CN106776316A - A kind of power information software defect prevention method - Google Patents
A kind of power information software defect prevention method Download PDFInfo
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- CN106776316A CN106776316A CN201611157188.6A CN201611157188A CN106776316A CN 106776316 A CN106776316 A CN 106776316A CN 201611157188 A CN201611157188 A CN 201611157188A CN 106776316 A CN106776316 A CN 106776316A
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- 230000007547 defect Effects 0.000 title claims abstract description 181
- 238000000034 method Methods 0.000 title claims abstract description 44
- 230000002265 prevention Effects 0.000 title claims abstract description 38
- 238000012360 testing method Methods 0.000 claims abstract description 10
- 238000011161 development Methods 0.000 claims abstract description 7
- 230000000694 effects Effects 0.000 claims description 11
- 238000013461 design Methods 0.000 claims description 9
- 230000002950 deficient Effects 0.000 claims description 4
- 238000000605 extraction Methods 0.000 claims description 3
- 238000005259 measurement Methods 0.000 claims description 3
- 230000005611 electricity Effects 0.000 claims 1
- 238000010276 construction Methods 0.000 abstract description 6
- 238000012986 modification Methods 0.000 abstract description 3
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- 238000013528 artificial neural network Methods 0.000 description 2
- 239000012141 concentrate Substances 0.000 description 2
- 238000012706 support-vector machine Methods 0.000 description 2
- 230000003044 adaptive effect Effects 0.000 description 1
- 238000007621 cluster analysis Methods 0.000 description 1
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- 238000005516 engineering process Methods 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/36—Preventing errors by testing or debugging software
- G06F11/3604—Software analysis for verifying properties of programs
- G06F11/3608—Software analysis for verifying properties of programs using formal methods, e.g. model checking, abstract interpretation
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Abstract
The present invention provides a kind of power information software defect prevention method, and the metric of the metric in each stage and development process in power information software life-cycle is input into AODE forecast models by the method, calculates the defect concentration in each stage；Power information software defect knowledge base is then looked up, each stage defect is obtained and is introduced reason and corresponding precautionary measures；It is last pointedly to carry out Defect prevention, reduce defect concentration.The power information software defect prevention method that the present invention is provided can carry out Defect prevention and provide method and technical foundation for power information system construction, the introducing of defect can be in early days prevented in power information system construction, reduce defect introducing rate, reduce the repeatability work of " defect introducing test modifications ", and the specific aim of Defect prevention can be effectively increased, Exploitation of Information System Project expense is saved, the failure risk for reducing software systems integrally lifts power information software entirety construction quality.
Description
Technical field
The present invention relates to a kind of prevention method, and in particular to a kind of power information software defect prevention method.
Background technology
Failure prediction is the basis of prevention, and Accurate Prediction could concentrate the limited resource to carry out specific aim prevention to defect.
According to the difference of Predicting Technique, it is big that failure prediction method can be divided into analogy method, Delph estimation algorithms, mathematical forecasting model method three
Class method, but because first two method has the limitation that cannot be overcome, the research work in current software defect prediction field
Major part is to concentrate on mathematical forecasting model method.
Common mathematical forecasting model method has：Linear discriminant analysis (Linear Discriminant Analysis,
LDA), boolean's discriminant function (Boolean Discriminant Function, BDF), Bayesian network (Bayesian
Network, BN), post-class processing (Classification And Regression Tree, CART), optimization collection is simplified
(Optimized Set Reduce, OSR), cluster analysis (Clustering Analysis, CA), SVMs
(Support Vector Machine, SVM), artificial neural network (Artificial Neural Network, ANN), averagely
Single related evaluator (Average One Dependence Estimators, AODE) etc..Various Forecasting Methodologies have difference
Adaptive surface, can solve the problems, such as different, also have respective limitation.
So far, 100 number of drawbacks forecast models are published on the publication and academic conference of various specialties both at home and abroad,
But most models do not obtain the support and checking of sufficient project data, and its validity and accuracy cannot be effectively
Prove, thus fail extensive use.
The content of the invention
In order to overcome the above-mentioned deficiencies of the prior art, the present invention provides a kind of power information software defect prevention method, will
The metric input AODE forecast models of the metric in each stage and development process in power information software life-cycle,
Calculate the defect concentration in each stage；Power information software defect knowledge base is then looked up, each stage defect is obtained and is introduced reason
And corresponding precautionary measures；It is last pointedly to carry out Defect prevention, reduce defect concentration.
In order to realize foregoing invention purpose, the present invention is adopted the following technical scheme that：
The present invention provides a kind of power information software defect prevention method, and methods described includes：
The defect concentration in power information each stage of software life-cycle is calculated according to AODE forecast models；
Each stage defect introducing of power information software life-cycle is obtained according to power information software defect knowledge base former
Cause and corresponding precautionary measures.
The defect concentration for calculating the power information software life-cycle stage according to AODE forecast models includes：
Build AODE forecast models, and to build AODE forecast models optimize；
The metric of the metric in power information each stage of software life-cycle and development process is input into AODE
Forecast model, and then obtain the defect concentration in power information each stage of software life-cycle.
The structure AODE forecast models include：
The AODE forecast models are expressed as：
Wherein, y represents code attribute value, xiRepresent i-th code attribute value of attribute vector, F (xi) represent that there is xi's
Example number；XjRepresent j-th attribute vector；I=1,2 ..., n, j=1,2 ..., n, n represent code attribute value sum；m
Represent that there is xiInstance number purpose threshold value；Represent y and xiProduce the probability of power information software defect；
Represent y and xiIn XjThe probability of middle generation power information software defect.
Described pair structure AODE forecast models optimize including：
Using increase measurement metric number, the collection of control metric data and/or adjustment I classes/II classes error rate to structure
AODE forecast models are optimized.
Described each stage defect according to power information software defect knowledge base acquisition power information software life-cycle is drawn
Entering reason and corresponding precautionary measures includes：
Build power information software defect knowledge base；
Power information software life-cycle each stage defect is obtained according to the power information software defect knowledge base for building
Introduce reason and corresponding precautionary measures.
The structure power information software defect knowledge base includes：
The historical data of collection；
Code attribute value is extracted from the historical data collected；
The code attribute value of extraction is combined with the description information in test report, so as to build power information software lack
Fall into knowledge base.
The code attribute value includes defect type number, the defect counts per in class defect, defect state quantity and lacks
Fall into number of species；
Description information in test report includes problem report and defect report of accessment and test.
It is described that power information each stage of software life-cycle is obtained according to the power information software defect knowledge base for building
Defect introduces reason and corresponding precautionary measures to be included：
It was found that and when submitting power information software defect to, collected according to defect and require record code property value；
When repairing or responding power information software defect, according to minimum defective data token record defect source, defect type
And defect carrier attribute value, while recording reclamation activities and the repairing effect for repairing that power information software defect is taken；
Carry out Bug Tracking, trace back to the introducing stage of defect, analyze and determine defect there may be the reason for, and combine
Reclamation activities and repairing effect are analyzed to the measure for preventing defect, while recording, defect introduces reason and corresponding prevention is arranged
Apply；
Aforesaid operations are repeated, power information software defect data set is obtained.
The power information software defect of the power information software defect data centralized recording includes that demand defect, design lack
Fall into and coding defect.
Described each stage defect according to power information software defect knowledge base acquisition power information software life-cycle is drawn
To enter include after reason and corresponding precautionary measures：
Supplemented and screened by defect introducing reason and corresponding precautionary measures, be finally completed power information software
The prevention of defect.
Compared with immediate prior art, the technical scheme that the present invention is provided has the advantages that：
The power information software defect prevention method that the present invention is provided, by each stage in power information software life-cycle
Metric and development process metric input AODE forecast models, calculate the defect concentration in each stage；Then look up
Power information software defect knowledge base, obtains each stage defect and introduces reason and corresponding precautionary measures；Pointedly finally
Defect prevention is carried out, defect concentration is reduced.The power information software defect prevention method that the present invention is provided can be power information system
Construction in a systematic way sets development Defect prevention and provides method and technical foundation, and the introducing of defect can be in early days prevented in power information system construction,
Defect introducing rate is reduced, the repeatability work of " defect introduces-test-modification " is reduced, and Defect prevention can be effectively increased
Specific aim, save Exploitation of Information System Project expense, reduce software systems failure risk integrally lift power information software
Overall construction quality.
Brief description of the drawings
Fig. 1 is power information software defect prevention method flow chart in the embodiment of the present invention.
Specific embodiment
The present invention is described in further detail below in conjunction with the accompanying drawings.
The present invention provides a kind of power information software defect prevention method, as shown in figure 1, methods described includes：
The defect concentration in power information each stage of software life-cycle is calculated according to AODE forecast models；
Each stage defect introducing of power information software life-cycle is obtained according to power information software defect knowledge base former
Cause and corresponding precautionary measures.
The defect concentration for calculating the power information software life-cycle stage according to AODE forecast models includes：
Build AODE forecast models, and to build AODE forecast models optimize；
The metric of the metric in power information each stage of software life-cycle and development process is input into AODE
Forecast model, and then obtain the defect concentration in power information each stage of software life-cycle.
The structure AODE forecast models include：
The AODE forecast models are expressed as：
Wherein, y represents code attribute value, xiRepresent i-th code attribute value of attribute vector, F (xi) represent that there is xi's
Example number；XjRepresent j-th attribute vector；I=1,2 ..., n, j=1,2 ..., n, n represent code attribute value sum；m
Represent that there is xiInstance number purpose threshold value；Represent y and xiProduce the probability of power information software defect；
Represent y and xiIn XjThe probability of middle generation power information software defect.
Described pair structure AODE forecast models optimize including：
Using increase measurement metric number, the collection of control metric data and/or adjustment I classes/II classes error rate to structure
AODE forecast models are optimized.
Described each stage defect according to power information software defect knowledge base acquisition power information software life-cycle is drawn
Entering reason and corresponding precautionary measures includes：
Build power information software defect knowledge base；
Power information software life-cycle each stage defect is obtained according to the power information software defect knowledge base for building
Introduce reason and corresponding precautionary measures.
The structure power information software defect knowledge base includes：
The historical data of collection；
Code attribute value is extracted from the historical data collected；
The code attribute value of extraction is combined with the description information in test report, so as to build power information software lack
Fall into knowledge base.
The code attribute value includes defect type number, the defect counts per in class defect, defect state quantity and lacks
Fall into number of species；
Description information in test report includes problem report and defect report of accessment and test.
It is described that power information each stage of software life-cycle is obtained according to the power information software defect knowledge base for building
Defect introduces reason and corresponding precautionary measures to be included：
It was found that and when submitting power information software defect to, collected according to defect and require record code property value；
When repairing or responding power information software defect, according to minimum defective data token record defect source, defect type
And defect carrier attribute value, while recording reclamation activities and the repairing effect for repairing that power information software defect is taken；
Carry out Bug Tracking, trace back to the introducing stage of defect, analyze and determine defect there may be the reason for, and combine
Reclamation activities and repairing effect are analyzed to the measure for preventing defect, while recording, defect introduces reason and corresponding prevention is arranged
Apply；
Aforesaid operations are repeated, power information software defect data set is obtained.
The power information software defect of the power information software defect data centralized recording includes that demand defect, design lack
Fall into and coding defect.
Whole software development process can be divided into demand, design, encode 3 stages, not only coding stage there may be
Defect, equally there is the possibility for introducing defect in demand and design phase, and the defect introduced in the two stages even can band
More to bother.In order to explicitly known defect is specifically to be caused by which in stage, it would be desirable to which the source to defect is entered
Row classification：
(1) demand defect：Defect caused by demand, shows as function, interface between software and hardware or user interface definition
Mistake etc.；
(2) design defect：Defect caused by design, shows as data definition, modelling or Interface design wrong
By mistake etc.；
(3) defect is encoded：Defect caused by coding, shows as that code is inconsistent with demand/design, code is patrolled
Volume, specification mistake etc..
Described each stage defect according to power information software defect knowledge base acquisition power information software life-cycle is drawn
To enter include after reason and corresponding precautionary measures：
Supplemented and screened by defect introducing reason and corresponding precautionary measures, be finally completed power information software
The prevention of defect.
Finally it should be noted that：The above embodiments are merely illustrative of the technical solutions of the present invention rather than its limitations, institute
The those of ordinary skill in category field specific embodiment of the invention can still be modified with reference to above-described embodiment or
Equivalent, these are applying for this pending hair without departing from any modification of spirit and scope of the invention or equivalent
Within bright claims.
Claims (10)
1. a kind of power information software defect prevention method, it is characterised in that：Methods described includes：
The defect concentration in power information each stage of software life-cycle is calculated according to AODE forecast models；
According to power information software defect knowledge base obtain power information software life-cycle each stage defect introduce reason and
Corresponding precautionary measures.
2. power information software defect prevention method according to claim 1, it is characterised in that：It is described to be predicted according to AODE
The defect concentration that model calculates the power information software life-cycle stage includes：
Build AODE forecast models, and to build AODE forecast models optimize；
By the metric input AODE predictions of the metric in power information each stage of software life-cycle and development process
Model, and then obtain the defect concentration in power information each stage of software life-cycle.
3. power information software defect prevention method according to claim 2, it is characterised in that：The structure AODE predictions
Model includes：
The AODE forecast models are expressed as：
Wherein, y represents code attribute value, xiRepresent i-th code attribute value of attribute vector, F (xi) represent that there is xiExample
Number；XjRepresent j-th attribute vector；I=1,2 ..., n, j=1,2 ..., n, n represent code attribute value sum；M is represented
With xiInstance number purpose threshold value；Represent y and xiProduce the probability of power information software defect；Represent y
And xiIn XjThe probability of middle generation power information software defect.
4. power information software defect prevention method according to claim 2, it is characterised in that：The described couple of AODE of structure
Forecast model optimize including：
Using increasing measurement metric number, the collection of control metric data and/or adjustment I classes/II classes error rate to the AODE that builds
Forecast model is optimized.
5. power information software defect prevention method according to claim 1, it is characterised in that：It is described according to power information
Software defect knowledge base obtains power information software life-cycle each stage defect introducing reason and corresponding precautionary measures bag
Include：
Build power information software defect knowledge base；
Each stage defect introducing of power information software life-cycle is obtained according to the power information software defect knowledge base for building
Reason and corresponding precautionary measures.
6. power information software defect prevention method according to claim 5, it is characterised in that：The structure power information
Software defect knowledge base includes：
The historical data of collection；
Code attribute value is extracted from the historical data collected；
The code attribute value of extraction is combined with the description information in test report, so as to build power information software defect know
Know storehouse.
7. power information software defect prevention method according to claim 6, it is characterised in that：The code attribute value bag
Include defect type number, the defect counts per in class defect, defect state quantity and defect kind quantity；
Description information in test report includes problem report and defect report of accessment and test.
8. power information software defect prevention method according to claim 1, it is characterised in that：The electricity according to structure
Force information software defect knowledge base obtains power information software life-cycle each stage defect introducing reason and corresponding prevention
Measure includes：
It was found that and when submitting power information software defect to, collected according to defect and require record code property value；
Repair or respond power information software defect when, according to minimum defective data token record defect source, defect type and
Defect carrier attribute value, while recording reclamation activities and the repairing effect for repairing that power information software defect is taken；
Carry out Bug Tracking, trace back to the introducing stage of defect, analyze and determine defect there may be the reason for, and combine repair
Measure and repairing effect are analyzed to the measure for preventing defect, while recording defect introduces reason and corresponding precautionary measures；
Aforesaid operations are repeated, power information software defect data set is obtained.
9. power information software defect prevention method according to claim 8, it is characterised in that：The power information software
The power information software defect of defective data centralized recording includes demand defect, design defect and coding defect.
10. the power information software defect prevention method according to claim 1 or 8, it is characterised in that：It is described according to electric power
Information software defect knowledge base obtains each stage defect of power information software life-cycle introducing reason and corresponding prevention is arranged
Include after applying：
Supplemented and screened by defect introducing reason and corresponding precautionary measures, be finally completed power information software defect
Prevention.
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN108154287A (en) * | 2017-12-06 | 2018-06-12 | 千寻位置网络有限公司 | The analyzing evaluation method of software system development quality |
| CN110084282A (en) * | 2019-04-01 | 2019-08-02 | 昆明理工大学 | One kind being used for metal plates and strips defect image classification method |
| CN111429003A (en) * | 2020-03-23 | 2020-07-17 | 北京互金新融科技有限公司 | Data processing method and device |
| CN112115045A (en) * | 2020-08-19 | 2020-12-22 | 北京航空航天大学 | Failure prediction method for complex software system |
| CN117093504A (en) * | 2023-10-19 | 2023-11-21 | 广东省城乡规划设计研究院有限责任公司 | Software quality control method for defect measurement |
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Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108154287A (en) * | 2017-12-06 | 2018-06-12 | 千寻位置网络有限公司 | The analyzing evaluation method of software system development quality |
| CN110084282A (en) * | 2019-04-01 | 2019-08-02 | 昆明理工大学 | One kind being used for metal plates and strips defect image classification method |
| CN110084282B (en) * | 2019-04-01 | 2021-04-02 | 昆明理工大学 | Defect image classification method for metal plate strip |
| CN111429003A (en) * | 2020-03-23 | 2020-07-17 | 北京互金新融科技有限公司 | Data processing method and device |
| CN111429003B (en) * | 2020-03-23 | 2023-11-03 | 北京互金新融科技有限公司 | Data processing method and device |
| CN112115045A (en) * | 2020-08-19 | 2020-12-22 | 北京航空航天大学 | Failure prediction method for complex software system |
| CN112115045B (en) * | 2020-08-19 | 2022-03-18 | 北京航空航天大学 | Failure prediction method for complex software system |
| CN117093504A (en) * | 2023-10-19 | 2023-11-21 | 广东省城乡规划设计研究院有限责任公司 | Software quality control method for defect measurement |
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Application publication date: 20170531 |