CN109240929A - Software quality prediction method, apparatus, terminal and computer readable storage medium - Google Patents
Software quality prediction method, apparatus, terminal and computer readable storage medium Download PDFInfo
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Abstract
The embodiment of the present invention proposes a kind of software quality prediction method, apparatus, terminal and computer readable storage medium, and method includes: to obtain the characteristic of multiple classifications of the software to be predicted in software development flow；Characteristic of all categories is pre-processed, and is input in quality prediction model；Quality prediction model is the model constructed according to the history feature data of reverse transmittance nerve network and multiple classifications；Quality prediction model handles characteristic of all categories, and exports the prediction result of software to be predicted.For the embodiment of the present invention according to the index of correlation for influencing software quality, the characteristic of each classification in integrated software development process quickly and comprehensively carries out software quality prediction, guarantees prediction accuracy.
Description
Technical field
The present invention relates to field of information security technology more particularly to a kind of software quality prediction method, apparatus, terminal and meter
Calculation machine readable storage medium storing program for executing.
Background technique
In the prior art, the method that all kinds of index factors carry out software quality prediction is not integrated.It is using simple
Index factor or the mode of human intervention carry out software quality prediction.Due to influence software quality factor be it is various,
It is less rationally that the index directly quantified for code revision line number or code coverage etc. only is simplified in the prediction of software quality
, because these factors can not all embody the software quality for being really, error in judgement is larger.And dependent on software test person's test
Method poor operability, there are very big differences for the result of personnel's test of different experiences.And it is very big rich with internet product
Richness faces mass data, and it is unpractical for carrying out test by people merely.
Disclosed above- mentioned information are only used for reinforcing the understanding to background of the invention in the background technology, therefore it may be wrapped
Containing the information for not being formed as the prior art that those of ordinary skill in the art are known.
Summary of the invention
The embodiment of the present invention provides a kind of software quality prediction method, apparatus, terminal and computer readable storage medium, with
Solve one or more technical problems in the prior art.
In a first aspect, the embodiment of the invention provides a kind of software quality prediction methods, comprising:
Obtain the characteristic of multiple classifications of the software to be predicted in software development flow；
The characteristic of each classification is pre-processed, and is input in quality prediction model；The prediction of quality
Model is the model constructed according to the history feature data of reverse transmittance nerve network and multiple classifications；
The quality prediction model handles the characteristic of each classification, and exports the software to be predicted
Prediction result.
With reference to first aspect, the embodiment of the present invention is in the first implementation of first aspect, the characteristic
Classification includes modular character, demand characteristic, development features, test feature, issues on online feature and line in O&M feature
At least one of.
With reference to first aspect, the embodiment of the present invention is in second of implementation of first aspect, further includes:
From several softwares in the historical data of software development process, the history feature number of multiple classifications is obtained
According to；
The history feature data of each classification are pre-processed；
Using the history feature data of pretreated each classification as training sample, it is based on the Back propagation neural
Network carries out model training, obtains the quality prediction model.
With reference to first aspect, the embodiment of the present invention obtains software to be predicted in the third implementation of first aspect
The characteristic of multiple classifications in software development flow, comprising:
Obtain the initial data of the multiple classifications of the software to be predicted in software development flow；
The initial data of each classification passes through corresponding Random Forest model respectively and is screened, to obtain each class
Another characteristic data.
With reference to first aspect, the embodiment of the present invention is in the 4th kind of implementation of first aspect, further includes:
Obtain the newest history feature data of each classification；
The quality prediction model carries out self study according to the newest history feature data, updates the matter with optimization
Measure prediction model.
The first implementation with reference to first aspect, five kind implementation of the embodiment of the present invention in first aspect
In, the characteristic of each classification includes behavioral characteristics and static nature, it specifically includes at least one of following:
Modular character includes module behavioral characteristics and module static nature；Wherein, module behavioral characteristics include: module upgrade
At least one of the iteration frequency, module failure, module efficiency and mass ratio；Module static nature includes: the important journey of module
Degree scoring, module are relied at least one of degree scoring and the scoring of module degree of dependence；
Demand characteristic includes demand behavioral characteristics and demand static nature；Wherein, demand behavioral characteristics include: answering for demand
At least one of miscellaneous degree, the degree of perfection of requirement documents and demand evaluation number；Demand static nature includes: that demand is begged for
At least one of the effective time of opinion and the index of demand raiser；
Development features include development behavior feature and exploitation static nature；Wherein, development behavior feature includes: code review
At least one of number and continuous integrating number；Developing static nature includes: program language, development time, developer
Index changes lines of code, code annotation line number and surveys at least one of coverage rate certainly；
Test feature includes test behavioral characteristics and test static nature；Wherein, test behavioral characteristics include: test sexual valence
Than；Test static nature includes: automatic test cases percent of pass, lines of code coverage rate and static code scanning problem number
At least one of；
Issuing online feature includes issuing online behavioral characteristics and the online static nature of publication；Wherein, online dynamic is issued
Feature includes: previous problem interception rate；Issuing online static nature includes: that classification publication sum of series classification publication validation test is used
At least one of example；
O&M feature includes O&M static nature on O&M behavioral characteristics and line on line on line；Wherein, O&M dynamic on line
Feature includes: to monitor maturity on line, stop loss at least one of maturity automatically on problem recall rate and line on line；On line
O&M static nature includes: at least one of containerization deployment and automation O&M maturity.
Second aspect, the embodiment of the invention provides a kind of software quality prediction devices, comprising:
Characteristic obtains module, for obtaining the characteristic of multiple classifications of the software to be predicted in software development flow
According to；
First preprocessing module for pre-processing the characteristic of each classification, and is input to prediction of quality
In model；The quality prediction model is constructed according to the history feature data of reverse transmittance nerve network and multiple classifications
Model；
Prediction module, for being handled by characteristic of the quality prediction model to each classification, and it is defeated
The prediction result of the software to be predicted out.
In a possible design, further includes:
History feature data acquisition module, in the historical data of software development process, being obtained from several softwares
The history feature data of multiple classifications；
Second preprocessing module is pre-processed for the history feature data to each classification；
Model construction module, for using the history feature data of pretreated each classification as training sample, base
Model training is carried out in the reverse transmittance nerve network, obtains the quality prediction model.
In a possible design, the characteristic obtains module and includes:
Acquisition submodule, for obtaining the original of the multiple classifications of the software to be predicted in software development flow
Data；
Submodule is screened, is sieved for the initial data of each classification to be passed through corresponding Random Forest model respectively
Choosing, to obtain the characteristic of each classification.
In a possible design, further includes:
Module is obtained, for obtaining the newest history feature data of each classification；
Update module carries out self study according to the newest history feature data for the quality prediction model, with
Optimization updates the quality prediction model.
The third aspect, the embodiment of the invention provides a kind of terminals of software quality prediction, comprising:
The function can also execute corresponding software realization by hardware realization by hardware.The hardware or
Software includes one or more modules corresponding with above-mentioned function.
It is described including processor and memory in the structure of the terminal of software quality prediction in a possible design
Memory is used to store the journey for supporting the terminal of software quality prediction to execute the method for software quality prediction in above-mentioned first aspect
Sequence, the processor is configured to for executing the program stored in the memory.The terminal of software quality prediction can be with
Including communication interface, terminal and other equipment or communication for software quality prediction.
Fourth aspect, it is pre- for storing software quality the embodiment of the invention provides a kind of computer readable storage medium
Computer software instructions used in the terminal of survey comprising the method for executing software quality prediction in above-mentioned first aspect is
Program involved in the terminal of software quality prediction.
A technical solution in above-mentioned technical proposal has the following advantages that or the utility model has the advantages that according to software quality is influenced
Index of correlation, the characteristic of each classification in integrated software development process, quickly and comprehensively carries out software quality prediction,
Guarantee prediction accuracy.
Above-mentioned general introduction is merely to illustrate that the purpose of book, it is not intended to be limited in any way.Except foregoing description
Schematical aspect, except embodiment and feature, by reference to attached drawing and the following detailed description, the present invention is further
Aspect, embodiment and feature, which will be, to be readily apparent that.
Detailed description of the invention
In the accompanying drawings, unless specified otherwise herein, otherwise indicate the same or similar through the identical appended drawing reference of multiple attached drawings
Component or element.What these attached drawings were not necessarily to scale.It should be understood that these attached drawings depict only according to the present invention
Disclosed some embodiments, and should not serve to limit the scope of the present invention.
Fig. 1 is the flow chart for the software quality prediction method that embodiment of the present invention provides.
Fig. 2 is the flow chart for the acquisition characteristic that embodiment of the present invention provides.
Fig. 3 is the flow chart for the building Random Forest model that embodiment of the present invention provides.
Fig. 4 is the structural block diagram that the characteristic that embodiment of the present invention provides obtains.
Fig. 5 is the structural block diagram for the software quality prediction method that embodiment of the present invention provides.
Fig. 6 is the flow chart for the building quality prediction model that embodiment of the present invention provides.
Fig. 7 is the flow chart for the quality prediction model optimization that embodiment of the present invention provides.
Fig. 8 is the schematic diagram for six category feature data input quality prediction models that embodiment of the present invention provides.
Fig. 9 is the structural schematic diagram for the quality prediction model network structure that embodiment of the present invention provides.
Figure 10 is the structural schematic diagram for the software quality prediction device that embodiment of the present invention provides.
Figure 11 is the structural schematic diagram for the software quality prediction device that one embodiment of the present invention provides.
Figure 12 is the structural schematic diagram that the characteristic that embodiment of the present invention provides obtains module.
Figure 13 is the structural schematic diagram for the software quality prediction device that one embodiment of the present invention provides.
Figure 14 is the structural schematic diagram for the software quality prediction terminal that embodiment of the present invention provides.
Specific embodiment
Hereinafter, certain exemplary embodiments are simply just described.As one skilled in the art will recognize that
Like that, without departing from the spirit or scope of the present invention, described embodiment can be modified by various different modes.
Therefore, attached drawing and description are considered essentially illustrative rather than restrictive.
The embodiment of the invention provides a kind of software quality prediction methods, as shown in Figure 1, comprising the following steps:
S100: the characteristic of multiple classifications of the software to be predicted in software development flow is obtained.Software development flow
It may include each stage and the process in software life-cycle.For example, the period of software development is pressed, it will be related with software quality
The feature of connection is divided into six classifications, including modular character, demand characteristic, development features, test feature, the online feature of publication and
O&M feature on line.
S200: characteristic of all categories is pre-processed, and is input in quality prediction model.Quality prediction model
It is according to reverse transmittance nerve network (BP neural network, Back Propagation Neural Network) and multiple classifications
History feature data building model.History feature data can be each software before software to be predicted and open in software
Send out the data of recording and storage in process.
S300: quality prediction model handles characteristic of all categories, and exports the prediction knot of software to be predicted
Fruit.The prediction result index of software under testing can be selected and be adjusted as needed.For example, the quality of software is attributed to software
Bug (loophole) number, then the prediction result of software under testing is bug number that may be present in software.
In one embodiment, characteristic of all categories includes behavioral characteristics and static nature.
In a specific embodiment, modular character includes module behavioral characteristics and module static nature.Module dynamic
Feature includes: that at least one of the module upgrade iteration frequency, module failure, module efficiency and mass ratio (work as module efficiency
When cannot be considered in terms of with mass ratio, it is more likely to the data of module efficiency).Module static nature include: module significance level scoring,
Module is relied at least one of degree scoring and the scoring of module degree of dependence.It should be noted that module is understood that
To be any functional module in software or the small routine block in exploitation.
Demand characteristic includes demand behavioral characteristics and demand static nature.Demand behavioral characteristics include: the complicated journey of demand
At least one of degree, the degree of perfection of requirement documents and demand evaluation number.Demand static nature includes: demand discussion
At least one of effective time and the index of demand raiser.
Development features include development behavior feature and exploitation static nature.Development behavior feature includes: code review number
And at least one of continuous integrating (ci, Continuous integration) number.Developing static nature includes: program
At least one in language, development time, developer's index, change lines of code, code annotation line number and oneself survey coverage rate
It is a.It should be noted that developer's index may include development quality (such as thousand row bug quantity, ci failure rate, problem on line
Deng), current position hierarchy, main programming language etc. index.
Test feature includes test behavioral characteristics and test static nature.Test behavioral characteristics include: that test cost performance (is surveyed
Examination personnel's project waiting).Test static nature include: automatic test cases (case) percent of pass, lines of code coverage rate with
And at least one of static code scanning problem number.
Issuing online feature includes issuing online behavioral characteristics and the online static nature of publication.Issue online behavioral characteristics packet
It includes: previous problem interception rate.Issuing online static nature includes: classification publication sum of series classification publication verification test cases
At least one of (case).
O&M feature includes O&M static nature on O&M behavioral characteristics and line on line on line.O&M behavioral characteristics packet on line
It includes: monitoring maturity on line, stops loss at least one of maturity automatically on problem recall rate and line on line.O&M is quiet on line
State feature includes: at least one of containerization deployment and automation O&M maturity.
In one embodiment, as shown in Fig. 2, obtaining the spy of multiple classifications of the software to be predicted in software development flow
Levy data, comprising:
S110: the initial data of multiple classifications of the software to be predicted in software development flow is obtained.The original of each classification
Beginning data are recorded and are stored at any time in software under testing development process, transfer use so as to subsequent.For example, by initial data
It stores in database or exploitation whole process data platform.
S120: initial data of all categories carries out screening assessment by corresponding Random Forest model respectively, each to obtain
The characteristic of classification.Since the data of each classification are different, it is therefore desirable to be screened with different Random Forest models.
The Random Forest model of each classification can be trained and be constructed by the historical data of software, can also be according to category Properties
Choose existing Random Forest model.
In one embodiment, this method may also include step S130: the characteristic of all categories that will acquire into
Row storage.Wherein, characteristic is possibly stored in data-storage system or exploitation whole process data platform.
It should be noted that each characteristic all has characteristic value and weight, pass through the specific ginseng of characteristic value and weight
Number realizes the calculating of screening characteristic and prediction model result.
In an application example, step S110-S130 can be realized by data acquisition function module.By acquiring function mould
Block realizes the acquisition of initial data, screening assessment and data storage, to facilitate data management and subsequent processing.
In one embodiment, the history that the corresponding Random Forest model of characteristic of all categories passes through previous software
Data carry out model training and building.As shown in figure 3, specific construction step includes:
S121: previous initial data of multiple softwares in software development process is obtained as historical data；
S122: the initial model of each classification is created, and using a large amount of historical datas abundant of each classification as instruction
Practice sample, the initial model of each classification is trained, to construct a suitable screening model (example for each classification
Such as, Random Forest model).
In an application example, as shown in figure 4, step S110-S130 and step S121-122 can also be by different function
It can module realization.For example, the initial data of multiple classifications in software development flow is stored in whole process platform 10.So
Screening assessment is carried out to the initial data of each classification using Random Forest model by data assessment system 20 afterwards.Data acquisition
Module 30 obtains the characteristic of all categories obtained after screening assessment.And characteristic storage of all categories is stored to data
In system 40.If the initial data in whole process platform can be used directly, data acquisition module can also be directly flat from whole process
Characteristic is obtained in platform.
In another application example, as shown in figure 5, according to user demand, by multiple classifications in software development flow
Initial data be stored in whole process platform 10.Whole process platform 10 carries out screening assessment and shape to initial data of all categories
At characteristic of all categories.Prediction of quality module 50 handles characteristic, by the prediction result of forecasting software quality
It is sent in data-storage system 40 and stores.Data-storage system 40 sends information to whole process platform 10 according to prediction result,
Whole process platform 10 issues software according to instruction online.The initial data of all categories stored in whole process platform 10 with when
Between be constantly updated, updated initial data be storable in whole process platform 10 for quality prediction model 50 obtain,
Updated initial data can be stored in data-storage system and be obtained for quality prediction model 50.Quality prediction model 50 can
To optimize quality prediction model using latest data.
In one embodiment, as shown in fig. 6, the method for the present embodiment further comprises the steps of:
S10: from several softwares in the historical data of software development process, the history feature number of multiple classifications is obtained
According to.
S20: history feature data of all categories are pre-processed.Pretreated mode can be selected as needed.
S30: using pretreated history feature data of all categories as training sample, it is based on reverse transmittance nerve network
Model training is carried out, quality prediction model is obtained.
In one embodiment, history feature data prediction include: firstly, to history feature data of all categories into
Row screens out, and removes the dirty data in historical data.Such as remove the data deviated considerably from, empty data.Then, to different-format
History feature data extract processing, such as are related to the calculating acquisition of text class or picture category data.Finally, by history spy
Sign data are converted into history feature vector, use in order to construct the model training of quality prediction model.
In a specific embodiment, building quality prediction model needs to select s type transmission function, and anti-by error
It is constantly adjusted to propagation function.And BP network is optimized by genetic algorithm, is found out in analytic space preferably
Exploration space, searching for optimal solution in lesser exploration space with BP network.Wherein, during model training, can lead to
It crosses empirical equation and chooses hidden layer neuron number.
S type transmission function are as follows:Wherein, x indicates the input quantity of the random layer of BP network, and f (x) is indicated
The output quantity of the random layer of BP network.
Error back propagation function:Wherein, E indicates the error of random layer, and O indicates that this is any
The reality output of layer is as a result, t indicates the target output of this layer as a result, i representational level is numbered.
Choose hidden layer neuron number function:Wherein, n is input layer number, and m is
Output layer neuron number, constant of a between [1,10].
In one embodiment, as shown in fig. 7, the embodiment of the invention also includes steps:
S40: newest history feature data of all categories are obtained.
S50: quality prediction model carries out self study according to newest history feature data, updates prediction of quality mould with optimization
Type.
It is only partially to complete model training as the time is continually changing in data due to carrying out model training
Quality prediction model only need with database or exploitation whole process data platform be connected to, newest history number can be obtained at any time
According to newest history feature data, and by constantly carry out automatic data acquisition and study complete quality prediction model it is excellent
Change and updates.
In an application example, as shown in figure 8, by the characteristics of six classifications of software under testing (modular character m,
It is pre- to be input to quality together by O&M feature o) on demand characteristic n, development features d, test feature t, the online feature r of publication and line
Survey model in handled, quality prediction model predicts the quality of software under testing, and using the bug number predicted as
As a result it exports.
At another using in example, as shown in figure 9, obtaining the initial data of each classification, (initial data includes static state
Initial data and dynamic initial data).The initial data screening assessment of each classification is generated to the characteristic of each classification.Example
Such as, static initial data msv1, msv2, msv3 and dynamic initial data mdv1, mdv2, mdv3 of modular character m are handled,
Form the characteristic of modular character m.Static initial data osv1, osv2, osv3 and dynamic to O&M feature o on line is former
Beginning data odv1, odv2, odv3 processing, form the characteristic of O&M feature o on line.By the spy of six classifications of software under testing
It levies data and (modular character m, demand characteristic n, development features d, test feature t, issues O&M feature on online feature r and line
O) it is input in the neural network of quality prediction model and is handled together, quality prediction model carries out the quality of software under testing
It predicts and generates prediction result.
The embodiment of the invention provides a kind of software quality prediction devices, as shown in Figure 10, comprising:
Characteristic obtains module 61, for obtaining multiple class another characteristics of the software to be predicted in software development flow
Data.
First preprocessing module 62 for pre-processing characteristic of all categories, and is input to prediction of quality mould
In type.Quality prediction model is the model constructed according to the history feature data of reverse transmittance nerve network and multiple classifications.
Prediction module 63 for being handled by quality prediction model characteristic of all categories, and is exported to pre-
Survey the prediction result of software.
In one embodiment, as shown in figure 11, software quality prediction device further include:
History feature data acquisition module 64, in the historical data of software development process, being obtained from several softwares
Take the history feature data of multiple classifications.
Second preprocessing module 65, for being pre-processed to history feature data of all categories.
Model construction module 66, for being based on using pretreated history feature data of all categories as training sample
Reverse transmittance nerve network carries out model training, obtains quality prediction model.
In one embodiment, as shown in figure 12, characteristic acquisition module 61 includes:
Acquisition submodule 611, for obtaining the initial data of multiple classifications of the software to be predicted in software development flow.
Submodule 612 is screened, is sieved for initial data of all categories to be passed through corresponding Random Forest model respectively
Choosing, to obtain characteristic of all categories.
In one embodiment, as shown in figure 13, software quality prediction device further include:
Module 67 is obtained, for obtaining newest history feature data of all categories.
Update module 68 carries out self study according to newest history feature data for quality prediction model, to optimize more
New quality prediction model.
The embodiment of the invention provides a kind of terminals of software quality prediction, as shown in figure 14, comprising:
Memory 910 and processor 920 are stored with the computer journey that can be run on processor 920 in memory 910
Sequence.Processor 920 realizes the software quality prediction in above-described embodiment method when executing computer program.910 He of memory
The quantity of processor 920 can be one or more.
Communication interface 930 is communicated for memory 910 and processor 920 with outside.
Memory 910 may include high speed RAM memory, it is also possible to further include nonvolatile memory (non-
Volatile memory), a for example, at least magnetic disk storage.
If memory 910, processor 920 and the independent realization of communication interface 930, memory 910, processor 920
And communication interface 930 can be connected with each other by bus and complete mutual communication.Bus can be Industry Standard Architecture
Structure (ISA, Industry Standard Architecture) bus, external equipment interconnection (PCI, Peripheral
Component) bus or extended industry-standard architecture (EISA, Extended Industry Standard
Component) bus etc..Bus can be divided into address bus, data/address bus, control bus etc..For convenient for indicating, in Fig. 3 only
It is indicated with a thick line, it is not intended that an only bus or a type of bus.
Optionally, in specific implementation, if memory 910, processor 920 and communication interface 930 are integrated in one piece
On chip, then memory 910, processor 920 and communication interface 930 can complete mutual communication by internal interface.
The embodiment of the invention provides a kind of computer readable storage mediums, are stored with computer program, the program quilt
Processor execute when realize embodiment one include it is any as described in software quality prediction method.
The embodiment of the present invention have it is following a little: 1, according to influence software quality index of correlation, integrated software exploitation stream
The characteristic of each classification in journey can quickly and comprehensively carry out software quality prediction, ensure that prediction accuracy.2,
By all kinds of characteristic indexs of combing software quality related each development phase, a large amount of historical datas abundant based on accumulation,
It is to select a suitable filtering algorithm each development phase, and utilize quality prediction model to each with Random Forest model
The characteristic that a stage filters out is integrated.Solve causes prediction result is insecure to ask because prediction index is too simple
Topic, and solve the problems, such as to rely on tester's experience.3, according to the prediction result of software quality not only adjustable software
The emphasis of prediction model test, optimizes online process, and software quality inspection can be more improved under the premise of guaranteeing quality well
Survey efficiency.4, using the historical data of the model algorithm of artificial intelligence and magnanimity, scientific forecasting is carried out to software quality problem, no
But good decision path of simulation field expert during doing such prediction of energy, can easily more be applied to other field
In.By the carry out software quality prediction of acquisition characteristic science of all categories, can preferably coordinate to configure test resource, it is excellent
Melt the process for sending out online.Faster and better solves the problems, such as in online functional module and closed loop line, to create bigger value.
5, the continuous iteration optimization to software prediction model is realized by the data of update, so that software prediction model passes through self study reality
It now evolves, guarantees the accuracy of software quality prediction result.
In the description of this specification, reference term " one embodiment ", " some embodiments ", " example ", " specifically show
The description of example " or " some examples " etc. means specific features, structure, material or spy described in conjunction with this embodiment or example
Point is included at least one embodiment or example of the invention.Moreover, particular features, structures, materials, or characteristics described
It may be combined in any suitable manner in any one or more of the embodiments or examples.In addition, without conflicting with each other, this
The technical staff in field can be by the spy of different embodiments or examples described in this specification and different embodiments or examples
Sign is combined.
In addition, term " first ", " second " are used for descriptive purposes only and cannot be understood as indicating or suggesting relative importance
Or implicitly indicate the quantity of indicated technical characteristic." first " is defined as a result, the feature of " second " can be expressed or hidden
It include at least one this feature containing ground.In the description of the present invention, the meaning of " plurality " is two or more, unless otherwise
Clear specific restriction.
Any process described otherwise above or method description are construed as in flow chart or herein, and expression includes
It is one or more for realizing specific logical function or process the step of executable instruction code module, segment or portion
Point, and the range of the preferred embodiment of the present invention includes other realization, wherein can not press shown or discussed suitable
Sequence, including according to related function by it is basic simultaneously in the way of or in the opposite order, to execute function, this should be of the invention
Embodiment person of ordinary skill in the field understood.
Expression or logic and/or step described otherwise above herein in flow charts, for example, being considered use
In the order list for the executable instruction for realizing logic function, may be embodied in any computer-readable medium, for
Instruction execution system, device or equipment (such as computer based system, including the system of processor or other can be held from instruction
The instruction fetch of row system, device or equipment and the system executed instruction) it uses, or combine these instruction execution systems, device or set
It is standby and use.For the purpose of this specification, " computer-readable medium ", which can be, any may include, stores, communicates, propagates or pass
Defeated program is for instruction execution system, device or equipment or the dress used in conjunction with these instruction execution systems, device or equipment
It sets.The more specific example (non-exhaustive list) of computer-readable medium include the following: there is the electricity of one or more wirings
Interconnecting piece (electronic device), portable computer diskette box (magnetic device), random access memory (RAM), read-only memory
(ROM), erasable edit read-only storage (EPROM or flash memory), fiber device and portable read-only memory
(CDROM).In addition, computer-readable medium can even is that the paper that can print described program on it or other suitable Jie
Matter, because can then be edited, be interpreted or when necessary with other for example by carrying out optical scanner to paper or other media
Suitable method is handled electronically to obtain described program, is then stored in computer storage.
It should be appreciated that each section of the invention can be realized with hardware, software, firmware or their combination.Above-mentioned
In embodiment, software that multiple steps or method can be executed in memory and by suitable instruction execution system with storage
Or firmware is realized.It, and in another embodiment, can be under well known in the art for example, if realized with hardware
Any one of column technology or their combination are realized: having a logic gates for realizing logic function to data-signal
Discrete logic, with suitable combinational logic gate circuit specific integrated circuit, programmable gate array (PGA), scene
Programmable gate array (FPGA) etc..
Those skilled in the art are understood that realize all or part of step that above-described embodiment method carries
It suddenly is that relevant hardware can be instructed to complete by program, the program can store in a kind of computer-readable storage medium
In matter, which when being executed, includes the steps that one or a combination set of embodiment of the method.
It, can also be in addition, each functional unit in each embodiment of the present invention can integrate in a processing module
It is that each unit physically exists alone, can also be integrated in two or more units in a module.Above-mentioned integrated mould
Block both can take the form of hardware realization, can also be realized in the form of software function module.The integrated module is such as
Fruit is realized and when sold or used as an independent product in the form of software function module, also can store in a computer
In readable storage medium storing program for executing.The storage medium can be read-only memory, disk or CD etc..
The above description is merely a specific embodiment, but scope of protection of the present invention is not limited thereto, any
Those familiar with the art in the technical scope disclosed by the present invention, can readily occur in its various change or replacement,
These should be covered by the protection scope of the present invention.Therefore, protection scope of the present invention should be with the guarantor of the claim
It protects subject to range.
Claims (12)
1. a kind of software quality prediction method characterized by comprising
Obtain the characteristic of multiple classifications of the software to be predicted in software development flow；
The characteristic of each classification is pre-processed, and is input in quality prediction model；The quality prediction model
It is the model constructed according to the history feature data of reverse transmittance nerve network and multiple classifications；
The quality prediction model handles the characteristic of each classification, and exports the prediction of the software to be predicted
As a result.
2. the method as described in claim 1, which is characterized in that the classification of the characteristic includes modular character, demand spy
On sign, development features, test feature, the online feature of publication and line in O&M feature at least one of.
3. the method as described in claim 1, which is characterized in that further include:
From several softwares in the historical data of software development process, the history feature data of multiple classifications are obtained；
The history feature data of each classification are pre-processed；
Using the history feature data of pretreated each classification as training sample, it is based on the reverse transmittance nerve network
Model training is carried out, the quality prediction model is obtained.
4. the method as described in claim 1, which is characterized in that obtain multiple classes of the software to be predicted in software development flow
Another characteristic data, comprising:
Obtain the initial data of the multiple classifications of the software to be predicted in software development flow；
The initial data of each classification passes through corresponding Random Forest model respectively and is screened, to obtain each classification
Characteristic.
5. the method as described in claim 1, which is characterized in that further include:
Obtain the newest history feature data of each classification；
The quality prediction model carries out self study according to the newest history feature data, and it is pre- to update the quality with optimization
Survey model.
6. method according to claim 2, which is characterized in that the characteristic of each classification includes behavioral characteristics and static state
Feature specifically includes at least one of following:
Modular character includes module behavioral characteristics and module static nature；Wherein, module behavioral characteristics include: module upgrade iteration
At least one of the frequency, module failure, module efficiency and mass ratio；Module static nature includes: that module significance level is commented
Divide, module is relied at least one of degree scoring and the scoring of module degree of dependence；
Demand characteristic includes demand behavioral characteristics and demand static nature；Wherein, demand behavioral characteristics include: the complicated journey of demand
At least one of degree, the degree of perfection of requirement documents and demand evaluation number；Demand static nature includes: demand discussion
At least one of effective time and the index of demand raiser；
Development features include development behavior feature and exploitation static nature；Wherein, development behavior feature includes: code review number
And at least one of continuous integrating number；Exploitation static nature include: program language, the development time, developer's index,
It changes lines of code, code annotation line number and surveys at least one of coverage rate certainly；
Test feature includes test behavioral characteristics and test static nature；Wherein, test behavioral characteristics include: test cost performance；
Test static nature includes: in automatic test cases percent of pass, lines of code coverage rate and static code scanning problem number
At least one；
Issuing online feature includes issuing online behavioral characteristics and the online static nature of publication；Wherein, online behavioral characteristics are issued
It include: previous problem interception rate；Issuing online static nature includes: in classification publication sum of series classification publication verification test cases
At least one；
O&M feature includes O&M static nature on O&M behavioral characteristics and line on line on line；Wherein, O&M behavioral characteristics on line
It include: to monitor maturity on line, stop loss at least one of maturity automatically on problem recall rate and line on line；O&M on line
Static nature includes: at least one of containerization deployment and automation O&M maturity.
7. a kind of software quality prediction device characterized by comprising
Characteristic obtains module, for obtaining the characteristic of multiple classifications of the software to be predicted in software development flow；
First preprocessing module for pre-processing the characteristic of each classification, and is input to quality prediction model
In；The quality prediction model is the mould constructed according to the history feature data of reverse transmittance nerve network and multiple classifications
Type；
Prediction module for handling by characteristic of the quality prediction model to each classification, and exports institute
State the prediction result of software to be predicted.
8. device as claimed in claim 7, which is characterized in that further include:
History feature data acquisition module, in the historical data of software development process, being obtained multiple from several softwares
The history feature data of the classification；
Second preprocessing module is pre-processed for the history feature data to each classification；
Model construction module, for being based on institute using the history feature data of pretreated each classification as training sample
It states reverse transmittance nerve network and carries out model training, obtain the quality prediction model.
9. device as claimed in claim 7, which is characterized in that the characteristic obtains module and includes:
Acquisition submodule, for obtaining the original number of the multiple classifications of the software to be predicted in software development flow
According to；
Submodule is screened, is screened for the initial data of each classification to be passed through corresponding Random Forest model respectively,
To obtain the characteristic of each classification.
10. device as claimed in claim 7, which is characterized in that further include:
Module is obtained, for obtaining the newest history feature data of each classification；
Update module carries out self study according to the newest history feature data for the quality prediction model, with optimization
Update the quality prediction model.
11. a kind of software quality prediction terminal characterized by comprising
One or more processors；
Storage device, for storing one or more programs；
When one or more of programs are executed by one or more of processors, so that one or more of processors
Realize such as method described in any one of claims 1 to 6.
12. a kind of computer readable storage medium, is stored with computer program, which is characterized in that the program is held by processor
Such as method described in any one of claims 1 to 6 is realized when row.
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