maintenance and requirement, and to the design structure and function of software
Realize etc. makes corresponding guidance.It is used by the intersection to the methods of computer technology, artificial intelligence, probability theory, software
Reliability assessment has formd the theoretical system of a set of relative maturity as a kind of index evaluation system.However, due to component
Software and the software systems situation being made of component software are sufficiently complex, the side such as acquisition and arrangement of data needed for reliability assessment
Face inevitably will receive the influence of various factors, so that generating certain deviation between assessment result and truth, i.e.,
There are errors.It is former that Reliability Evaluation Model carries out different degrees of simulation and simplification etc. to the practical operation situation of assessment object
Cause, the new problem as after reliability assessment that whether final assessment result of reliability is credible enough.Therefore it needs to reliable
The evaluation method of property assessment result is studied, and determines whether the result meets the truth of component software system, and then say
Can the bright reliability assessment done to system play original effect.
The essence of software reliability evaluation process is a mapping, i.e., the situation and characterisitic parameter of software and software systems
As input, pass through preset Reliability Evaluation Model, output reliability associated arguments, when such as reliability, average Inactivity Interval
Between, etc..Therefore, error source present in assessment result can be divided into following three kinds: error originated from input, model error and random
Error.Illustrate these three error categories and corresponding evaluation method separately below.
Error originated from input refers to defeated in the arrangement to software and the situation of system, analysis, the reliability assessment that obtains after description
Enter parameter and real conditions have gap, enables between assessment result and legitimate reading that there are certain errors.Because assessment models are reflected
It is sufficiently complex to penetrate function itself, it is possible to which there are chaos phenomenons near certain points in domain, so that input quantity is small
Variation is uncontrollably amplified after mapping, to influence the levels of precision of the assessment result as output quantity.It is general next
It says, by the methods of sensitivity analysis or analysis of uncertainty, can determine influence of the minor change to output quantity of input quantity
Size achievees the purpose that examine error originated from input size.
Model error refers to the random error that Reliability Evaluation Model generates in calculating process, and other may cause is commented
The error for estimating result inaccuracy is then classified as random error.The difference of the two is that model error is from assessment models right
Intrinsic uncertainty when the truth of software is emulated, and other errors are except input data and assessment models
Random perturbation.In general, model error and random error can be distinguished by statistical method such as variance analysis etc., and respectively
It determines influence size of each to assessment result, realizes the effect for examining error size.
Whether the evaluation method of component software reliability assessment result has credibility primarily directed to assessment result, that is, passes through
The calculated predicted value of assessment models is crossed compared with the true value for representing truth, whether deviation can connect in soft project
By in the range of.Here it is intended that with four indexs and the credibility of assessment result is evaluated: accuracy；Stability；Steadily and surely
Property；Uncertainty.Accuracy refer to assessment result compared with true value (or other approximate can represent the reference value of true value) whether
It is close enough；After stability refers to that repeatedly same component software system identical to Developing status and operation conditions is assessed,
It is whether close enough between the multiple assessment results obtained；Robustness refers in view of carrying out data to software systems to be assessed
The error being likely to occur when information collection, thus to input condition carry out minor modifications after, the change for the assessment result observed
Change situation, even if there are error originated from input, assessment result is still not much different with truth explanation if variation is smaller.Not really
Surely degree index is then under the premise of considering the uncertainty of known each parameter of evaluation scheme, to the reliability assessment result of system
Uncertainty is synthesized and is calculated, to judge whether assessment result is reliable.If assessment result it is above four aspect all
Performance preferably, is then enough the achieved reliability situation for illustrating that the result can be used to indicate software systems.
Summary of the invention
It is evaluated, is considered when lacking true value for the reliability assessment result of component software system, it can not
Two kinds of reliability assessment scheme technological difficulties which is better and which is worse are judged, based on stability, accuracy, robustness, uncertainty etc.
Generally acknowledged reliability assessment result Measure Indexes propose a kind of reliability of component software system assessment based on multi objective measurement
Evaluation of result technology, the Dependability Problem for solving reliability assessment result excessively rely on sensitivity analysis, and reliability assessment side
The problems such as case can not effectively select the superior and eliminate the inferior.Specific step is as follows for this method:
Step 1 carries out reliability assessment to component software using reliability assessment scheme, obtains reliability assessment result.
Step 2, for the reliability assessment as a result, calculating its corresponding robustness parameter.
Step 3 calculates the corresponding accuracy parameter of the reliability assessment result.
Step 4 calculates the corresponding stability parameter of the reliability assessment result.
Step 5, reliability and uncertainty for the component software calculate the not true of the reliability assessment result
Determine parameter.
Step 6 determines the robustness parameter, the accuracy parameter, the stability parameter and the unstability
Credible weighing factor of the parameter to the reliability assessment result.
Step 7, the robustness parameter, the accuracy parameter, the stability parameter and the institute obtained according to step 6
Unstability parameter is stated to the credible weighing factor of the reliability assessment result, obtains the reliability assessment result
Credible parameter.
Step 8 repeats step 1 to step 7, obtains the resulting component software reliability assessment of other reliability assessment schemes
As a result credible parameter.
Step 9, compare the resulting component software reliability assessment result of all reliability assessment schemes assessed can
Letter property parameter, obtains Optimal reliability evaluation scheme.
The specific method of step 6 is as follows:
Step 601, using expert graded, by the robustness parameter, the accuracy parameter, the stability parameter
Compared with being carried out two-by-two with the unstability parameter, Judgement Matricies.
Step 602 calculates coincident indicator, determines the coincident indicator of the judgment matrix.
If the coincident indicator of step 603, the judgment matrix meets condition, the accuracy parameter, described steady is calculated
Qualitative parameter is with the unstability parameter to the credible weighing factor of the reliability assessment result.
If the coincident indicator of the judgment matrix is unsatisfactory for condition, using re-starting expert estimation, consistency adjustment
Method is adjusted, until the coincident indicator of the judgment matrix meets condition, calculates the accuracy parameter, the stabilization
Property parameter and the unstability parameter to the credible weighing factor of the reliability assessment result.
The robustness parameter be assuming that input deviation on the basis of verify the variation model of the reliability assessment result
It encloses, determines that in the probability of acceptable degree, the acquisition of first-order reliability method method is can be used in error originated from input.
The calculation method of the accuracy parameter are as follows:
If the true value of dependability parameter exists,
Wherein E is accuracy parameter, XiFor the assessment result (i=1,2 ..., k) of i-th dependability parameter, s is reliable
The true value of property parameter.
If the true value of dependability parameter is not present:
Wherein E=1/E ' is accuracy parameter, XiFor the assessment result (i=1,2 ..., k) of i-th dependability parameter, s1
And s2The assessment result respectively obtained through other two kinds of reliability assessment schemes.
The condition are as follows: when the judgment matrix order is less than or equal to 2, consistency ration 0；When the judgment matrix
When order is greater than 2, consistency ratio is less than 0.1.
The consistency ratio is the ratio of the coincident indicator and the random index.
Currently both at home and abroad for the evaluation method of reliability assessment result, only from error analysis or sensitivity analysis isogonism
Degree is judged, judges that angle is excessively single, and judging result also lacks credibility, and Rule of judgment is also excessively harsh, can not obtained
In the case where obtaining real reliability result, the quality of evaluation scheme can not be judged.
Therefore, this technology is optimized on the basis of Traditional measurements evaluation of result scheme, proposes four degrees of recognition
Higher reliability assessment evaluation of result Measure Indexes, and comprehensive measurement is carried out to reliability assessment scheme from this four indexs,
Strong guidance is provided for the selection and improvement of evaluation scheme.
Detailed description of the invention
Fig. 1 is the reliability of component software system assessment result assessment technique flow chart based on multi objective measurement.
Specific embodiment
A specific embodiment of the invention is discussed in detail with reference to the accompanying drawing.
Fig. 1 gives the reliability of component software system assessment result assessment technique flow chart based on multi objective measurement.
Specific implementation process is as follows:
Step 1: using certain component software reliability assessment scheme based on architecture to a certain component software system into
Row reliability assessment.
It is specific: component software architecture is established, carries out Member Reliability Analysis assessment, then carry out Reliability evaluation,
Obtain reliability assessment result.
Step 2: being directed to the assessment result of the evaluation scheme, it is corresponding steady to calculate component software reliability assessment result
Property parameter determine error originated from input in acceptable journey that is, assuming that verify the variation range of assessment result on the basis of input deviation
The probability of degree.
Since the input quantity of software reliability evaluation is there may be a degree of uncertain variation, lead to assessment result
Generate corresponding error, therefore use first-order reliability method method, assuming that input deviation on the basis of verify the change of assessment result
Change range, determines influence of the error originated from input to assessment result.
Reliability assessment result may be expressed as:
Y=F (X)
Wherein X=(X1, X2..., Xn)T, indicate the input vector of assessment models, Xi(i=1,2 ..., n) be input quantity；
Y indicates the output vector of assessment models, i.e. assessment result；
The mapping ruler of F expression assessment models.
Assuming that input value X is in true value XdNearby do minor change, it may be assumed that
X=Xd+Xp
Wherein,Indicate the determination part of X, Xp=(Δ X1, Δ X2..., Δ Xn)TIndicate X
Random partial, and have 1) XpMean value be 0；2)|Xp| it is much smaller than | Xd|。
Have at this time:
Y=Yd+Yp
=F (X)
=Fd(X)+Fp(X)
Wherein, Yd=Fd(X)=F (Xd) indicate Y determination part, Yp=Fp(X) random partial of Y is indicated, this part
It is the variation of the assessment result as caused by the random fluctuation of input quantity X, i.e. error originated from input, therefore equally has 1) YpMean value be 0；2)
|Yp| it is much smaller than | Yd|。
To mapping Y=F (X) in XdSingle order Taylor expansion is done at place, is had:
WhereinWithRespectivelyWith(i=1,2 ..., slightly writing n).
By Y=Fd(X)+Fp(X) it can obtain:
By statistical analysis it is found that the expectation of output quantity Y:
μF=E [F (X)]=E [Fd(X)]+E(Fp(X)]=Fd(X)=F (Xd)
The variance of output quantity Y:
Assuming that Y approximation Normal Distribution, i.e.,
If the acceptable degree of error originated from input is defined as follows: when the mobility scale of input quantity X is 10%, causing to export
The error originated from input range for measuring Y is not more than 10%.Then the probability of such case is
Wherein
μF=F (Xd)
This probability value is higher, illustrates that the error originated from input of assessment result is more possible to be limited in acceptable degree, i.e.,
As a result robustness is better.
It has been generally acknowledged that the robustness of assessment result is preferable, takes P as robustness parameter here when P >=80%.
Step 3: being directed to the assessment result of the evaluation scheme, it is corresponding accurate to calculate component software reliability assessment result
Property parameter.
Software reliability evaluation result can be considered as the stochastic variable for obeying certain specific distribution rule.Assuming that same soft
Part or software systems carry out k reliability assessment, and acquired results are respectively X1、X2……Xk, then i-th assessment result is absolute
Error may be expressed as:
ei=| Xi-s|
Wherein s indicates the true value of reliability, has at this time:
WhereinIndicate the arithmetic mean of instantaneous value of k assessment result；Indicate i-th assessment result relative to assessment mould
Type remove uncertainty after standard results error, i.e. model error；Indicate that the standard results of assessment models are opposite
In the error of true value, due to being computed error originated from input and model error, this is shown as random error.
The standard of evaluation model error size is stability, i.e. model error is smaller, and the stability of assessment result is better；It comments
The standard of valence random error size is accuracy, i.e. random error is smaller, and the accuracy of assessment result is better.
The accuracy of evaluation result can be by carrying out the result obtained after repeatedly assessing to same software or software systems
Relative difference between average value and true value indicates, it is contemplated that certain softwares or the dependability parameter true value of software systems without
Method obtains or acquisition difficulty is larger, and true value herein can be divided into following two situation:
In the case where it can obtain dependability parameter true value, as there is certain be specifically intended for verifying software reliability
When data, the true value that dependability parameter can be used is evaluated, i.e.,
Wherein E is accuracy parameter, XiFor the assessment result (i=1,2 ..., k) of i-th dependability parameter, s is reliable
The true value of property parameter；
When true value can not be obtained, two outside this assessment models method kinds of reliability estimation methods can be removed and divided
It does not assess, and takes the average value of the two result as true reference value.Since many existing reliability estimation methods are through too long
Phase practice test has certain reference value, therefore can be used as and represent true value with reference to approximate, at this time:
Wherein E=1/E ' is accuracy parameter, XiFor the assessment result (i=1,2 ..., k) of i-th dependability parameter, s1
And s2The assessment result respectively obtained via other two kinds of appraisal procedures.
As E≤30%, it is believed that the accuracy of assessment result is preferable.
Step 4: being directed to the assessment result of the evaluation scheme, the corresponding stabilization of component software reliability assessment result is calculated
Property parameter.
The stability of software reliability evaluation result can be determined the phase of model error and random error by method of analysis of variance
To size, then within an acceptable range whether the ratio of judgment models error and random error.
To m different situations or it is horizontal under software or software systems reliability do n times reliability assessment respectively, obtain m
× n sample, record are as follows:
Reliability assessment distribution of results table under 1 different level of table
Then have:
ST=SE+SA
Wherein:
S at this timeEIt reflects to the dispersion degree of software or software systems assessment result under same situation, represents and assessed
The random error of journey；SAAssessment result dispersion degree caused by the different situations of software or software systems is reflected, assessment is represented
The error of model itself.If SANoticeably greater than SE, illustrate the model error of assessment beyond due level.
, sample stochastic variable approximation Normal Distribution assuming that whole samples are all satisfied following three conditions: 1)；2), each
Sample variance of a random variable is identical；3), mutually indepedent between each sample.It enables:
Enabling statistic S is stability parameter, obeys the F that distribution freedom degree is (m-1, m (n-1)) and is distributed.
Conspicuousness factor alpha=0.95 is taken, as S≤F0.05When (m-1, m (n-1)), it is believed that model error is in tolerance interval
Interior, the stability of assessment result is preferable.
Common F distribution table (α=0.95) is as follows:
F distribution table when 2 conspicuousness factor alpha of table=0.95
Step 5: according to the reliability and its uncertainty of component, the uncertainty of computing system reliability assessment result
Parameter.
Due to the function of component software and the reliability assessment of system be it is non-linear and uncorrelated between each input quantity, because
This can be become the measurement model of approximately linear, the combined standard uncertainty of measured estimated value y by Taylor series expansion
ucIt (y) include the higher order term in taylor series expansion in expression formula.As each input quantity XiWhen being all normal distribution, consider
Reliability evaluation result uncertainty formula after higher order term calculates are as follows:
It willIt substitutes into above formula, can be obtained based on uncertainty index
Software systems reliability assessment evaluation of result formula is uc(y):
Wherein, R1, R2..., RnFor each component software reliability evaluation as a result, μ (Ri) it is that each component is reliable
The property corresponding uncertainty of assessment result.Generally, it is considered that the smaller reliability assessment result of uncertainty is more credible, uncertainty is taken
Parameter
Step 6: determining robustness, accuracy, stability and uncertainty to reliability assessment using tournament method
As a result the credible weight influenced.
601st step: expert graded is used, four factors are compared two-by-two, Judgement Matricies.
4 robustness, accuracy, stability and uncertainty factors are compared two-by-two, use aijExpression factor AiThan
Factor AjOpposite significance level, specific scale are shown in Table 3:
3 expert estimation scale of table
It comparison result will be listed in judgment matrix two-by-two:
602nd step: coincident indicator, the consistency of judgment matrix are calculated.
In the construction of judgment matrix, it is not required that judge consistency, this is because the complexity of objective affairs and people
Understanding diversity is determined, but requires to judge there is consistency substantially, that is, excludes some judgements for violating common sense.When judgement is inclined
When excessive from consistency, the result calculated certain problems will occur as decision-making foundation.Therefore it needs to carry out consistency inspection
It tests, its step are as follows:
Calculate coincident indicator(m is the order of judgment matrix, λmaxFor the maximum feature of judgment matrix
Value), corresponding random index R.I. is searched, and calculate consistency ratioAs C.R. < 0.1, it is believed that
The inconsistent degree of matrix can receive, and otherwise need to reconfigure judgment matrix.It is wherein always consistent for one, second-order matrix
, i.e. C.R.=0.
4 random index R.I of table
| m | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
| R.I | 0 | 0 | 0.58 | 0.90 | 1.21 | 1.24 | 1.32 | 1.41 | 1.45 | 1.49 |
603rd step: calculating four factors when the condition for consistence of judgment matrix meets can to software reliability evaluation result
The weighing factor for relative importance i.e. four factor that letter property influences.
Every a line of judgment matrix is separately summed, then (each single item is divided by row each in column by the normalization of resulting column vector
The sum of) to get the relative Link Importance of each factor A1, A2, A3, A4 out, successively sort.
604th step: it when the coincident indicator of judgment matrix is unsatisfactory for, is given a mark again or using consistency adjustment method
Consistency adjustment is carried out, four factors are recalculated when meeting coincident indicator to software reliability evaluation credible result shadow
Ring weight.
Step 7: being obtained according to stability, accuracy, robustness, uncertainty parameter calculated result and its weighing factor
Using the credibility for the software reliability evaluation result that the program calculates.
Step 8: repeating the first to the 6th step.When calculating using other reliability assessment schemes progress reliability assessment
Credible parameter.
Step 9: the credible calculated result of more each scheme, obtains optimal case.
Confidence level is higher, illustrates that evaluation scheme is more excellent.
Claims (5)
1. the credible evaluation method of the reliability assessment result based on multi objective measurement, specifically includes the following steps:
Step 1 carries out reliability assessment to component software using reliability assessment scheme, obtains reliability assessment result；
Step 2, for the reliability assessment as a result, calculating its corresponding robustness parameter；
Step 3 calculates the corresponding accuracy parameter of the reliability assessment result；
Step 4 calculates the corresponding stability parameter of the reliability assessment result；
Step 5, reliability and uncertainty for the component software, calculate the uncertain ginseng of the reliability assessment result
Number；
Step 6 determines the robustness parameter, the accuracy parameter, the stability parameter and the unstability parameter
To the credible weighing factor of the reliability assessment result；
Step 7, the robustness parameter obtained according to step 6, the accuracy parameter, the stability parameter and it is described not
Stability parameter obtains the credible of the reliability assessment result to the credible weighing factor of the reliability assessment result
Property parameter；
Step 8 repeats step 1 to step 7, obtains the resulting component software reliability assessment result of other reliability assessment schemes
Credible parameter；
Step 9, the credibility for comparing the resulting component software reliability assessment result of all reliability assessment schemes assessed
Parameter obtains Optimal reliability evaluation scheme.
2. the credible evaluation method of the reliability assessment result according to claim 1 based on multi objective measurement, feature
It is, the specific method of step 6 is as follows:
Step 601, using expert graded, by the robustness parameter, the accuracy parameter, the stability parameter and institute
It states unstability parameter to be compared two-by-two, Judgement Matricies；
Step 602 calculates coincident indicator, determines the coincident indicator of the judgment matrix；
If the coincident indicator of step 603, the judgment matrix meets condition, the accuracy parameter, the stability are calculated
Parameter is with the unstability parameter to the credible weighing factor of the reliability assessment result；
If the coincident indicator of the judgment matrix is unsatisfactory for condition, using re-starting expert estimation, consistency adjustment method
It is adjusted, until the coincident indicator of the judgment matrix meets condition, calculates the accuracy parameter, stability ginseng
Credible weighing factor of the several and unstability parameter to the reliability assessment result.
3. the credible evaluation method of the reliability assessment result according to claim 1 based on multi objective measurement, feature
Be, the robustness parameter be assuming that input deviation on the basis of verify the variation range of the reliability assessment result,
Determine that in the probability of acceptable degree, the acquisition of first-order reliability method method is can be used in error originated from input.
4. the credible evaluation method of the reliability assessment result according to claim 1 based on multi objective measurement, feature
It is, the calculation method of the accuracy parameter are as follows:
If the true value of dependability parameter exists,
Wherein E is accuracy parameter, XiFor the assessment result (i=1,2 ..., k) of i-th dependability parameter, s is dependability parameter
True value；
If the true value of dependability parameter is not present:
Wherein E=1/E ' is accuracy parameter, XiFor the assessment result (i=1,2 ..., k) of i-th dependability parameter, s1And s2
The assessment result respectively obtained through other two kinds of reliability assessment schemes.
5. the credible evaluation method of the reliability assessment result according to claim 2 based on multi objective measurement, feature
It is, the condition are as follows: when the judgment matrix order is less than or equal to 2, consistency ration 0；When the judgment matrix rank
When number is greater than 2, consistency ratio is less than 0.1；
The consistency ratio is the ratio of the coincident indicator and the random index.
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