Computer Science ›› 2017, Vol. 44 ›› Issue (7): 147-150.doi: 10.11896/j.issn.1002-137X.2017.07.027

Previous Articles     Next Articles

Web Resource Recommendation Based on Analysis of Developer’s Behavior

YANG Jun-wen, WANG Hai, PENG Xin and ZHAO Wen-yun   

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

Abstract: Modern integrated development environment (IDE) provides developers with a variety of tools,including error warning,code complementary,code analysis,version control management,etc.,to support software development and improve the developers’ efficiency.However,such tools are deficient,as much more information,such as code sample,configure manifest,and error handling,is needed during development,and frequently switching between Web browser and IDE costs time and effort.A Web information resource recommendation method was proposed,which is based on the analysis of developer’s behavior.The method extracts structured information including code samples from the developers’ browsing history,and classifies them through text clustering.At the same time,the developer’s behavior in the IDE was recorded.The relationship between WEB resources and developer’s behavior will be established so that similar information can be recommended when the same situation happens.At last,an experiments was conducted to demonstrate that our method can save developing time efficiently.

Key words: Web resource,Recommendation,IDE,Behavior monitoring,Web information extraction

[1] AMOR J J,ROBLES G,GONZALEZ-B ARAHONA J M.Effort estimation by characterizing developer activity[C]∥InternationalWorkshop on Economics Driven Software Engineering Research.ACM,2006:3-6.
[2] LAYMAN L,WILLIAMS L,AMANT R S.Toward reducing fault fix time:Understanding developer behavior for the design of automated fault detection tools[C]∥Empirical Software Engineering and Measurement.IEEE,2007:176-185.
[3] NUYUN Z,GANG H,YING Z,et al.Automating Reusable- Procedure Discovery through Developer’s Action Analysis[C]∥2010 10th International Conference on Quality Software (QSIC).IEEE,2010:240-247.
[4] JIN X,ZHOU Y,MOBASHER B.A maximum entropy Web re-commendation system:combining collaborative and content features[C]∥ACM SIGKDD International Conference on Know-ledge Discovery in Data Mining.2005:612-617.
[5] WEI C,SEN W,YUAN Z,et al.Algorithm of mining sequential patterns for web personalization services[J].ACM SIGMIS Database,2009,40(2):57-66.
[6] BRODER A Z,GLASSMAN S C,M ANASSE M S,et al.Syntactic clustering of the Web[J].Computer Networks and ISDN Systems,1997,29(8):1157-1166.
[7] HOBBS J R,APPELT D,BEAR J,et al.13 FASTUS:A Cascaded Finite-State Transducer for Extracting Information from Natural-Language Text[J].Finite-state Language Processing,arXiv:cmp-lg/9705013V1,7:383.
[8] CHANG C H,LUI S C.IEPAD:information extraction based on pattern discovery[C]∥International Conference on World Wide Web.ACM,2001:681-688.
[9] CHANG M L,LIN Y C,GUO L F.Design and implementation of an efficient Web cluster with content-based request distribution and file caching[J].Journal of Systems and Software,2008,81(11):2044-2058.
[10] SHAHABI C,BANAEI-K ASHANI F,CHEN Y S,et al.Yoda:An accurate and scalable Web-based recommendation system[M]∥Cooperative Information Systems.Springer Berlin Heidelberg,2001:418-432.
[11] N′SKI S.An algorithm for clustering of Web search results[D].Poznań University of Technology,Poland,2003.
[12] WANG F H,SHAO H M.Effective personalized recommendation based on time-framed navigation clustering and association mining[J].Expert Systems with Applications,2004,27(3):365-377.

No related articles found!
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
[1] LEI Li-hui and WANG Jing. Parallelization of LTL Model Checking Based on Possibility Measure[J]. Computer Science, 2018, 45(4): 71 -75, 88 .
[2] XIA Qing-xun and ZHUANG Yi. Remote Attestation Mechanism Based on Locality Principle[J]. Computer Science, 2018, 45(4): 148 -151, 162 .
[3] LI Bai-shen, LI Ling-zhi, SUN Yong and ZHU Yan-qin. Intranet Defense Algorithm Based on Pseudo Boosting Decision Tree[J]. Computer Science, 2018, 45(4): 157 -162 .
[4] WANG Huan, ZHANG Yun-feng and ZHANG Yan. Rapid Decision Method for Repairing Sequence Based on CFDs[J]. Computer Science, 2018, 45(3): 311 -316 .
[5] 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 .
[6] ZHANG Jia-nan and XIAO Ming-yu. Approximation Algorithm for Weighted Mixed Domination Problem[J]. Computer Science, 2018, 45(4): 83 -88 .
[7] 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 .
[8] LIU Qin. Study on Data Quality Based on Constraint in Computer Forensics[J]. Computer Science, 2018, 45(4): 169 -172 .
[9] ZHONG Fei and YANG Bin. License Plate Detection Based on Principal Component Analysis Network[J]. Computer Science, 2018, 45(3): 268 -273 .
[10] 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, 116 .