CN109634833A - A kind of Software Defects Predict Methods and device - Google Patents
A kind of Software Defects Predict Methods and device Download PDFInfo
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Abstract
The embodiment of the present invention provides a kind of Software Defects Predict Methods and device, is related to field of computer technology.A kind of Software Defects Predict Methods of the embodiment of the present invention, comprising: the release information for acquiring passing software version establishes defects count prediction model according to the release information；The release information of software under testing version is inputted into the defects count prediction model, to carry out failure prediction to the software under testing version.The embodiment of the present invention establishes defects count prediction model by acquiring the release information of passing software version, carries out failure prediction to software version to be measured according to the defects count prediction model, provides reference to manager.
Description
Technical field
The present invention relates to computer field more particularly to a kind of Software Defects Predict Methods and devices.
Background technique
The defects of so-called software, usually again be called Bug, refer in computer system or program it is more under covering not by
It was found that defect or problem.In various types of Bug, what is had will not cause damages to software, and some can but allow software to collapse
It bursts, the loss that can not be estimated is brought to user.
With the development of information technology, software quality estimation can help enterprise development to go out the software product of high quality, subtract
The generation and maintenance cost of few software product.It is general that software quality is carried out using Kilo Lines of Code Bug rate in software enterprise
Assessment, Kilo Lines of Code Bug rate=Bug quantity/(lines of code/1000).
In realizing process of the present invention, at least there are the following problems in the prior art for inventor's discovery:
(1) this method of Kilo Lines of Code Bug rate is only with reference to lines of code, but lines of code and Bug quantity and non-straight
The linear relationship connect, the other factors influenced on Bug quantity cause the software quality evaluated inaccurate there are also very much.
(2) developer by increasing cardinal number of code, massive duplication pastes code, invents various methods these means again just
Size of code can significantly be increased, and then reduce Kilo Lines of Code Bug rate, commenting using Kilo Lines of Code Bug rate as software quality
Estimate module, is unfavorable for the real quality of manager's control software version.
Summary of the invention
In view of this, the embodiment of the present invention provides a kind of Software Defects Predict Methods and device.The embodiment of the present invention passes through
The release information of passing software version is acquired to establish defects count prediction model, according to the defects count prediction model to be measured
Software version carries out failure prediction, provides reference to manager.
To achieve the above object, according to an aspect of an embodiment of the present invention, a kind of Software Defects Predict Methods are provided.
A kind of Software Defects Predict Methods of the embodiment of the present invention, comprising: acquire the release information of passing software version, root
Defects count prediction model is established according to the release information；It is pre- that the release information of software under testing version is inputted into the defects count
Model is surveyed, to carry out failure prediction to the software under testing version.
Optionally, defects count prediction model is established according to the release information, comprising: by the passing software version
It is returned in release information input multi-classification algorithm with carrying out more classification；The release information includes that various features information and reality lack
Fall into quantity；The minimum value that the cost function of the multi-classification algorithm is solved according to iteration optimization algorithms, the minimum value is corresponding
Characteristic parameter optimal solution of the value as the characteristic parameter.
Optionally, the various features information includes demand acquisition time, the corresponding demand number of each priority requirements, exploitation
Time, exploitation code amount, testing time and test case item number.
Optionally, the release information of software under testing version is inputted into the defects count prediction model, to described to be measured
Software version carries out failure prediction, comprising: the release information of software under testing version is inputted into the defects count prediction model, with
Obtain the probability of the software under testing version generic；The multi-classification algorithm is Softmax algorithm；By the class of maximum probability
Prediction defects count of the not corresponding defects count as the software under testing version.
Optionally, the method also includes: after the completion of sending out version, the release information of the software under testing version is updated to
Actual value；According to the release information training of the release information of the updated software under testing version and passing software version
Defects count prediction model.
Optionally, the method also includes: establish release information data bank, the release information data bank includes passing soft
The release information of part version；The release information of the updated software under testing version is saved in the release information data
Library.
To achieve the above object, according to another aspect of an embodiment of the present invention, a kind of software defect prediction meanss are provided.
A kind of software defect prediction meanss of the embodiment of the present invention, comprising: model building module, for acquiring passing software
The release information of version establishes defects count prediction model according to the release information；Failure prediction module, being used for will be to be measured soft
The release information of part version inputs the defects count prediction model, to carry out failure prediction to the software under testing version.
Optionally, the model building module, is also used to: by the more classification of release information input of the passing software version
It is returned in algorithm with carrying out more classification；The release information includes various features information and actual defects quantity；It is excellent according to iteration
Change algorithm solve the multi-classification algorithm cost function minimum value, using the value of the corresponding characteristic parameter of the minimum value as
The optimal solution of the characteristic parameter.
Optionally, the various features information includes demand acquisition time, the corresponding demand number of each priority requirements, exploitation
Time, exploitation code amount, testing time and test case item number.
Optionally, the failure prediction module, is also used to: the release information of software under testing version is inputted the defect number
Prediction model is measured, to obtain the probability of the software under testing version generic；The multi-classification algorithm is Softmax algorithm；
Using the corresponding defects count of the classification of maximum probability as the prediction defects count of the software under testing version.
Optionally, described device further include: information updating module is used for after the completion of sending out version, by the software under testing version
This release information is updated to actual value；Model reinforced module, for the publication according to the updated software under testing version
Information and the release information of the passing software version training defects count prediction model.
Optionally, the method also includes: data bank establishes module, for establish release information data bank, the publication
Information material library includes the release information of passing software version；Information preservation module is used for the updated software under testing
The release information of version is saved in the release information data bank.
To achieve the above object, according to an embodiment of the present invention in another aspect, providing a kind of electronic equipment.
The a kind of electronic equipment of the embodiment of the present invention, comprising: one or more processors；Storage device, for storing one
A or multiple programs, when one or more of programs are executed by one or more of processors, so that one or more
A processor realizes a kind of Software Defects Predict Methods of the embodiment of the present invention.
To achieve the above object, according to an embodiment of the present invention in another aspect, providing a kind of computer-readable medium.
A kind of computer-readable medium of the embodiment of the present invention, is stored thereon with computer program, and described program is processed
A kind of Software Defects Predict Methods of the embodiment of the present invention are realized when device executes.
One embodiment in foregoing invention has the following advantages that or the utility model has the advantages that the hair for passing through the passing software version of acquisition
Cloth information establishes defects count prediction model, and it is pre- to carry out defect to software version to be measured according to the defects count prediction model
It surveys, provides reference to manager；It is more classification problems by software Bug quantitative forecast Problem Summary, is solved using Softmax algorithm
More classification problems, obtain defects count prediction model；It sends out version and counts practical Bug quantity later, strengthened according to practical Bug quantity
Defects count prediction model provides the accuracy rate of prediction.
Further effect possessed by above-mentioned non-usual optional way adds hereinafter in conjunction with specific embodiment
With explanation.
Detailed description of the invention
Attached drawing for a better understanding of the present invention, does not constitute an undue limitation on the present invention.Wherein:
Fig. 1 is the key step schematic diagram of Software Defects Predict Methods according to an embodiment of the present invention；
Fig. 2 is the flow chart of Software Defects Predict Methods according to an embodiment of the present invention；
Fig. 3 is the main modular schematic diagram of software defect prediction meanss according to an embodiment of the present invention；
Fig. 4 is that the embodiment of the present invention can be applied to exemplary system architecture figure therein；
Fig. 5 is the structural schematic diagram for being suitable for the computer installation of the electronic equipment to realize the embodiment of the present invention.
Specific embodiment
Below in conjunction with attached drawing, an exemplary embodiment of the present invention will be described, including the various of the embodiment of the present invention
Details should think them only exemplary to help understanding.Therefore, those of ordinary skill in the art should recognize
It arrives, it can be with various changes and modifications are made to the embodiments described herein, without departing from scope and spirit of the present invention.Together
Sample, for clarity and conciseness, descriptions of well-known functions and structures are omitted from the following description.
Fig. 1 is the key step schematic diagram of Software Defects Predict Methods according to an embodiment of the present invention.As shown in Figure 1, this
The Software Defects Predict Methods of inventive embodiments, mainly include the following steps:
Step S101: acquiring the release information of passing software version, establishes defects count prediction according to the release information
Model.The release information includes various features information and practical Bug quantity, when the various features information includes that demand is collected
Between, the corresponding demand number of each priority requirements, development time, exploitation code amount, testing time and test case item number.Above-mentioned hair
Cloth information is the actual value of the software version.Using various features information as feature, using practical Bug quantity as training label
Establish defects count prediction model.Defects count, which is established, by multi-classification algorithm and iteration optimization algorithms in embodiment predicts mould
Type.
Step S102: inputting the defects count prediction model for the release information of software under testing version, with to it is described to
It surveys software version and carries out failure prediction.The various features information for collecting software under testing version, by the characteristic information input step
Prediction Bug quantity can be obtained in the defects count prediction model that S101 is established.
In addition, the Software Defects Predict Methods of the embodiment of the present invention can also include: after step s 102 in hair version
After the completion, the release information of the software under testing version is updated to actual value；According to the updated software under testing version
Release information and passing software version the release information training defects count prediction model.The step is used for intensive training
Model, to improve the accuracy rate of prediction.
Fig. 2 is the flow chart of Software Defects Predict Methods according to an embodiment of the present invention.As shown in Figure 1, the present invention is implemented
The Software Defects Predict Methods of example, mainly include the following steps:
(1) release information for acquiring passing software version establishes release information data bank.Table 1 is in the embodiment of the present invention
The composition for the data bank that releases news, as shown in table 1, which includes the release information of passing software version, should
Release information includes various features information and practical Bug quantity, and release information here is the actual value of the software version.It is special
Reference breath includes during demand acquisition time, the corresponding high priority demand number of high priority demand, middle priority requirements are corresponding
The corresponding low priority demand number of priority requirements number, low priority demand, the development time, exploitation code amount, the testing time with
And test case item number.Wherein, demand acquisition time, high priority demand number, middle priority requirements number and low priority need
Ask number that can be obtained by requirement documents；Development time, testing time, test case item number and practical Bug quantity can be by surveying
Examination report obtains；Exploitation code amount can be counted by Version.Version number is used to identify unique software version.
The release information data bank of table 1
(2) defects count prediction model is established according to the release information of the release information data bank.
By demand acquisition time, high priority demand number, middle priority requirements number, low priority demand number, the development time,
Testing time and test case item number are respectively as feature x1、x2、x3、x4、x5、x6、x7And x8, using practical Bug quantity as training
Label y, it can be deduced that following calculation formula:
y1=θ0+θ1x1+θ2x2+θ3x3+θ4x4+θ5x5+θ6x6+θ7x7+θ8x8
In formula, y1For the training label for the data bank the first row sample that releases news, θ0To θ8It is for adjusting each feature
Characteristic parameter.The every a line sample standard deviation for the data bank that releases news can be expressed with above-mentioned formula.According to release information data
The release information in library, as long as solving the optimal solution of each characteristic parameter, then optimal solution is applied to software version to be predicted
Above-mentioned formula in can predict the value of trained label y, namely obtain prediction Bug quantity.
It since practical Bug quantity has much from zero to infinity, and is mutual exclusion, i.e., if the software version has 10
Bug is impossible to be 20 Bug, therefore the forecasting problem of Bug quantity is classification problem more than one.According to release information data
Training label y is had k value by library, then can be summarized as k classification, and each classification corresponding characteristic parameter one (n+1) is tieed up
Vector, n is characterized total quantity, and n is 8 in the embodiment of the present invention.More classification problems can establish defect using multi-classification algorithm
Then quantitative forecast model iterates to calculate out the optimal solution of characteristic parameter by iteration optimization algorithms, optimal solution is substituted into and is established
Defects count prediction model, save the model to treat the Bug quantity of forecasting software version and be predicted.More classification are calculated
Method can be Softmax algorithm, support vector machines (Support Vector Machines, SVM) algorithm or decision tree
(Gradient Boosting Decision Tree, GBDT) algorithm, the iteration optimization algorithms can be or feature selecting
(Relif F) algorithm.
Below with multi-classification algorithm for Softmax algorithm, iteration optimization algorithms are that gradient descent method predicts defects count
The establishment process of model is described in detail.
The function expression of Softmax algorithm are as follows:
In formula, x(i)For all features for the i-th row of data bank sample that releases news, i=1 ..., m；J=1 ..., k；θ is
The matrix of k (n+1) represents all characteristic parameters of k classification；·TFor matrix transposition.The function divides x for solution
The probability of classification j.
The expression formula of the cost function J (θ) of Softmax algorithm are as follows:
In formula, 1 { } was indicative function, and value rule is 1 { value is genuine expression formula }=1,1 { value is false expression formula }
=0.
Keep cost function J (θ) minimum using gradient descent method:
In formula,Indicate J (θ) to θjJ-th of element partial derivative.Iteration will carry out as follows more each time
It is new:
In formula, θjRepresent all characteristic parameters of j-th of classification.
So far, it can be deduced that the optimal solution of each characteristic parameter.
(3) release information of software under testing version is inputted into the defects count prediction model and carries out failure prediction.
The demand acquisition time of software under testing version, high priority can be collected in the waiting stage at version publication initial stage need to
Number, middle priority requirements numbers and low priority demand number are asked, using this four characteristic informations as feature x1、x2、x3And x4；
Development time and exploitation code amount are estimated out by developer, using the two characteristic informations as feature x5And x6；By testing
Personnel estimate out testing time and test case item number, using the two characteristic informations as feature x7And x8.By all features
It is input in trained defects count prediction model, then can obtain the prediction Bug quantity of the software under testing version.
Multi-classification algorithm is Softmax algorithm in the embodiment of the present invention, and the output of the algorithm is a range and its affiliated
Probability.Assuming that output are as follows:
[20-30) 80%
[10-20) 10%
…
The classification of maximum probability is chosen as most preferably predicting come the Bug quantity to software version to be measured.It then can be with
Conclude that the software version Bug quantity be [20-30) a possibility that highest, probability 80%.
(4) after the completion of sending out version, collect the software under testing version practical Bug quantity and actual characteristic information, root
Data bank update will be released news to actual value according to collected data, re-establish defects count prediction model.By sending out version
Practical release information afterwards strengthens defects count prediction model, improves the accuracy rate of Bug quantitative forecast.
In addition, the software defect quantitative forecasting technique of the embodiment of the present invention can be comprising steps of output prediction Bug number
Amount compares prediction Bug quantity with practical Bug quantity.By the two values it can be concluded that the general quality of the software version
Situation provides foundation for manager's decision.
Software Defects Predict Methods through the embodiment of the present invention can be seen that the publication by acquiring passing software version
Information establishes defects count prediction model, carries out failure prediction to software version to be measured according to the defects count prediction model,
Reference is provided to manager；It is more classification problems by software Bug quantitative forecast Problem Summary, being solved using Softmax algorithm should
More classification problems obtain defects count prediction model；It sends out version and counts practical Bug quantity later, strengthened according to practical Bug quantity and lacked
Quantitative forecast model is fallen into, the accuracy rate of prediction is provided.
Fig. 3 is the main modular schematic diagram of software defect prediction meanss according to an embodiment of the present invention.As shown in figure 3, this
The software defect prediction meanss 300 of inventive embodiments, specifically include that
Model building module 301 is established according to the release information and is lacked for acquiring the release information of passing software version
Fall into quantitative forecast model.The release information includes various features information and practical Bug quantity, and the various features information includes
The corresponding demand number of demand acquisition time, each priority requirements, development time, exploitation code amount, testing time and test case
Item number.Above-mentioned release information is the actual value of the software version.Using various features information as feature, by practical Bug quantity
Defects count prediction model is established as training label.By multi-classification algorithm (such as Softmax algorithm) and repeatedly in embodiment
Defects count prediction model is established for optimization algorithm (such as gradient descent method).
Failure prediction module 302, for the release information of software under testing version to be inputted the defects count prediction model,
To carry out failure prediction to the software under testing version.The various features information for collecting software under testing version, the feature is believed
Prediction Bug quantity can be obtained in the defects count prediction model that breath input is established.
In addition, the software defect prediction meanss of the embodiment of the present invention, can also include information updating module, model reinforcing mould
Block, data bank establish module and information preservation module, and above-mentioned module does not export in figure.Information updating module, in hair version
After the completion, the release information of the software under testing version is updated to actual value.Model reinforced module, for according to updated
The release information of the software under testing version and the release information training defects count prediction model of passing software version.Money
Module is established in material library, and for establishing release information data bank, the release information data bank includes the publication of passing software version
Information.Information preservation module, for the release information of the updated software under testing version to be saved in the release information
Data bank.
From the above, it can be seen that establishing defects count by the release information for acquiring passing software version predicts mould
Type carries out failure prediction to software version to be measured according to the defects count prediction model, provides reference to manager；By software
Bug quantitative forecast Problem Summary is more classification problems, solves more classification problems using Softmax algorithm, obtains defects count
Prediction model；It sends out version and counts practical Bug quantity later, defects count prediction model is strengthened according to practical Bug quantity, prediction is provided
Accuracy rate.
Fig. 4, which is shown, can apply the Software Defects Predict Methods of the embodiment of the present invention or showing for software defect prediction meanss
Example property system architecture 400.
As shown in figure 4, system architecture 400 may include terminal device 401,402,403, network 404 and server 405.
Network 404 between terminal device 401,402,403 and server 405 to provide the medium of communication link.Network 404 can be with
Including various connection types, such as wired, wireless communication link or fiber optic cables etc..
User can be used terminal device 401,402,403 and be interacted by network 404 with server 405, to receive or send out
Send message etc..Various telecommunication customer end applications, such as the application of shopping class, net can be installed on terminal device 401,402,403
(merely illustrative) such as the application of page browsing device, searching class application, instant messaging tools, mailbox client, social platform softwares.
Terminal device 401,402,403 can be the various electronic equipments with display screen and supported web page browsing, packet
Include but be not limited to smart phone, tablet computer, pocket computer on knee and desktop computer etc..
Server 405 can be to provide the server of various services, such as utilize terminal device 401,402,403 to user
Generated click event provides the back-stage management server (merely illustrative) supported.Back-stage management server can be to receiving
The data such as click data, content of text analyze etc. processing, and (such as target push information, product are believed by processing result
Breath -- merely illustrative) feed back to terminal device.
It should be noted that Software Defects Predict Methods provided by the embodiment of the present application are generally executed by server 405,
Correspondingly, software defect prediction meanss are generally positioned in server 405.
It should be understood that the number of terminal device, network and server in Fig. 4 is only schematical.According to realization need
It wants, can have any number of terminal device, network and server.
According to an embodiment of the invention, the present invention also provides a kind of electronic equipment and a kind of computer-readable medium.
Electronic equipment of the invention includes: one or more processors；Storage device, for storing one or more journeys
Sequence, when one or more of programs are executed by one or more of processors, so that one or more of processors are real
A kind of Software Defects Predict Methods of the existing embodiment of the present invention.
Computer-readable medium of the invention is stored thereon with computer program, real when described program is executed by processor
A kind of Software Defects Predict Methods of the existing embodiment of the present invention.
Below with reference to Fig. 5, it illustrates the computer systems 500 being suitable for realize the electronic equipment of the embodiment of the present invention
Structural schematic diagram.Electronic equipment shown in Fig. 5 is only an example, function to the embodiment of the present invention and should not use model
Shroud carrys out any restrictions.
As shown in figure 5, computer system 500 includes central processing unit (CPU) 501, it can be read-only according to being stored in
Program in memory (ROM) 502 or be loaded into the program in random access storage device (RAM) 503 from storage section 508 and
Execute various movements appropriate and processing.In RAM 503, also it is stored with computer system 500 and operates required various programs
And data.CPU 501, ROM 502 and RAM 503 are connected with each other by bus 504.Input/output (I/O) interface 505
It is connected to bus 504.
I/O interface 505 is connected to lower component: the importation 506 including keyboard, mouse etc.；It is penetrated including such as cathode
The output par, c 507 of spool (CRT), liquid crystal display (LCD) etc. and loudspeaker etc.；Storage section 508 including hard disk etc.；
And the communications portion 509 of the network interface card including LAN card, modem etc..Communications portion 509 via such as because
The network of spy's net executes communication process.Driver 510 is also connected to I/O interface 505 as needed.Detachable media 511, such as
Disk, CD, magneto-optic disk, semiconductor memory etc. are mounted on as needed on driver 510, in order to read from thereon
Computer program be mounted into storage section 508 as needed.
Particularly, disclosed embodiment, the process of key step figure description above may be implemented as counting according to the present invention
Calculation machine software program.For example, embodiment disclosed by the invention includes a kind of computer program product comprising be carried on computer
Computer program on readable medium, the computer program include the program generation for executing method shown in key step figure
Code.In such embodiments, which can be downloaded and installed from network by communications portion 509, and/or
It is mounted from detachable media 511.When the computer program is executed by central processing unit (CPU) 501, execute of the invention
The above-mentioned function of being limited in system.
It should be noted that computer-readable medium shown in the present invention can be computer-readable signal media or meter
Calculation machine readable storage medium storing program for executing either the two any combination.Computer readable storage medium for example can be --- but not
Be limited to --- electricity, magnetic, optical, electromagnetic, infrared ray or semiconductor system, device or device, or any above combination.Meter
The more specific example of calculation machine readable storage medium storing program for executing can include but is not limited to: have the electrical connection, just of one or more conducting wires
Taking formula computer disk, hard disk, random access storage device (RAM), read-only memory (ROM), erasable type may be programmed read-only storage
Device (EPROM or flash memory), optical fiber, portable compact disc read-only memory (CD-ROM), light storage device, magnetic memory device,
Or above-mentioned any appropriate combination.In the present invention, computer readable storage medium can be it is any include or storage journey
The tangible medium of sequence, the program can be commanded execution system, device or device use or in connection.And at this
In invention, computer-readable signal media may include in a base band or as carrier wave a part propagate data-signal,
Wherein carry computer-readable program code.The data-signal of this propagation can take various forms, including but unlimited
In electromagnetic signal, optical signal or above-mentioned any appropriate combination.Computer-readable signal media can also be that computer can
Any computer-readable medium other than storage medium is read, which can send, propagates or transmit and be used for
By the use of instruction execution system, device or device or program in connection.Include on computer-readable medium
Program code can transmit with any suitable medium, including but not limited to: wireless, electric wire, optical cable, RF etc. are above-mentioned
Any appropriate combination.
Flow chart and block diagram in attached drawing are illustrated according to the system of various embodiments of the invention, method and computer journey
The architecture, function and operation in the cards of sequence product.In this regard, each box in flowchart or block diagram can generation
A part of one module, program segment or code of table, a part of above-mentioned module, program segment or code include one or more
Executable instruction for implementing the specified logical function.It should also be noted that in some implementations as replacements, institute in box
The function of mark can also occur in a different order than that indicated in the drawings.For example, two boxes succeedingly indicated are practical
On can be basically executed in parallel, they can also be executed in the opposite order sometimes, and this depends on the function involved.Also it wants
It is noted that the combination of each box in block diagram or flow chart and the box in block diagram or flow chart, can use and execute rule
The dedicated hardware based systems of fixed functions or operations is realized, or can use the group of specialized hardware and computer instruction
It closes to realize.
Being described in module involved in the embodiment of the present invention can be realized by way of software, can also be by hard
The mode of part is realized.Described module also can be set in the processor, for example, can be described as: a kind of processor packet
Include model building module and failure prediction module.Wherein, the title of these modules is not constituted under certain conditions to the module
The restriction of itself, for example, model building module is also described as " release information of passing software version being acquired, according to institute
State the module that release information establishes defects count prediction model ".
As on the other hand, the present invention also provides a kind of computer-readable medium, which be can be
Included in equipment described in above-described embodiment；It is also possible to individualism, and without in the supplying equipment.Above-mentioned calculating
Machine readable medium carries one or more program, when said one or multiple programs are executed by the equipment, makes
Obtaining the equipment includes: to acquire the release information of passing software version, establishes defects count prediction model according to the release information；
The release information of software under testing version is inputted into the defects count prediction model, to carry out defect to the software under testing version
Prediction.
According to the technique and scheme of the present invention, defects count prediction is established by acquiring the release information of passing software version
Model carries out failure prediction to software version to be measured according to the defects count prediction model, provides reference to manager；By software
Bug quantitative forecast Problem Summary is more classification problems, solves more classification problems using Softmax algorithm, obtains defects count
Prediction model；It sends out version and counts practical Bug quantity later, defects count prediction model is strengthened according to practical Bug quantity, prediction is provided
Accuracy rate.
Method provided by the embodiment of the present invention can be performed in the said goods, has the corresponding functional module of execution method and has
Beneficial effect.The not technical detail of detailed description in the present embodiment, reference can be made to method provided by the embodiment of the present invention.
Above-mentioned specific embodiment, does not constitute a limitation on the scope of protection of the present invention.Those skilled in the art should be bright
It is white, design requirement and other factors are depended on, various modifications, combination, sub-portfolio and substitution can occur.It is any
Made modifications, equivalent substitutions and improvements etc. within the spirit and principles in the present invention, should be included in the scope of the present invention
Within.
Claims (10)
1. a kind of Software Defects Predict Methods characterized by comprising
The release information for acquiring passing software version establishes defects count prediction model according to the release information；
The release information of software under testing version is inputted into the defects count prediction model, to carry out to the software under testing version
Failure prediction.
2. the method according to claim 1, wherein establishing defects count prediction mould according to the release information
Type, comprising:
It will be returned in the release information input multi-classification algorithm of the passing software version with carrying out more classification；The release information
Including various features information and actual defects quantity；
The minimum value that the cost function of the multi-classification algorithm is solved according to iteration optimization algorithms, by the corresponding spy of the minimum value
Levy optimal solution of the value of parameter as the characteristic parameter.
3. according to the method described in claim 2, it is characterized in that, the various features information include demand acquisition time, it is each
The corresponding demand number of priority requirements, development time, exploitation code amount, testing time and test case item number.
4. according to the method described in claim 2, it is characterized in that, the release information of software under testing version is inputted the defect
Quantitative forecast model, to carry out failure prediction to the software under testing version, comprising:
The release information of software under testing version is inputted into the defects count prediction model, to obtain the software under testing version institute
Belong to the probability of classification；The multi-classification algorithm is Softmax algorithm；
Using the corresponding defects count of the classification of maximum probability as the prediction defects count of the software under testing version.
5. the method according to claim 1, wherein the method also includes:
After the completion of sending out version, the release information of the software under testing version is updated to actual value；
It is lacked according to the release information training of the release information of the updated software under testing version and passing software version is described
Fall into quantitative forecast model.
6. according to the method described in claim 5, it is characterized in that, the method also includes:
Release information data bank is established, the release information data bank includes the release information of passing software version；
The release information of the updated software under testing version is saved in the release information data bank.
7. a kind of software defect prediction meanss characterized by comprising
Model building module establishes defects count according to the release information for acquiring the release information of passing software version
Prediction model；
Failure prediction module, for the release information of software under testing version to be inputted the defects count prediction model, to institute
It states software under testing version and carries out failure prediction.
8. device according to claim 7, which is characterized in that the model building module is also used to:
It will be returned in the release information input multi-classification algorithm of the passing software version with carrying out more classification；The release information
Including various features information and actual defects quantity；
The minimum value that the cost function of the multi-classification algorithm is solved according to iteration optimization algorithms, by the corresponding spy of the minimum value
Levy optimal solution of the value of parameter as the characteristic parameter.
9. a kind of electronic equipment 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 are real
Now such as method as claimed in any one of claims 1 to 6.
10. a kind of computer-readable medium, is stored thereon with computer program, which is characterized in that described program is held by processor
Such as method as claimed in any one of claims 1 to 6 is realized when row.
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