CN107402797A - A kind of software compilation method and device - Google Patents
A kind of software compilation method and device Download PDFInfo
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- CN107402797A CN107402797A CN201610338595.0A CN201610338595A CN107402797A CN 107402797 A CN107402797 A CN 107402797A CN 201610338595 A CN201610338595 A CN 201610338595A CN 107402797 A CN107402797 A CN 107402797A
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Abstract
The invention discloses a kind of version software Compilation Method and device, belong to software technology field, this method includes：According to the failure structure information knowledge storehouse pre-established, the target frequent item set in knowledge base is determined, wherein, this, which unsuccessfully builds, saves before the item collection of failure structure every time in information knowledge storehouse；According to the dependence between target frequent item set computing module, it is determined that effective Strong association rule；Will effectively strongly connected module binding compiling or early warning simultaneously simultaneously together.The present invention unsuccessfully builds incidence relation between accurate information locating module by history, and effectively strongly connected module binding is compiled together, can maximum possible realize the self-regeneration of compiling project, can guarantee that continuous integrating effect, reduce version issue risk.And can save the human resources of debugging compiling during failure.
Description
Technical field
The present invention relates to the version software Compilation Method and device of software technology field, more particularly to a kind of communications industry.
Background technology
Software version compiling is indispensable link in continuous integrating, past for large software system such as communication system
It is various toward module, and the conjunction coupling degree of intermodule is higher, easily pulls one hair and move the whole body, often because some module one
Small modification causes whole system compiling not pass through.Because the module being related to is various, the personnel being related to are extensive, compile malfunction elimination
Cycle is longer, Severe blockage continuous integrating, causes software version update risk very high.
At present, it is obstructed out-of-date to occur in version compiling, is all often to be fixed a breakdown one by one by attendant, to solve
Compiling not by the problem of, on the one hand this mode can cause attendant to be trapped in maintenance and the fault location of basic environment for a long time
In, while the situation that relation of interdependence influences between multimode when compiling fault location is easily ignored, investigation is obstructed, failure solution
Certainly the cycle is longer；On the other hand, carried out it is difficult to find the professional known about to the dependence of all modules and intermodule
Failture evacuation.
The content of the invention
In view of this, it is an object of the invention to provide a kind of software compilation method and device, compiled with solving artificial solution
That translates problem realizes that difficulty is higher, and the problem of not considering intermodule Correlative Dependence and Influence during compiling failture evacuation.
Technical scheme is as follows used by the present invention solves above-mentioned technical problem：
According to an aspect of the present invention, there is provided a kind of version software Compilation Method, methods described includes：
According to the failure structure information knowledge storehouse pre-established, the target frequent item set in the knowledge base is determined, wherein,
It is described unsuccessfully to build the item collection that failure is built every time before being saved in information knowledge storehouse；
According to the dependence between the target frequent item set computing module, it is determined that effective Strong association rule；
Will effectively strongly connected module binding compiling or early warning simultaneously simultaneously together.
Preferably, this method also includes：
In each compiling failure, obtain the item collection of failure structure and be added in the knowledge base.
Preferably, the failure structure information knowledge storehouse that the basis pre-establishes, the target frequency in the knowledge base is determined
Numerous item collection, further comprises：
According to the item collection in the knowledge base, each rank frequent item set is calculated successively；
The support of each 1 rank item collection in the knowledge base is calculated, 1 rank item collection of the support less than first threshold is carried out
Beta pruning, obtain 1 rank frequent item set；And
According to m-1 rank frequent item sets, m rank item collections are generated using lexicographic order, calculate the support of each m ranks item collection respectively
Degree, the m ranks item collection that default m rank threshold values are less than to support carry out beta pruning, obtain m rank frequent item sets, and to the last remaining one
Untill individual high-order frequent item set, wherein, m is positive integer, and m is more than 1.
Preferably, m rank frequent item set supports are calculated using equation below：
Support (X)=P (X)/P (I)；
Support(X->Y)=P (X ∪ Y)/P (I)；
Wherein, I represents total item collection in the knowledge base, and it is general that Support (X) represents that item collection { X } occurs in total item collection
Rate, Support (X->Y the probability that item collection { X, Y } occurs in total item collection) is represented.
Preferably, the dependence according between the target frequent item set computing module, it is determined that effectively strong association
Rule, further comprise：
The confidence level of each non-NULL item collection in the target frequent item set is calculated, to being unsatisfactory for this default confidence threshold value
Non-NULL item collection carries out beta pruning；
According to the confidence level of the non-NULL item collection left after beta pruning, the mutual lifting degree of non-NULL item collection is calculated；
It is determined that meet that the rule between non-NULL item collection of the lifting degree more than 1 is effective Strong association rule.
Preferably, the confidence level for calculating each non-NULL item collection in the target frequent item set, using equation below：
Confidence(X->Y)=P (Y | X)=P (X ∪ Y)/P (X)；
Wherein, confidence level Confidence (X->Y) represent in the case of with item collection X, by correlation rule " X->Y " is pushed away
Go out the probability with item collection Y.
Preferably, the confidence level according to the non-NULL item collection left after beta pruning, calculating non-NULL item collection is mutual to be carried
Liter degree, using equation below：
Lift(X->Y)=P (Y | X)/P (Y)=P (X ∪ Y)/P (Y)；
Wherein, lifting degree Lift (X->Y) represent contain item collection X under conditions of, the probability simultaneously containing item collection Y, with without
But the ratio between probability of the Y containing item collection under conditions of item collection X；
If Lift (X->Y)>1, then represent regular X->Y is effective Strong association rule；
If Lift (X->Y)<=1, then represent regular X->Y is invalid Strong association rule；
If Lift (X->Y)=1, then it represents that item collection X and item collection Y are separate.
According to another aspect of the present invention, there is provided a kind of version software compilation device include：
Target frequent item set determining unit, the failure structure information knowledge storehouse pre-established for basis, it is determined that described know
Know the target frequent item set in storehouse, wherein, described unsuccessfully build preserves before the item collection of failure structure every time in information knowledge storehouse；
Effective Strong association rule analytic unit, for being closed according to the dependence between the target frequent item set computing module
System, it is determined that effective Strong association rule；
Association compiling construction unit, for effectively strongly connected module to be bound together into compiling or early warning simultaneously simultaneously.
Preferably, target frequent item set determining unit further comprises：
First determination subelement, for the item collection according to the knowledge base, each rank frequent item set is calculated successively；
Second determination subelement, for calculating the support of each 1 rank item collection in the knowledge base, to support less than the
1 rank item collection of one threshold value carries out beta pruning, obtains 1 rank frequent item set；And
3rd determination subelement, for according to m-1 rank frequent item sets, generating m rank item collections using lexicographic order, calculating respectively
The support of each m rank item collections, beta pruning is carried out to m rank item collection of the support less than predetermined threshold value, obtains m rank frequent item sets, directly
To the end untill a remaining high-order frequent item set, wherein, m is positive integer, and m is more than 1.
Preferably, effective Strong association rule analytic unit further comprises：
First analysis subelement, for calculating the confidence level of each non-NULL item collection in the target frequent item set, to discontented
The non-NULL item collection of the default confidence threshold value of foot carries out beta pruning；
Second analysis subelement, for the confidence level according to the non-NULL item collection left after beta pruning, it is mutual to calculate non-NULL item collection
Between lifting degree；
3rd analysis subelement, closed for determining to meet the rule between non-NULL item collection of the lifting degree more than 1 to be effectively strong
Connection rule.
Version software Compilation Method provided by the invention and device, by according to the failure structure information knowledge pre-established
Storehouse, the degree of association between automatic computing module, it is determined that effective Strong association rule；Will be effectively strongly connected according to effective Strong association rule
Module binding compiling or early warning simultaneously simultaneously together.Can maximum possible realize the self-regeneration of compiling project, can guarantee that and hold
Continuous integrated result, reduce version issue risk.And can save the human resources of debugging compiling during failure.
Brief description of the drawings
Fig. 1 is a kind of flow chart of version software Compilation Method provided in an embodiment of the present invention；
Fig. 2 is the flow chart that a kind of target frequent item set provided in an embodiment of the present invention determines method；
Fig. 3 is a kind of flow chart of effectively Strong association rule analysis method provided in an embodiment of the present invention；
Fig. 4 is a kind of structural representation of version software compilation device provided in an embodiment of the present invention；
Fig. 5 is a kind of structural representation of target frequent item set determining unit provided in an embodiment of the present invention；
Fig. 6 is a kind of structural representation of effectively Strong association rule analytic unit provided in an embodiment of the present invention.
The realization, functional characteristics and advantage of the object of the invention will be described further referring to the drawings in conjunction with the embodiments.
Embodiment
In order that technical problems, technical solutions and advantages to be solved are clearer, clear, tie below
Drawings and examples are closed, the present invention will be described in further detail.It should be appreciated that specific embodiment described herein is only
To explain the present invention, it is not intended to limit the present invention.
The embodiment of version software Compilation Method in the embodiment of the present invention is introduced first below, the version software Compilation Method
Executive agent be version software compilation device, the software translating device can be located in server or terminal.
Referring to Fig. 1, for one embodiment of version software Compilation Method in the embodiment of the present invention, this method includes：
The failure structure information knowledge storehouse that S10, basis pre-establish, determines the target frequent item set in the knowledge base.
Wherein, the item collection for the structure that fails every time before being preserved in information knowledge storehouse, the mould of each compiling failure are unsuccessfully built
The corresponding item collection of block.Assuming that I={ i1, i2 ..., ij }, is j different item destination aggregation (mda)s, each ik is referred to as a project.
Item destination aggregation (mda) I is referred to as item collection, and the number of its element is referred to as the length of item collection, and length is that k item collection is referred to as k rank item collections.Item collection
For I={ A, B, C, D, E, F }, I length is 6.The task-set of failure structure is total item collection I a subset every time.
Preferably, referring to Fig. 2, step S10 further comprises：
S101, the item collection according to the knowledge base, each rank frequent item set is calculated successively.
Specifically, minimum support is the minimum lowest importance supported threshold values, represent correlation rule of item collection, frequently
Item collection can refer to the item collection that support is not less than this threshold value, and length is that k frequent item set is properly termed as k rank frequent item sets.
Calculating to 1 rank frequent item set：The support of each 1 rank item collection in the knowledge base is calculated, support is less than
1 rank item collection of first threshold carries out beta pruning, obtains 1 rank frequent item set；
In the embodiment of the present invention, equation below can be used to calculate n rank frequent item set supports：
Support (X)=P (X)/P (I)；
Support(X->Y)=P (X ∪ Y)/P (I)；
Wherein, I represents total item collection in the knowledge base, and it is general that Support (X) represents that item collection { X } occurs in total item collection
Rate, Support (X->Y the probability that item collection { X, Y } occurs in total item collection) is represented.
For example, respectively just like table 1 below module item collection (abbreviation item collection) failure in the compiler task of four structures：
Table 1
| ID | Build the module item collection of failure |
| 1 | A, C, D |
| 2 | B, C, E |
| 3 | A, B, C, E |
| 4 | B, E |
S102, support of the individual module item collection (1 rank item collection) in total item collection is calculated, to less than default minimum support
The 1 rank item collection for spending threshold value carries out beta pruning.
Specifically, assume that minimum support threshold value (i.e. first threshold) is 50%, such as table 2 below, to less than this minimum support
The 1 rank item collection for spending threshold value carries out beta pruning, obtains 1 rank frequent item set { A } { B } { C } { E }.
Table 2
S103, according to m-1 rank frequent item sets, m rank item collections are generated using lexicographic order, calculate each m ranks item collection respectively
Support, beta pruning is carried out to m rank item collection of the support less than predetermined threshold value, obtains m rank frequent item sets, to the last remaining one
Untill individual high-order frequent item set.
Wherein, m is positive integer, and m is more than 1.
Continue then above-mentioned example to illustrate：According to 1 rank frequent item set, according to lexicographic order, 2 rank item collections of generation A,
B }, { A, C }, { A, E }, { B, C }, { B, E }, { C, E }, be unsatisfactory for minimum support threshold value (predetermined threshold value, it is assumed that now for
25%) 2 rank item collections obtain 2 rank frequent item sets { A, C } { B, C }, { B, E } { C, E }, table 3 specific as follows by beta pruning：
Table 3
According to 2 rank frequent item sets, according to lexicographic order, 3 rank item collections are generated, the 3 rank item collections for being unsatisfactory for minimum support are (false
If being 25%) by beta pruning herein, 3 rank frequent item sets { B, C, E }, table 4 specific as follows are obtained：
Table 4
Due to only remaining next frequent item set { B, C, E }, the 3 rank frequent item set so far obtained is target frequent episode
Collection.
In the embodiment of the present invention, if the 1 rank frequent item set only has one, the 1 rank frequent item set is the target
Frequent item set, if 1 rank frequent item set more than one, the obtained 1 rank frequent item set according to, 2 are generated using lexicographic order
Rank frequent item set, the support of each 2 rank frequent item set is calculated respectively, 2 rank item collections of the support less than Second Threshold are carried out
Subtract branch, obtain 2 rank frequent item sets；
If the 2 rank frequent item set more than one, continue to calculate high-order item collection successively, obtain high-order frequent item set, directly
To the end untill a remaining high-order frequent item set.
S20, according to the dependence between target frequent item set computing module, it is determined that effective Strong association rule.
Referring to Fig. 3, step S20 can further comprise：
S201, the confidence level for calculating each non-NULL item collection in the target frequent item set, to being unsatisfactory for this default confidence level
The non-NULL item collection of threshold value carries out beta pruning.
S202, the confidence level according to the non-NULL item collection left after beta pruning, calculate the mutual lifting degree of non-NULL item collection.
S203, determine to meet that the rule between non-NULL item collection of the lifting degree more than 1 is effective Strong association rule.
Wherein, the confidence level for calculating each non-NULL item collection in the target frequent item set, can use equation below：
Confidence(X->Y)=P (Y | X)=P (X ∪ Y)/P (X)；
Wherein, confidence level Confidence (X->Y) represent in the case of with item collection X, by correlation rule " X->Y " is pushed away
Go out the probability with item collection Y.
The confidence level according to the non-NULL item collection left after beta pruning, the mutual lifting degree of non-NULL item collection is calculated, is adopted
Use equation below：
Lift(X->Y)=P (Y | X)/P (Y)=P (X ∪ Y)/P (Y)；
Wherein, lifting degree Lift (X->Y) represent contain item collection X under conditions of, the probability simultaneously containing item collection Y, with without
But the ratio between probability of the Y containing item collection under conditions of item collection X；
Meet the rule of minimum support and min confidence, be called Strong association rule, in Strong association rule, also divide effectively
Strong association rule and invalid Strong association rule.
If Lift (X->Y)>1, then represent regular X->Y is effective Strong association rule；
If Lift (X->Y)<=1, then represent regular X->Y is invalid Strong association rule；
If Lift (X->Y)=1, then it represents that item collection X and item collection Y are separate.
Or gone on to say, calculated respectively in target frequent item set ({ B, C, E }) between single non-NULL item collection with above-mentioned example
Confidence level, it is assumed that minimal confidence threshold 75%, the rule for being unsatisfactory for this minimal confidence threshold will be by beta pruning, such as following table
Shown in 5：
Table 5
Now, non-NULL item collection B, E is left.
Continue the confidence level according to the non-NULL item collection left after beta pruning, calculate the mutual lifting degree of non-NULL item collection, such as
Shown in lower：
Lift(B->E)=100%/75%=1.33；
Lift(E->B)=100%/75%=1.33；
Now, effective Strong association rule B- is obtained>E and E->B.
S30, will effectively strongly connected module binding compiling or early warning simultaneously simultaneously together.
Specifically, effectively strongly connected module binding is compiled into the maximum self-regeneration of energy simultaneously together, will be effective
Early warning simultaneously can reduce the human resources of artificial investigation mistake together for strongly connected module binding, improve efficiency.
Or illustrated with above-mentioned example, step S20 obtains effective Strong association rule B->E and E->B, fail when E is built
When, B can be linked simultaneously and built, module B can also be notified simultaneously；When E builds failure, B can be linked structure simultaneously
Build, can also simultaneously notification module E, so B and E are associated, it is contemplated that the incidence relation between disparate modules, according to
The effective Strong association rule carries out version compiling, can maximum self-regeneration, ensure continuous integrating effect, reduce version
This issue risk.When can not self-regeneration when, pass through the efficiency that early warning simultaneously improves artificial investigation mistake.
It should be noted that in the embodiment of the present invention, subsequently after one section of preset time, knowledge base may update, this
When can also correct effective Strong association rule again, in each compiling failure, the item collection for obtaining failure structure is simultaneously added to institute
State in knowledge base.
Version software Compilation Method provided in an embodiment of the present invention, by according to the failure structure information knowledge pre-established
Storehouse, the degree of association between automatic computing module, it is determined that effective Strong association rule；Will be effectively strongly connected according to effective Strong association rule
Module binding compiling or early warning simultaneously simultaneously together.Can maximum possible realize the self-regeneration of compiling project, can guarantee that and hold
Continuous integrated result, reduce version issue risk.And can save the human resources of debugging compiling during failure.
The embodiment of version software compilation device in the embodiment of the present invention is described below.
Referring to Fig. 5, being one embodiment schematic diagram of version software compilation device in the embodiment of the present invention, the version fills
Put including target frequent item set determining unit 10, effective Strong association rule analytic unit 20 and associate compiling construction unit 30.
Target frequent item set determining unit 10, the failure structure information knowledge storehouse pre-established for basis, it is determined that described
Target frequent item set in knowledge base, wherein, described unsuccessfully build preserves before the item of failure structure every time in information knowledge storehouse
Collection.
Preferably, target frequent item set determining unit 10 further comprises：
First determination subelement 101, for the item collection according to the knowledge base, each rank frequent item set is calculated successively；
Second determination subelement 102 is low to support for calculating the support of each 1 rank item collection in the knowledge base
Beta pruning is carried out in 1 rank item collection of first threshold, obtains 1 rank frequent item set；And
3rd determination subelement 103, for according to m-1 rank frequent item sets, generating m rank item collections using lexicographic order, respectively
The support of each m ranks item collection is calculated, beta pruning is carried out to m rank item collection of the support less than predetermined threshold value, obtains m rank frequent episodes
Collection, to the last untill a remaining high-order frequent item set, wherein, m is positive integer, and m is more than 1.
Optionally, the 3rd determination subelement 103 is specifically used for calculating the support of n ranks frequent item set using equation below
Degree：
Support (X)=P (X)/P (I)；
Support(X->Y)=P (X ∪ Y)/P (I)；
Wherein, I represents total item collection in the knowledge base, and it is general that Support (X) represents that item collection { X } occurs in total item collection
Rate, Support (X->Y the probability that item collection { X, Y } occurs in total item collection) is represented.
Effective Strong association rule analytic unit 20, for being closed according to the dependence between the target frequent item set computing module
System, it is determined that effective Strong association rule.
Preferably, effective Strong association rule analytic unit 20 further comprises：
First analysis subelement 201, for calculating the confidence level of each non-NULL item collection in the target frequent item set, to not
Meet that the non-NULL item collection of default confidence threshold value carries out beta pruning；
Second analysis subelement 202, for the confidence level according to the non-NULL item collection left after beta pruning, calculates non-NULL item collection phase
Lifting degree between mutually；
3rd analysis subelement 203, for determining to meet the rule between non-NULL item collection of the lifting degree more than 1 to be effectively strong
Correlation rule.
Optionally, the first analysis subelement 201 is specifically used for each non-NULL item collection in the target frequent item set is calculated
Confidence level when, using equation below：
Confidence(X->Y)=P (Y | X)=P (X ∪ Y)/P (X)；
Wherein, confidence level Confidence (X->Y) represent in the case of with item collection X, by correlation rule " X->Y " is pushed away
Go out the probability with item collection Y.
Optionally, the second analysis subelement 202 is specifically used for the confidence level according to the non-NULL item collection left after beta pruning, calculates
The mutual lifting degree of non-NULL item collection, using equation below：
Lift(X->Y)=P (Y | X)/P (Y)=P (X ∪ Y)/P (Y)；
Wherein, lifting degree Lift (X->Y) represent contain item collection X under conditions of, the probability simultaneously containing item collection Y, with without
But the ratio between probability of the Y containing item collection under conditions of item collection X；
If Lift (X->Y)>1, then represent regular X->Y is effective Strong association rule；
If Lift (X->Y)<=1, then represent regular X->Y is invalid Strong association rule；
If Lift (X->Y)=1, then it represents that item collection X and item collection Y are separate.
Association compiling construction unit 30, for effectively strongly connected module to be bound together into compiling or simultaneously pre- simultaneously
It is alert.
Specifically, effectively strongly connected module binding is compiled into the maximum self-regeneration of energy simultaneously together, will be effective
Early warning simultaneously can reduce the human resources of artificial investigation mistake together for strongly connected module binding, improve efficiency.
Preferably, said apparatus can also include amending unit, in each compiling failure, obtaining failure structure
Item collection is simultaneously added in the knowledge base.
It should be noted that said apparatus embodiment belongs to same design with embodiment of the method, it is detailed that it implements process
See embodiment of the method, and the technical characteristic in embodiment of the method is corresponding applicable in device embodiment, repeats no more here.
Version software compilation device provided in an embodiment of the present invention, by according to the failure structure information knowledge pre-established
Storehouse, the degree of association between automatic computing module, it is determined that effective Strong association rule；Will be effectively strongly connected according to effective Strong association rule
Module binding compiling or early warning simultaneously simultaneously together.Can maximum possible realize the self-regeneration of compiling project, can guarantee that and hold
Continuous integrated result, reduce version issue risk.And can save the human resources of debugging compiling during failure.
Through the above description of the embodiments, those skilled in the art can be understood that above-described embodiment side
Method can add the mode of required general hardware platform to realize by software, naturally it is also possible to realized by hardware, but a lot
In the case of the former be more preferably embodiment.Based on such understanding, technical scheme is substantially in other words to existing
The part that technology contributes can be embodied in the form of software product, and the computer software product is stored in a storage
In medium (such as ROM/RAM, magnetic disc, CD), including some instructions to cause a station terminal equipment (can be mobile phone, calculate
Machine, server, air conditioner, or network equipment etc.) perform method described in each embodiment of the present invention.
Above by reference to the preferred embodiments of the present invention have been illustrated, not thereby limit to the interest field of the present invention.This
Art personnel do not depart from all any modification, equivalent and improvement made in the scope of the present invention and essence, all should be at this
Within the interest field of invention.
Claims (10)
1. a kind of version software Compilation Method, it is characterised in that this method includes：
According to the failure structure information knowledge storehouse pre-established, the target frequent item set in the knowledge base is determined, wherein, it is described
The item collection of each failure structure before being saved in failure structure information knowledge storehouse；
According to the dependence between the target frequent item set computing module, it is determined that effective Strong association rule；
Will effectively strongly connected module binding compiling or early warning simultaneously simultaneously together.
2. version software Compilation Method according to claim 1, it is characterised in that methods described also includes：
In each compiling failure, obtain the item collection of failure structure and be added in the knowledge base.
3. version software Compilation Method according to claim 1, it is characterised in that the failure structure that the basis pre-establishes
Information knowledge storehouse is built, the target frequent item set in the knowledge base is determined, further comprises：
According to the item collection of the knowledge base, each rank frequent item set is calculated successively；
The support of each 1 rank item collection in the knowledge base is calculated, 1 rank item collection of the support less than first threshold is cut
Branch, obtains 1 rank frequent item set；And
According to m-1 rank frequent item sets, m rank item collections are generated using lexicographic order, calculate the support of each m ranks item collection respectively, it is right
Support carries out beta pruning less than the m ranks item collection of default m rank threshold values, obtains m rank frequent item sets, to the last remaining one
Untill high-order frequent item set, wherein, m is positive integer, and m is more than 1.
4. version software Compilation Method according to claim 3, it is characterised in that it is frequent that m ranks are calculated using equation below
Item collection support：
Support (X)=P (X)/P (I)；
Support(X->Y)=P (X ∪ Y)/P (I)；
Wherein, I represents total item collection in the knowledge base, and Support (X) represents the probability that item collection { X } occurs in total item collection,
Support(X->Y the probability that item collection { X, Y } occurs in total item collection) is represented.
5. according to any described version software Compilation Method in Claims 1-4, it is characterised in that described according to the mesh
The dependence between frequent item set computing module is marked, it is determined that effective Strong association rule, further comprises：
The confidence level of each non-NULL item collection in the target frequent item set is calculated, the non-NULL to being unsatisfactory for this default confidence threshold value
Item collection carries out beta pruning；
According to the confidence level of the non-NULL item collection left after beta pruning, the mutual lifting degree of non-NULL item collection is calculated；
It is determined that meet that the rule between non-NULL item collection of the lifting degree more than 1 is effective Strong association rule.
6. version software Compilation Method according to claim 5, it is characterised in that described to calculate the target frequent item set
In each non-NULL item collection confidence level, using equation below：
Confidence(X->Y)=P (Y | X)=P (X ∪ Y)/P (X)；
Wherein, confidence level Confidence (X->Y) represent in the case of with item collection X, by correlation rule " X->Y " releases tool
There is item collection Y probability.
7. version software Compilation Method according to claim 6, it is characterised in that described according to the non-NULL left after beta pruning
The confidence level of item collection, the mutual lifting degree of non-NULL item collection is calculated, using equation below：
Lift(X->Y)=P (Y | X)/P (Y)=P (X ∪ Y)/P (Y)；
Wherein, lifting degree Lift (X->Y) represent under conditions of containing item collection X, the probability simultaneously containing item collection Y, and without item collection
But the ratio between probability of the Y containing item collection under conditions of X；
If Lift (X->Y)>1, then represent regular X->Y is effective Strong association rule；
If Lift (X->Y)<=1, then represent regular X->Y is invalid Strong association rule；
If Lift (X->Y)=1, then it represents that item collection X and item collection Y are separate.
8. a kind of version software compilation device, it is characterised in that described device includes：
Target frequent item set determining unit, for according to the failure structure information knowledge storehouse pre-established, determining the knowledge base
In target frequent item set, wherein, it is described unsuccessfully build preserved in information knowledge storehouse before failure structure every time item collection；
Effective Strong association rule analytic unit, for according to the dependence between the target frequent item set computing module, really
Fixed effective Strong association rule；
Association compiling construction unit, for effectively strongly connected module to be bound together into compiling or early warning simultaneously simultaneously.
9. version software compilation device according to claim 8, it is characterised in that the target frequent item set determining unit
Further comprise：
First determination subelement, for the item collection according to the knowledge base, each rank frequent item set is calculated successively；
Second determination subelement, for calculating the support of each 1 rank item collection in the knowledge base, the first threshold is less than to support
1 rank item collection of value carries out beta pruning, obtains 1 rank frequent item set；And
3rd determination subelement, for according to m-1 rank frequent item sets, generating m rank item collections using lexicographic order, calculating respectively each
The support of m rank item collections, beta pruning is carried out to m rank item collection of the support less than predetermined threshold value, m rank frequent item sets are obtained, until most
Afterwards untill a remaining high-order frequent item set, wherein, m is positive integer, and m is more than 1.
10. version software compilation device according to claim 8 or claim 9, it is characterised in that the effectively Strong association rule point
Analysis unit further comprises：
First analysis subelement, it is pre- to being unsatisfactory for for calculating the confidence level of each non-NULL item collection in the target frequent item set
The non-NULL item collection of confidence threshold is set to carry out beta pruning；
Second analysis subelement, for the confidence level according to the non-NULL item collection left after beta pruning, it is mutual to calculate non-NULL item collection
Lifting degree；
3rd analysis subelement, for determining to meet the rule between non-NULL item collection of the lifting degree more than 1 as effectively strong association rule
Then.
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| CN111368871B (en) * | 2019-12-18 | 2023-04-07 | 中国人民解放军战略支援部队信息工程大学 | Multi-feature information fusion method, device and system |
| CN111221650A (en) * | 2019-12-31 | 2020-06-02 | 青岛海尔科技有限公司 | System resource recovery method and device based on process type association |
| CN115617698B (en) * | 2022-12-15 | 2023-04-18 | 北京航空航天大学 | Software defect measurement element selection method based on association rule network |
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