CN109299007A - A kind of defect repair person's auto recommending method - Google Patents

A kind of defect repair person's auto recommending method Download PDF

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Publication number
CN109299007A
CN109299007A CN201811088636.0A CN201811088636A CN109299007A CN 109299007 A CN109299007 A CN 109299007A CN 201811088636 A CN201811088636 A CN 201811088636A CN 109299007 A CN109299007 A CN 109299007A
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report
defect
defect report
similarity
person
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张涛
杨泽浩
栾思敏
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Harbin Engineering University
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/36—Preventing errors by testing or debugging software
    • G06F11/3668—Software testing
    • G06F11/3672—Test management
    • G06F11/3692—Test management for test results analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/10—Text processing
    • G06F40/194—Calculation of difference between files
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/20—Natural language analysis
    • G06F40/279—Recognition of textual entities
    • G06F40/289—Phrasal analysis, e.g. finite state techniques or chunking
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/23—Clustering techniques
    • G06F18/232—Non-hierarchical techniques
    • G06F18/2321—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions
    • G06F18/23213—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions with fixed number of clusters, e.g. K-means clustering
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
    • G06F18/2411—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on the proximity to a decision surface, e.g. support vector machines

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  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Audiology, Speech & Language Pathology (AREA)
  • Computational Linguistics (AREA)
  • General Health & Medical Sciences (AREA)
  • Health & Medical Sciences (AREA)
  • Artificial Intelligence (AREA)
  • Computer Hardware Design (AREA)
  • Quality & Reliability (AREA)
  • Information Retrieval, Db Structures And Fs Structures Therefor (AREA)

Abstract

The invention proposes a kind of defect repair person's auto recommending methods, and first after a latest report is submitted to system, this method will search for similar report；Then correlated characteristic, such as candidate defect repair person are extracted from similar report.Finally factor in summary is recommended.Experiments have shown that the method for the invention not only increases a possibility that reparation, avoids excessive repetition and recommend, and reduce repair time.

Description

A kind of defect repair person's auto recommending method
Technical field
The invention belongs to technical field of software engineering, more particularly to a kind of defect repair person's auto recommending method.
Background technique
With the increase of software system complexity and scale, the generation of software defect is also more and more frequent, or even causes huge Huge economic loss, repairing defect becomes a very urgent job.As the defect tracking system of Bugzilla one kind uses A large amount of developer manually carries out unified investigation to defect, and the exploitation for needing defect information is fed back in the form of defect report Person.So they are faced with some problems.It repeats to recommend to be one of them, the defect and formation generated daily due to software systems Defect report substantial amounts, content it is similar report, similar defect easily recommend different developers, cause repeat push away It recommends, so as to cause the difficulty and waste of time in reparation.
Summary of the invention
The invention aims to solve problems of the prior art, proposes a kind of defect repair person and recommend automatically Method.The method of the invention not only increases a possibility that reparation, avoids excessive repetition and recommends, and reduces reparation Time.
The purpose of the present invention is achieved through the following technical solutions: a kind of defect repair person's auto recommending method, including following Step:
Step 1: newly-generated defect report is added to database；
Step 2: being pre-processed using natural language processing technique to defect report, and being calculated using the cosine law should The similarity of defect report and other defect report；
Step 3: newly-generated defect report is referred to and the highest one kind of his similarity using support vector machines In；
Step 4: extracting correlated characteristic；
Step 5: executing reparation person's proposed algorithm, final reparation person's recommendation list is obtained.
Further, the similarity that the defect report and other defect report are calculated using the cosine law, specifically:
The similarity between two defect reports is calculated using cosine similarity, formula is as follows:
Wherein, ViAnd VjIndicate the vector of different defect reports, WkiRefer to the weight of k-th of word in defect report di, WkjRefer to The weight of k-th of word in defect report dj, n represent the size of word set；
The value of two weights is calculated by TF-IDF, and formula is as follows:
Wherein, tfkiIt is the frequency of k-th of word in defect report di, N is total defect report number, nkRepresent k-th of word extremely Occurs primary defect report number less；
Title and the description of defect report are only extracted, is independently calculated between two defect report titles between description Text similar value, obtain the similarity (SBBR) between two defect reports according to the following formula:
SBBR(bi, bj)=α × tsij+(1-α)×dsij
Wherein, tsijRefer to the text similar value between defect report bi and bj title, dsijRefer to defect report bi and bj Text similar value between description, α index topic relative weighting shared in defect report.
Further, the step 3 specifically:
Using K-means clustering algorithm, existing defect report is clustered to obtain similar report collection, the K-means is poly- Class algorithm description are as follows: 1) K defect report is in space arbitrarily placed as reference data points, and guarantees that they disperse enough, The reference data points are by the centroid as initial cluster；2) each defect report is assigned to that closest to him of centroid In cluster；3) position of form center of each cluster is recalculated after all defect reports are all assigned；4) step 2) and 3) straight is repeated Until centroid is no longer mobile；
The similarity degree between two defect reports is being defined as described two defect reports and centroid in clustering algorithm The distance between；The clustering algorithm will be iterated always process until error metric E reaches minimum, claim centroid not at this time It moves again；The formula of E is as follows:
Wherein, K is the number of cluster, and N is the sum for participating in the defect report of cluster, and Xn is the vector of n-th of defect report, μjRefer to the centroid of j-th of cluster；
All defect report in training set is all assigned in each similar report collection { B1, B2 ..., Bn }, report collection Each of report it is similar；When a new defect report arrives, the new defect report and similar report are calculated The similarity for concentrating each to report, be just regarded as when finding maximum similarity MAXSBBR new defect report to it is similar Similarity between report collection, then distinguishes whether new defect report belongs to some collection using SVM；SVMPredict is exactly SVM distinguishes the expression of model, and SVMPredict will find out a possibility that new defect report belongs to each collection, when obtaining After the value of some SVMPredict, its maximum value is taken to be compared with threshold θ, if more than θ, then belongs to some collection, if being less than Then new defect report is independent at a collection, dissimilar with other reports.
Further, the correlated characteristic is reparation person and repair time.
Further, the step 5 specifically:
After the correlated characteristic for extracting defect report, the factor for influencing presentee is quantified, the factor is to repair Efficiency and reparation experience；
Remediation efficiency:
Wherein, deviCandidate restoration person is represented, bj refers to the person of being repaired deviThe defect report repaired, F (bj,devi) refer to The repair time of defect report b, n refer to reparation person deviThe report total of reparation；
Reparation experience:
Wherein, " # " indicates " ... number "；
It executes reparation person's proposed algorithm (DRA):
Wherein, β and γ is the weight of two different factors, meets β+γ=1, M, which indicates to work as, has new defect report to reach When, all recommendable numbers.
The advantages of the method for the invention are as follows: when carrying out similarity calculation using the cosine law, report is described It is assigned to different weights respectively with report heading, indicates the importance difference of the corresponding word in these two types of texts to improve information The accuracy of retrieval.Consider the association attributes of recommended reparation person, such as the efficiency of reparation person, the experience etc. of reparation person, As the whether suitable foundation of Standard Judgement reparation person.
Detailed description of the invention
Fig. 1 is a kind of flow diagram of defect repair person's auto recommending method of the present invention；
Fig. 2 is that F-measure value-reparation person of each method recommends number curve figure.
Specific embodiment
Technical solution in the embodiment of the present invention that following will be combined with the drawings in the embodiments of the present invention carries out clear, complete Ground description, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.Based on this Embodiment in invention, every other reality obtained by those of ordinary skill in the art without making creative efforts Example is applied, shall fall within the protection scope of the present invention.
In conjunction with Fig. 1, the present invention proposes a kind of defect repair person's auto recommending method, comprising the following steps:
Step 1: newly-generated defect report is added to database；
Step 2: pre-processing using natural language processing technique (NLP) to defect report, and utilize cosine law meter Calculate the similarity of the defect report and other defect report；The pretreatment specifically:
NLTK and TEXTBLOB are performed the steps of in the library system loads python
(1) segment: defect report or user comment information are cut into several vocabulary, these vocabulary are used to calculate text This similarity.
(2) stop-word removes: some stop-words (such as " the " " a " " are " etc.) frequently occur in English text but There is no any specific meaning to defect location.Stop word list according to WordNet, system will remove these vocabulary.
(3) root: all words will be converted into their root morphology, that is to say, that third-person singular, mistake When going and the tenses such as future tense can be converted into the original form of vocabulary.
(4) it noun and verb screening: is identified in defect report and user comment information by load POS labeling module Verb and noun.Only these vocabulary are used to calculate text similarity, because they are most representative meanings in text The vocabulary of justice.
Step 3: newly-generated defect report is referred to and the highest one kind of his similarity using support vector machines In；
Step 4: extracting correlated characteristic；The correlated characteristic is reparation person and repair time.
Step 5: executing reparation person's proposed algorithm, final reparation person's recommendation list is obtained.
The similarity that the defect report and other defect report are calculated using the cosine law, specifically:
The similarity between two defect reports is calculated using cosine similarity, formula is as follows:
Wherein, ViAnd VjIndicate the vector of different defect reports, WkiRefer to the weight of k-th of word in defect report di, WkjRefer to The weight of k-th of word in defect report dj, n represent the size of word set；
The value of two weights is calculated by TF-IDF, and formula is as follows:
Wherein, tfkiIt is the frequency of k-th of word in defect report di, N is total defect report number, nkRepresent k-th of word extremely Occurs primary defect report number less；
Title and the description of defect report are only extracted, is independently calculated between two defect report titles between description Text similar value, obtain the similarity (SBBR) between two defect reports according to the following formula:
SBBR(bi, bj)=α × tsij+(1-α)×dsij
Wherein, tsijRefer to the text similar value between defect report bi and bj title, dsijRefer to defect report bi and bj Text similar value between description, α index topic relative weighting shared in defect report.
The step 3 specifically:
When a new defect report arrives, the similar defect report retrieval based on SVM will start to execute, for new report Announcement finds one and the highest class of his similarity.It needs to create example before this to enable SVM to be used, this is just needed using K- Means clustering algorithm clusters existing defect report to obtain similar report collection, the K-means clustering algorithm description are as follows: 1) K defect report is arbitrarily placed in space as reference data points, and guarantees that they disperse enough, and the reference data points will Centroid as initial cluster；2) each defect report is assigned in that closest to him cluster of centroid；3) when all Defect report recalculates the position of form center of each cluster after being all assigned；4) step 2) is repeated and 3) until centroid no longer movement is Only；
The similarity degree between two defect reports is being defined as described two defect reports and centroid in clustering algorithm The distance between (or data point)；The clustering algorithm will be iterated always process until error metric E reaches minimum, Centroid is claimed no longer to move at this time；The formula of E is as follows:
Wherein, K is the number of cluster, and N is the sum for participating in the defect report of cluster, and Xn is the vector of n-th of defect report, μjRefer to the centroid of j-th of cluster；
All defect report in training set is all assigned in each similar report collection { B1, B2 ..., Bn }, report collection Each of report it is similar；When a new defect report arrives, the new defect report and similar report are calculated The similarity for concentrating each to report, be just regarded as when finding maximum similarity MAXSBBR new defect report to it is similar Similarity between report collection, then distinguishes whether new defect report belongs to some collection using SVM；SVMPredict is exactly SVM distinguishes the expression of model, and SVMPredict will find out a possibility that new defect report belongs to each collection, when obtaining After the value of some SVMPredict, its maximum value is taken to be compared with threshold θ, if more than θ, then belongs to some collection, if being less than Then new defect report is independent at a collection, dissimilar with other reports.
The step 5 specifically:
After the correlated characteristic for extracting defect report, the factor for influencing presentee is quantified, the factor is to repair Efficiency and reparation experience；
Remediation efficiency:
Wherein, deviCandidate restoration person is represented, bj refers to the person of being repaired deviThe defect report repaired, F (bj, devi) refer to The repair time of defect report b, n refer to reparation person deviThe report total of reparation；
Reparation experience:
Wherein, " # " indicates " ... number "；
It executes reparation person's proposed algorithm (DRA):
Wherein, β and γ is the weight of two different factors, meets β+γ=1, M, which indicates to work as, has new defect report to reach When, all recommendable numbers.
The present invention has chosen 1888 parts labeled as settled defect report from the open defect warehouse open source projects JBOSS Accuse, the method for the invention be trained, is assessed, Fig. 2 shows that F value is better than other methods, it is maximum up to 82.5% and The DREX with Frequency to behave oneself best in other methods, only 80%.The method of the invention is then embodied in table 1 The significant effect of defect repair time is reduced, average cost is 01 days two months, DREX with more second-best than effect The 01 days three months reduction one months that Degree is spent.(note: F-measure score value is a kind of measurement model accuracy Index, be considered as the weighted harmonic mean value for accuracy rate and recall rate).
Table 1 predicts repair time comparison result
Note: SVM-based, which refers to, calls the report being solved in SVM study defect library to make when there is latest report to enter It is assigned in existing classification with classifier, and the suitable developer of sub-fraction is recommended to repair defect.DREX with Frequency, Degree are a kind of measurements for reparation person's grading, and Frequency refers to comment of the reparation person in report Record, Degree refer to the connection number of a certain node.
Above to a kind of defect repair person's auto recommending method provided by the present invention, it is described in detail, herein Apply that a specific example illustrates the principle and implementation of the invention, the explanation of above example is only intended to help Understand method and its core concept of the invention；At the same time, for those skilled in the art, according to the thought of the present invention, There will be changes in the specific implementation manner and application range, in conclusion the content of the present specification should not be construed as to this The limitation of invention.

Claims (5)

1. a kind of defect repair person's auto recommending method, which comprises the following steps:
Step 1: newly-generated defect report is added to database；
Step 2: pre-processing using natural language processing technique to defect report, and the defect is calculated using the cosine law The similarity of report and other defect report；
Step 3: using support vector machines by newly-generated defect report be referred to his similarity it is highest one kind in；
Step 4: extracting correlated characteristic；
Step 5: executing reparation person's proposed algorithm, final reparation person's recommendation list is obtained.
2. the method according to claim 1, wherein described calculate the defect report and other using the cosine law The similarity of defect report, specifically:
The similarity between two defect reports is calculated using cosine similarity, formula is as follows:
Wherein, ViAnd VjIndicate the vector of different defect reports, WkiRefer to the weight of k-th of word in defect report di, WkjRefer to defect Report the weight of k-th of word in dj, n represents the size of word set；
The value of two weights is calculated by TF-IDF, and formula is as follows:
Wherein, tfkiIt is the frequency of k-th of word in defect report di, N is total defect report number, nkK-th of word is represented at least to go out Now primary defect report number；
Title and the description of defect report are only extracted, the text between two defect report titles between description is independently calculated This similar value obtains the similarity (SBBR) between two defect reports according to the following formula:
SBBR(bi, bj)=α × tsij+(1-α)xdsij
Wherein, tsijRefer to the text similar value between defect report bi and bj title, dsijRefer to that defect report bi and bj are described Between text similar value, α index topic relative weighting shared in defect report.
3. according to the method described in claim 2, it is characterized in that, the step 3 specifically:
Using K-means clustering algorithm, existing defect report is clustered to obtain similar report collection, the K-means cluster is calculated Method description are as follows: 1) K defect report is in space arbitrarily placed as reference data points, and guarantees that they disperse enough, it is described Reference data points are by the centroid as initial cluster；2) each defect report is assigned to that closest to him cluster of centroid In；3) position of form center of each cluster is recalculated after all defect reports are all assigned；4) repeat step 2) and 3) until Until centroid no longer moves；
The similarity degree between two defect reports is being defined as between described two defect reports and centroid in clustering algorithm Distance；The clustering algorithm will be iterated always process until error metric E reaches minimum, and title centroid is no longer moved at this time It is dynamic；The formula of E is as follows:
Wherein, K is the number of cluster, and N is the sum for participating in the defect report of cluster, and Xn is the vector of n-th of defect report, μjRefer to The centroid of j-th of cluster；
All defect report in training set is all assigned in each similar report collection { B1, B2 ..., Bn }, reports concentration Each report is similar；When a new defect report arrives, calculates the new defect report and similar report is concentrated The similarity of each report is just regarded as new defect report and similar report when finding maximum similarity MAXSBBR Then similarity between collection distinguishes whether new defect report belongs to some collection using SVM；SVMPredict is exactly SVM Distinguish the expression of model, SVMPredict will find out a possibility that new defect report belongs to each collection, when being owned SVMPredict value after, take its maximum value to be compared with threshold θ, if more than θ, then belong to some collection, if be less than if New defect report is independent at a collection, dissimilar with other reports.
4. according to the method described in claim 3, it is characterized in that, the correlated characteristic is reparation person and repair time.
5. according to the method described in claim 4, it is characterized in that, the step 5 specifically:
After the correlated characteristic for extracting defect report, the factor for influencing presentee is quantified, the factor is remediation efficiency With the experience of reparation；
Remediation efficiency:
Wherein, deviCandidate restoration person is represented, bj refers to the person of being repaired deviThe defect report repaired, F (bj,devi) refer to defect report The repair time of b is accused, n refers to reparation person deviThe report total of reparation；
Reparation experience:
Wherein, " # " indicates " ... number "；
It executes reparation person's proposed algorithm (DRA):
Wherein, β and γ is the weight of two different factors, meets β+γ=1, and M is indicated when there is new defect report to reach, institute There is recommendable number.
CN201811088636.0A 2018-09-18 2018-09-18 A kind of defect repair person's auto recommending method Pending CN109299007A (en)

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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110442514A (en) * 2019-07-11 2019-11-12 扬州大学 The method that defect repair is recommended is realized based on learning algorithm
CN112667492A (en) * 2020-11-06 2021-04-16 北京工业大学 Recommendation method for software defect report repairer
WO2021143056A1 (en) * 2020-01-16 2021-07-22 平安科技（深圳）有限公司 Text conclusion intelligent recommendation method and apparatus, computer device and computer-readable storage medium

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Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110442514A (en) * 2019-07-11 2019-11-12 扬州大学 The method that defect repair is recommended is realized based on learning algorithm
CN110442514B (en) * 2019-07-11 2024-01-12 扬州大学 Method for realizing defect repair recommendation based on learning algorithm
WO2021143056A1 (en) * 2020-01-16 2021-07-22 平安科技（深圳）有限公司 Text conclusion intelligent recommendation method and apparatus, computer device and computer-readable storage medium
CN112667492A (en) * 2020-11-06 2021-04-16 北京工业大学 Recommendation method for software defect report repairer
CN112667492B (en) * 2020-11-06 2024-03-08 北京工业大学 Software defect report repairman recommendation method

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Application publication date: 20190201