Cost-Sensitive Margin Distribution Optimization for Software Bug Localization

HTML
DOWNLOAD

Μέγεθος: px

Εμφάνιση ξεκινά από τη σελίδα:

Download "Cost-Sensitive Margin Distribution Optimization for Software Bug Localization"

Σίβύλλα Κόριννα Καλογιάννης
6 χρόνια πριν
Προβολές:

ISSN 000-9825, CODEN RUXUEW E-ail: jos@iscas.ac.cn Journal of Software,207,28():3072 3079 [doi: 0.3328/j.cnki.jos.005345] http://www.jos.org.cn. Tel: +86-0-62562563, ((), 20023) :, E-ail: li@lada.nju.

1 ISSN , CODEN RUXUEW E-ail: Journal of Software,207,28(): [doi: /j.cnki.jos ] Tel: , ((), 20023) :, E-ail: :,,,.,,.,,,,,.,(cost-sensitive argin distribution optiization, CSMDO),,,. : ;;;; : TP3 :,..,207,28(): org.cn/ /5345.ht : Xie Z, Li M. Cost-Sensitive argin distribution optiization for software bug localization. Ruan Jian Xue Bao/ Journal of Software, 207,28(): (in Chinese). Cost-Sensitive Margin Distribution Optiization for Software Bug Localization XIE Zheng, LI Ming (State Key Laboratory for Novel Software Technology (Nanjing University), Nanjing 20023, China) Abstract: It is costly to identify bugs fro nuerous source code files in a large software project. Thus, locating bug autoatically and effectively becoes a worthy proble. Bug report is one of the ost valuable source of bug description, and precisely locating related source codes linked to the bug reports can help reducing software developent cost. Currently, ost of the research on bug localization based on deep neural networks focus on design of network structures while lacking attention to the loss function, which ipacts the perforance significantly in prediction tasks. In this paper, a cost-sensitive argin distribution optiization (CSMDO) loss function is proposed and applied to deep neural networks. This new ethod is capable of handling the ibalance of software defect data sets, and iproves the accuracy significantly. Key words: software ining; achine learning; bug localization; convolutional neural network; argin distribution optiization,,,.,, [,2]. [3 0],,,;,. : ( , ) Foundation ite: National Natural Science Foundation of China ( , ). : ; : ; :

2 : 3073,,.,,,.,.,,,.,.,,.,,,.,Poshyvanyk [5] ;Lukins [6] LDA(latent Dirichlet allocation),;gay [7] (vector space odel, VSM);Zhou [8] BugLocator, VSM.,.La [9] auto encoder ;Huo [0],,.,,. [9 ] sigoid ( softax ),.,., AdaBoost [2,3].Zhang [4],(optial argin distribution achine, ODM),,., ODM.,(cost-sensitive argin distribution optiization, CSMDO),,,. CSMDO.,CSMDO ,.,.,,. C={c,c 2,,c n },R={r,r 2,,r n2 },y ij Y={+, }., f:r C, sgn(f(r i,c j )) Y={+, } r i c j.: in L ( f ) = λ L( f( r, c ), y ) + Ω( f) () f i, j i j ij,l(, ),Ω( ).λ,..2,[5,6]. [0] :

3 3074 Journal of Software Vol.28, No., Noverber 207,.,,., x ij =φ(r i,c j ),.,, f(r i,c j ). Fig. Convolutional neural network structure for bug localization one-hot, [7]., V={ bug, class, contains, file, is, this }, S= this file contains bug this file x = (2) contains bug one-hot,.,., [8]. 2 2 : 2,; 2,.,.,,.,,,.

4 : ,,.,, AdaBoost.Schapire [9] AdaBoost.Breian [20], boosting Arc-gv,.,Rayzin [2],Arc-gv,,., AdaBoost, [2,3].Zhang, ODM [4].ODM w, ;,ODM.,,.,,: in w w+ C+ i i 0 i,, 2 ξ + C ξ + C ε w ξε yi=+ yi = i= s.t. yw x D ξ, yw x + D+ ε, i i i i i i ξ 0, ε 0, i =,...,. i i,,; 2,. D,. C + C., C + >C,. C ,(3) : C in L( w) w w ax{0, D yw x} w + 2 = + i i + 2 yi =+ C ax{0, D yiw xi} + ax{0, yiw xi D} yi = i=,... (x i,y i ), L(w,x i,y i ) L(w). :L(w) 2C+ L( w) = w+ ( yiw xi + D ) yixii( yiw xi < D) I( yi =+ ) + 2C 2C 0 i= i= i= ( yw x + D ) y xi( yw x < D) I( y = ) + i i i i i i i ( yw x D ) y xi( yw x > + D) C i i i i i i (3) (4) (5)

5 3076 Journal of Software Vol.28, No., Noverber 207,I( ),, 0.,: E[ L( wx, i, yi)] = w+ 2 C+ E[( yiw xi + D ) yixii( yiw xi < D) I( yi =+ )] + 2 C E[( yiw xi + D ) yixii( yiw xi < D) I( yi = )] + 2 C0E[( yiw xi D ) yixii( yiw xi > + D)] 2C+ = w+ ( yiw xi + D ) yixii( yiw xi < D) I( yi =+ ) + i= (6) 2C ( yiw xi + D ) yixii( yiw xi < D) I( yi = ) + i= 2C0 ( yiw xi D ) yixii( yiw xi > + D) i= = L( w), L(w,x i,y i ) L(w).,., (ini-batch gradient descent),. 3, ,,.,, [22]. [8,9],,JDT(Java developent tools) Eclipse Java IDE,PF(eclipse platfor) Eclipse,PDE(plug-in developent environent) Eclipse,AspectJ Java.. Table Statistics of the data sets JDT PF PDE AspectJ ,,,., Top 0 Rank,AUC(area under ROC curve) MAP(ean average precision) [8].,,BugLocator [8] Zhou,;Two- Phase [8] Ki,, ;HyLoc [9] La, auto encoder,..,,.

6 : ,,.,NP-CNN logistic (cross entropy), Logistic logistic.np-cnn hinge SVM hinge,,.., CSMDO ,.Top 0 Rank,MAP AUC 2~ 4.,/ CSMDO /(Wilcoxon, 0.05). Table 2 2 Top 0 Rank of copared ethods on all data sets Top 0 Rank Data set BugLocator Two-Phase HyLoc NP-CNN logistic NP-CNN higne CSMDO JDT PF PDE AspectJ Avg Table 3 3 MAP of copared ethods on all data sets MAP Data set BugLocator Two-Phase HyLoc NP-CNN logistic NP-CNN higne CSMDO JDT PF PDE AspectJ Avg Table 4 4 AUC of coparedethods on all data sets AUC Data set BugLocator Two-Phase HyLoc NP-CNN logistic NP-CNN higne CSMDO JDT PF PDE AspectJ Avg ,CSMDO. 4, BugLocator,Two-Phase HyLoc 3,CSMDO AUC 8.3%,9.% 2.3%,.:,CSMDO,. NP-CNN logistic,csmdo Top 0 Rank,MAP AUC 3.%,2.4% 3.9%, hinge NP-CNN hinge,.8%,2.2% 3.3%., CSMDO MAP AUC 3 Top 0 Rank.,,

7 3078 Journal of Software Vol.28, No., Noverber 207,. 4, (CSMDO),.,,.,,,.,,.,.,,. References: [] Li M, Huo X. Software defect ining based on sei-supervised learning. Journal of Data Acquisition and Processing, 206,3(): (in Chinese with English abstract). [doi: /j ] [2] Li M. Software defect ining. Chinese Association for Artificial Intelligence Conunication, 206,2(8):44 49 (in Chinese with English abstract). [3] Tu WW, Li M, Zhou ZH. Mining software defect factor. Journal of Jilin University (Engineering and Technology Edition), 202, 42(s): (in Chinese with English abstract). [4] Ding H, Chen L, Qian J, Xu L, Xu BW. Fault localization ethod using inforation quantity. Ruan Jian Xue Bao/Journal of Software, 203,24(7): (in Chinese with English abstract). [doi: /SP.J ] [5] Poshyvanyk D, Gueheneuc YG, Marcus A, Antoniol G, Rajlich VC. Feature location using probabilistic ranking of ethods based on execution scenarios and inforation retrieval. IEEE Trans. on Software Engineering, 2007,33(6): [doi: 0.09/tse ] [6] Lukins SK, Kraft N, Etzkorn LH. Source code retrieval for bug localization using latent dirichlet allocation. In: Proc. of 5th Working Conf. on Reverse Engineering. IEEE, [doi: 0.09/wcre ] [7] Gay G, Haiduc S, Marcus A, Menzies T. On the use of relevance feedback in IR-based concept location. In: Proc. of Int l Conf. on Software Maintenance [doi: 0.09/ics ] [8] Zhou J, Zhang H, Lo D. Where should the bugs be fixed? More accurate inforation retrieval-based bug localization based on bug reports. In: Proc. of the 34th Int l Conf. on Software Engineering [doi: 0.09/icse ] [9] La AN, Nguyen AT, Nguyen HA, Nguyen TN. Cobining deep learning with inforation retrieval to localize buggy files for bug reports. In: Proc. of the 28th Int l Conf. on Autoated Software Engineering [doi: 0.09/ase ] [0] Huo X, Li M, Zhou ZH. Learning unified features fro natural and prograing languages for locating buggy source code. In: Proc. of the 25th Int l Joint Conf. on Artificial Intelligence (IJCAI 206). New York, [] Mou L, Li G, Zhang L, Wang T, Jin Z. Convolutional neural networks over tree structures for prograing language processing. In: Proc. of the 30th AAAI Conf. on Artificial Intelligence [2] Wang L, Sugiyaa M, Jing Z, Yang C, Zhou ZH, Feng J. A refined argin analysis for boosting algoriths via equilibriu argin. Journal of Machine Learning Research, 20,2: [3] Gao W, Zhou ZH. On the doubt about argin explanation of boosting. Artificial Intelligence, 203,203: 8. [doi: 0.06/j.artint ] [4] Zhang T, Zhou ZH. Optial argin distribution achine. arxiv: , 206.

8 : 3079 [5] Johnson R, Zhang T. Effective use of word order for text categorization with convolutional neural networks. In: Proc. of the Conf. of the North Aerican Chapter of the Association for Coputational Linguistics: Huan Language Technologies [doi: 0.35/v/n5-0] [6] Zhang X, LeCun Y. Text understanding fro scratch. arxiv: , 205. [7] Ki Y. Convolutional neural networks for sentence classification. In: Proc. of the Conf. on Epirical Methods in Natural Language Processing. ACL, [doi: 0.35/v/d4-8] [8] Ki D, Zeller A, Tao Y, Ki S. Where should we fix this bug? A two-phase recoendation odel. IEEE Trans. on Software Engineering, IEEE, 203,39(): [doi: 0.09/tse ] [9] Schapire RE, Freund Y, Barlett P, Lee WS. Boosting the argin: A new explanation for the effectiveness of voting ethods. In: Fisher DH, ed. Proc. of the 4th Int l Conf. on Machine Learning (ICML 97). Morgan Kaufann Publishers, [20] Breian L. Prediction gaes and arcing algoriths. Neural Coputation, 999,(7): [doi: 0.62/ ] [2] Reyzin L, Schapire RE. How boosting the argin can also boost classifier coplexity. In: Cohen WW, Moore A, eds. Proc. of the 23rd Int l Conf. on Machine Learning (ICML 2006), Vol [doi: 0.45/ ] [22] Fischer M, Pinzger M, Gall H. Populating a release history database fro version control and bug tracking systes. In: Proc. of the Int l Conf. on Software Maintenance. IEEE, [doi: 0.09/ics ] : [],..,206,3(): [doi: /j ] [2]..,206,2(8): [3],,..(),202,42(s): [4],,,,..,203,24(7): /4294.ht [doi: /SP.J ] (994),,,, (980),,,,,.,CCF,,.

Σχετικά έγγραφα

ER-Tree (Extended R*-Tree)

ER-Tree (Extended R*-Tree) 1-9825/22/13(4)768-6 22 Journal of Software Vol13, No4 1, 1, 2, 1 1, 1 (, 2327) 2 (, 3127) E-mail xhzhou@ustceducn,,,,,,, 1, TP311 A,,,, Elias s Rivest,Cleary Arya Mount [1] O(2 d ) Arya Mount [1] Friedman,Bentley