Computer Science ›› 2017, Vol. 44 ›› Issue (4): 56-59.doi: 10.11896/j.issn.1002-137X.2017.04.012

Previous Articles     Next Articles

Feature Location Method Based on Sub-graph Searching

FU Kun, WU Yi-jian, PENG Xin and ZHAO Wen-yun   

  • Online:2018-11-13 Published:2018-11-13

Abstract: The process to identify relevant program elements according to a given feature is called feature location.However,existing feature location methods,mainly based on feature description and source code structure,only produce source code elements as the result,which is usually lack of structural information and makes it difficult for developers to understand the code structure quickly.To solve this problem,a feature location method based on sub-graph search was proposed.The method finds out code elements related to the feature and the results can be displayed in a call graph.The method is implemented as a tool and tested for its performance.The average precision is 40.41% and the average recall is 50.28%.

Key words: Feature location,Program understanding,Code structure,Call dependence

[1] BIGGERSTAFF T J,MITBANDER B G,W EBSTER D E.Program understanding and the concept assignment problem[C]∥Communications of the ACM.1994:482-498.
[2] DIT B,REVELLE M,GETHERS M,et al.Feature location in source code:a taxonomy and survey[J].Journal of Software Maintenance & Evolution Research & Practice,2012,25(1):53-95.
[3] MARCUS A,MALETIC J I.Recovering Documentation to- Source-Code Traceability Links using Latent Semantic Indexing[C]∥ICSE.2003:125-135.
[4] TRIFU M.Using Dataflow Information for Concern Identification in Object-oriented Software Systems[C]∥European Conference on Software Maintenance & Reengineering.2008:193-202.
[5] WONG W E,HORGAN J R,GOKHALE S S,et al.LocatingProgram Features using Execution Slices[C]∥Proceedings 1999 IEEE Symposium on Application-Specific Systems and Software Engineering and Technology,1999(ASSET’99).IEEE,1999:194.
[6] KONAR K,VOSEK V,KULICH M,et al.CoMoGen:An approach to locate relevant task context by combining search and navigation[C]∥IEEE International Conference on Software Maintenance & Evolution.IEEE,2014:61-70.
[7] ZHAO W.Improving feature location practice with multi-faceted interactive exploration[C]∥2013 35th International Conference on Software Engineering (ICSE).IEEE,2013:762-771.
[8] BYRKA ,JAROSAW,GRANDONI F,et al.An Improved LP-based Approximation for Steiner Tree[C]∥Proceedings of the Forty-second ACM Symposium on Theory of Computing.ACM,2010:583-592.
[9] ZHAO W,ZHANG L,LIU Y,et al.SNIAFL:Towards a Static Non-Interactive Approach to Feature Location[C]∥International Conference on Software Enginee-ring.IEEE Computer Society,2004:293-303.
[10] EISENBARTH T,KOSCHKE R,S IMON D.Locating Features in Source Code[J].IEEE Transactions on Software Enginee-ring,2003,29(3):210-224.
[11] FU K,QIAN W Y,PENG X,et al.Feature Location Method Based on Call Chain Analysis[J].Computer Science,2014,1(11):36-39.(in Chinese) 付焜,钱文亿,彭鑫,等.一种基于调用链分析的特征定位方法[J].计算机科学,2014,41(11):36-39.
[12] BYRKA,JAROSAW,GRANDONI F,et al.An Improved LP-based Approximation for Steiner Tree[C]∥Proceedings of the Forty-second ACM Symposium on Theory of Computing.ACM,2010:583-592.

No related articles found!
Full text



[1] LEI Li-hui and WANG Jing. Parallelization of LTL Model Checking Based on Possibility Measure[J]. Computer Science, 2018, 45(4): 71 -75 .
[2] SUN Qi, JIN Yan, HE Kun and XU Ling-xuan. Hybrid Evolutionary Algorithm for Solving Mixed Capacitated General Routing Problem[J]. Computer Science, 2018, 45(4): 76 -82 .
[3] ZHANG Jia-nan and XIAO Ming-yu. Approximation Algorithm for Weighted Mixed Domination Problem[J]. Computer Science, 2018, 45(4): 83 -88 .
[4] WU Jian-hui, HUANG Zhong-xiang, LI Wu, WU Jian-hui, PENG Xin and ZHANG Sheng. Robustness Optimization of Sequence Decision in Urban Road Construction[J]. Computer Science, 2018, 45(4): 89 -93 .
[5] SHI Wen-jun, WU Ji-gang and LUO Yu-chun. Fast and Efficient Scheduling Algorithms for Mobile Cloud Offloading[J]. Computer Science, 2018, 45(4): 94 -99 .
[6] ZHOU Yan-ping and YE Qiao-lin. L1-norm Distance Based Least Squares Twin Support Vector Machine[J]. Computer Science, 2018, 45(4): 100 -105 .
[7] LIU Bo-yi, TANG Xiang-yan and CHENG Jie-ren. Recognition Method for Corn Borer Based on Templates Matching in Muliple Growth Periods[J]. Computer Science, 2018, 45(4): 106 -111 .
[8] GENG Hai-jun, SHI Xin-gang, WANG Zhi-liang, YIN Xia and YIN Shao-ping. Energy-efficient Intra-domain Routing Algorithm Based on Directed Acyclic Graph[J]. Computer Science, 2018, 45(4): 112 -116 .
[9] CUI Qiong, LI Jian-hua, WANG Hong and NAN Ming-li. Resilience Analysis Model of Networked Command Information System Based on Node Repairability[J]. Computer Science, 2018, 45(4): 117 -121 .
[10] WANG Zhen-chao, HOU Huan-huan and LIAN Rui. Path Optimization Scheme for Restraining Degree of Disorder in CMT[J]. Computer Science, 2018, 45(4): 122 -125 .