计算机科学 ›› 2020, Vol. 47 ›› Issue (3): 41-47.doi: 10.11896/jsjkx.191100132

所属专题: 智能软件工程

• 智能软件工程 • 上一篇    下一篇

基于用户评论的代码质量识别与分析

徐海燕,姜瑛   

  1. (云南省计算机技术应用重点实验室 昆明650500)
    (昆明理工大学信息工程与自动化学院 昆明650500)
  • 收稿日期:2019-11-15 出版日期:2020-03-15 发布日期:2020-03-30
  • 通讯作者: 姜瑛(jy_910@163.com)
  • 基金资助:
    国家重点研发计划项目(2018YFB1003904);国家自然科学基金(61462049,61063006,60703116);云南省应用基础研究计划重点项目(2017FA033)

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).

摘要: 随着IT社区和代码托管平台的发展,针对代码的用户评论数量急剧增加。用户在使用代码后给出的评论中包含丰富的静态和动态代码质量信息,对其进行提取与分析将有助于开发者了解用户关注的代码质量信息,以有针对性地提升代码质量,还有助于用户选择满足要求的代码。为此,文中提出了包含静态特性和动态特性的代码质量模型,以及识别并分析用户评论中代码质量信息的方法。首先,根据评价对象和评价句型规则识别出具有代码质量的用户评论;然后,应用评价对象和评价观点抽取代码质量属性表现;最后,通过分析代码质量属性表现和情感倾向给出代码静态和动态质量的相关结果。实验结果表明,所提方法能够有效地分析用户评论中的代码质量信息。

关键词: 代码质量, 评价对象, 评价观点, 评价句型, 用户评论, 质量属性表现

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

中图分类号: 

  • 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.
[1] 王晓涵, 谭陈琛, 相艳, 余正涛.
基于双嵌入卷积神经网络的涉案微博评价对象抽取
Aspect Extraction of Case Microblog Based on Double Embedded Convolutional Neural Network
计算机科学, 2021, 48(12): 319-323. https://doi.org/10.11896/jsjkx.201100105
[2] 王莹, 郑丽伟, 张禹尧, 张晓妘.
面向中文APP用户评论数据的软件需求挖掘方法
Software Requirement Mining Method for Chinese APP User Review Data
计算机科学, 2020, 47(12): 56-64. https://doi.org/10.11896/jsjkx.201200031
[3] 张会兵, 钟昊, 胡晓丽.
基于主题分析的用户评论聚类方法
User Reviews Clustering Method Based on Topic Analysis
计算机科学, 2019, 46(8): 50-55. https://doi.org/10.11896/j.issn.1002-137X.2019.08.008
[4] 冉猛,姜瑛.
APP软件的用户评论模式分析方法
Analytical Method for APP Software’s User Comment Patterns
计算机科学, 2017, 44(11): 181-186. https://doi.org/10.11896/j.issn.1002-137X.2017.11.027
[5] 张慧,李寿山,李培峰,朱巧明.
基于评价对象类别的跨领域情感分类方法研究
Cross-domain Sentiment Classification with Opinion Target Categorizati nn
计算机科学, 2013, 40(1): 229-232.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!