Computer Science ›› 2020, Vol. 47 ›› Issue (3): 41-47.doi: 10.11896/jsjkx.191100132

Special Issue: Intelligent Software Engineering

• Intelligent Software Engineering • Previous Articles     Next Articles

Code Quality Recognition and Analysis Based on User’s Comments

XU Hai-yan,JIANG Ying   

  1. (Computer Technology Application Key Lab of Yunnan Province, Kunming 650500, China)
    (Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China)
  • Received:2019-11-15 Online:2020-03-15 Published:2020-03-30
  • About author:XU Hai-yan,born in 1996,postgraduate,is member of China Computer Federation.Her main research interests include software engineering,software quality assurance and testing. JIANG Ying,born in 1974,Ph.D,professor,Ph.D supervisor,is senior member of China Computer Federation.Her main research interests include software quality assurance and testing,cloud computing, big data analysis and intelligent software engineering.
  • Supported by:
    This work was supported by the National Key Research and Development Program of China (2018YFB1003904), National Natural Science Foundation of China (61462049, 61063006, 60703116) and Key Project of Yunnan Applied Basic Research (2017FA033).

Abstract: With the development of IT community and code hosting platforms,the number of user’s comment about the code increasings dramatically.The comments given by users after using the code contain plenty of static and dynamic code quality information.The extraction and analysis of code quality information will help developers to understand the code quality information concerned by users and improve the quality of code.It is also helpful to users choose the code to meet the requirements.To this end,this paper proposed a code quality model including static and dynamic characteristics and a method to identify and analyze the code quality information in user’s comments.Firstly,the users’ comments with code quality are identified according to the evalua-tion objects and the evaluation sentence pattern rules.Secondly,the representations of the code quality attribute are extracted by using the evaluation objects and opinions.Finally,the related results of static and dynamic code quality are gained after analyzing the quality attributes representations and emotional tendency of code in user’s comments.The experimental results show that the proposed method can effectively analyze the code quality information in user’s comments.

Key words: Code quality, Evaluation object, Evaluation opinion, Evaluation pattern, Quality Attribute Representation, User’s comments

CLC Number: 

  • TP311
[1]PAGANO D,BRÜGGE B.User involvement in software evolution practice:a case study[C]∥International Conference on Software Engineering.San Francisco:IEEE Press,2013:953-962.
[2]LU Z,YANG D,LI J.A software evaluation system based on reviews mining[J].Computer Applications and Software,2014,31(7):1-4.
[3]RADHIKA D,VENKATASUBRAMANYAM R D,SOWMYA G R.Why is dynamic analysis not used as extensively as static analysis:an industrial study[C]∥Proceedings of the 1st International Workshop on Software Engineering Research and Industrial Practices.2014:24-33.
[4]HUANG P J,YANG M Q.Research and Application of Static Metrics for Code Quality[J].Computer Engineering and Applications,2011,47(23):61-63.
[5]LIU D R,LIU D S,ZHANG L P,et al.Prediction of Code Clone Quality Based on Bayesian Network[J].Computer Science,2017,44(4):165-168.
[6]XU N C,YUAN J,GAO X L.Code-based IT Community Answer Quality Evaluation Model[J].Journal of Chinese Computer Systems.2019,40(1):158-163.
[7]POCHE E,JHA N,WILLIAMS G,et al.Analyzing User Comments on YouTube Coding Tutorial Videos[C]∥ IEEE/ACM International Conference on Program Comprehension.IEEE,2017.
[8]ZHANY Y H,ZHU X F,XU C Y,et al.Hybrid Recommendation Approach Based on Deep Sentiment Analysis of User Reviews and Multi-View Collaborative Fusion[J].Chinese Journal of Computers,2019,42(6):1317-1333.
[9]FENG Y,CHEN Y G,QIONG B H.Social and Comment Text CNN Model Based Automobile Recommendation[J].Acta Automatica Sinica,2019,45(3):76-87.
[10]ZHANG Y,WANG C,GUO W Y,et al.Multi-Source Emotion Tagging for Online News Comments Using Bi-DirectionalHie- rarchical Semantic Representation Model[J].Journal ofCompu-ter Research and Development,2018,55(5):43-54.
[11]CHEN Q,ZHANG L,JIANG J,et al.Review Analysis Method Based on Support Vector Machine and Latent Dirichlet Allocation[J].Journal of Software,2019,30(5):349-362.
[12]TOLL D,ASSEMA J V,DURAN R,et al.I know it when I see it:Perceptions of Code Quality[C]∥ Acm Conference on Innovation & Technology in Computer Science Education.ACM,2017.
[13]HU T Y,JIANG Y.Mining of User’s Comments Reflecting Usage Feedback for APP Software[J].Journal of Software,2019,30(10):3168-3185.
[14]ZHENG R J.Computer Software Testing Technology[M].Beijing:Tsinghua University Press,1992:31-35.
[15]ATOUM I,BONG C H,KULATHURAMAIYER N.Towards resolving software quality-in-use measurement challenges [J].Computer Science,2015,5(11):877-885.
[16]LEOPAIROTE W,SURARERKS A,PROMPOON N.Software quality in use characteristic mining from customer reviews[C]∥Digital Information and Communication Technology and Applications (DICTAP).2012:434-439.
[17]ZHANG H B,ZHONG H,HU X L.User Reviews Clustering Method Based on Topic Analysis[J].Computer Science,2019,46(8):50-55.
[18]LEVENSHTEIN V.Binary codes capable of correcting dele- tions,insertions,and reversals[J].Soviet Physics Doklady,1966,10(8):707-710.
[19]DENG H C,WANG M F.Theoretical Study of the Relationship between Recall and Precision Ratio[J].Journalof The China Societyfor Scientificand Technical Information,2000,19(4):359-362.
[20]KIM Y.Convolutional neural networks for sentence classification[C]∥Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP).Doha,Qatar,2014:1746-1751.
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