缺陷报告质量研究综述
作者:
作者简介:

邹卫琴(1988-), 女, 博士, 副教授, CCF专业会员, 主要研究领域为缺陷定位, 软件仓库挖掘.;张静宣(1988-), 男, 博士, 副教授, CCF 专业会员, 主要研究领域为软件仓库挖掘, 智能软件工程.;张霄炜(1996-), 女, 博士, 主要研究领域为机器学习, 软件质量分析.;陈林(1979-), 男, 博士, 副教授, 博士生导师, CCF高级会员, 主要研究领域为软件工程, 程序分析.;玄跻峰(1984-), 男, 博士, 教授, 博士生导师, CCF高级会员, 主要研究领域为软件分析与测试.

通讯作者:

陈林,E-mail:lchen@nju.edu.cn

基金项目:

国家自然科学基金(62002161, 61902181, 61872177, 61872273); CCF-腾讯犀牛鸟基金(RAGR20200106); 中国博士后科学基金(2020M671489)


Survey of Research on Bug Report Quality
Author:
  • 摘要
  • | |
  • 访问统计
  • |
  • 参考文献 [132]
  • |
  • 相似文献 [20]
  • | | |
  • 文章评论
    摘要:

    在软件开发和维护过程中, 缺陷修复人员通常根据由终端用户或者开发/测试者提交的缺陷报告来定位和修复缺陷. 因此, 缺陷报告本身的质量对修复人员能否快速准确定位并修复缺陷具有重要的作用. 围绕缺陷报告质量的刻画及改进, 研究人员开展了大量的研究工作, 但尚未进行系统性的归纳. 旨在对这些工作进行系统性地梳理, 展示该领域的研究现状并为未来的研究方向提供参考意见. 首先, 总结了已有缺陷报告存在的质量问题, 如关键信息缺失、信息错误等; 接着, 总结了对缺陷报告质量进行自动化建模的技术; 然后, 描述了一系列对缺陷报告质量进行改进的方法; 最后, 对未来研究可能面临的挑战和机遇进行了展望.

    Abstract:

    During the software development and maintenance process, bug fixers usually refer to bug reports submitted by end-users or developers/testers to locate and fix a bug. In this sense, the quality of the bug report largely determines whether the bug fixer could quickly and precisely locate the bug and further fix it. Researchers have done much work on characterizing, modeling, and improving the quality of bug reports. This study offers a systematic survey on existing work on bug report quality, with an attempt to understand the current state of research on this area as well as to open new avenues for future research work. Firstly, quality problems of bug reports reported by existing studies are summarized into a list, such as the missing of key information and errors in information items. Then, a series of work on automatically modeling bug report quality are presented. After that, those approaches are introduced that aim to improve bug report quality. Finally, the challenges and potential opportunities for research on bug report quality are discussed.

    参考文献
    [1] Brooks Jr FP. The Mythical Man-month:Essays on Software Engineering (20th Anniversary Edition). Boston:Addison-Wesley Professional, 1995.
    [2] Zou WQ, Lo D, Chen ZY, Xia X, Feng Y, Xu BW. How practitioners perceive automated bug report management techniques. IEEE Transactions on Software Engineering, 2020, 46(8):836-862.[doi:10.1109/TSE.2018.2870414]
    [3] Joorabchi ME, Mirzaaghaei M, Mesbah A. Works for me! Characterizing non-reproducible bug reports. In:Proc. of the 11th Working Conf. on Mining Software Repositories. Hyderabad:ACM, 2014. 62-71.
    [4] Serrano N, Ciordia I. Bugzilla, ITracker, and other bug trackers. IEEE Software, 2005, 22(2):11-13.[doi:10.1109/MS.2005.32]
    [5] Xuan JF, Jiang H, Hu Y, Ren ZL, Zou WQ, Luo ZX, Wu XD. Towards effective bug triage with software data reduction techniques. IEEE Transactions on Knowledge and Data Engineering, 2015, 27(1):264-280.[doi:10.1109/TKDE.2014.2324590]
    [6] Bettenburg N, Just S, Schröter A, Weiss C, Premraj R, Zimmermann T. What makes a good bug report? In:?Proc. of the 16th Int'l Symp. on Foundations of Software Engineering. Atlanta Georgia:ACM, 2008. 308-318.
    [7] Zimmermann T, Premraj R, Sillito J, Breu S. Improving bug tracking systems. In:Proc. of the 31st Int'l Conf. on Software Engineering-Companion volume. Vancouver:IEEE, 2009. 247-250.
    [8] Wang DD, Wang Q, Yang Y, Li Q, Wang HT, Yuan F. "Is it really a defect?" An empirical study on measuring and improving the process of software defect reporting. In:Proc. of the 2011 Int'l Symp. on Empirical Software Engineering and Measurement. Banff:IEEE, 2011. 434-443.
    [9] Xia X, Lo D, Wen M, Shihab E, Zhou B. An empirical study of bug report field reassignment. In:Proc. of the 2014 Software Evolution Week-IEEE Conf. on Software Maintenance, Reengineering, and Reverse Engineering (CSMR-WCRE). Antwerp:IEEE, 2014. 174-183.
    [10] Zimmermann T, Premraj R, Bettenburg N, Just S, Schroter A, Weiss C. What makes a good bug report? IEEE Trans. on Software Engineering, 2010, 36(5):618-643.
    [11] Soltani M, Hermans F, Bäck T. The significance of bug report elements. Empirical Software Engineering, 2020, 25(6):5255-5294.[doi:10.1007/s10664-020-09882-z]
    [12] Chen X, Jiang H, Li XC, He TK, Chen ZY. Automated quality assessment for crowdsourced test reports of mobile applications. In:Proc. of the 25th IEEE Int'l Conf. on Software Analysis, Evolution and Reengineering (SANER). Campobasso:IEEE, 2018. 368-379.
    [13] Chaparro O, Bernal-Cárdenas C, Lu J, Moran K, Marcus A, Di Penta M, Poshyvanyk D, Ng V. Assessing the quality of the steps to reproduce in bug reports. In:Proc. of the 27th ACM Joint Meeting on European Software Engineering Conf. and Symp. on the Foundations of Software Engineering. Tallinn:ACM, 2019. 86-96.
    [14] Just S, Premraj R, Zimmermann T. Towards the next generation of bug tracking systems. In:Proc. of the 2008 IEEE Symp. on Visual Languages and Human-centric Computing. Herrsching:IEEE, 2008. 82-85.
    [15] Ko AJ, Myers BA, Chau DH. A linguistic analysis of how people describe software problems. In:Proc. of Visual Languages and Human-centric Computing (VL/HCC2006). Brighton:IEEE, 2006. 127-134.
    [16] Moran K, Linares-Vásquez M, Bernal-Cárdenas C, Poshyvanyk D. Auto-completing bug reports for Android applications. In:Proc. of the 10th Joint Meeting on Foundations of Software Engineering. Bergamo:ACM, 2015. 673-686.
    [17] Yang G, Min K, Lee JW, Lee B. Applying topic modeling and similarity for predicting bug severity in cross projects. KSII Transactions on Internet and Information Systems, 2019, 13(3):1583-1598.[doi:10.3837/tiis.2019.03.026]
    [18] Hao R, Feng Y, Jones JA, Li YY, Chen ZY. Ctras:Crowdsourced test report aggregation and summarization. In:Proc. of the 41st IEEE/ACM Int'l Conf. on Software Engineering (ICSE). Montreal:IEEE, 2019. 900-911.
    [19] Chen JJ, Patra J, Pradel M, Xiong YF, Zhang HY, Hao D, Zhang L. A survey of compiler testing. ACM Computing Surveys, 2021, 53(1):4.[doi:10.1145/3363562]
    [20] 涂菲菲, 周明辉. 软件开发活动数据的数据质量问题. 软件学报, 2019, 30(5):1522-1531. http://www.jos.org.cn/1000-9825/5727.htm
    Tu FF, Zhou MH. Data quality problems in software development activity data. Ruan Jian Xue Bao/Journal of Software, 2019, 30(5):1522-1531 (in Chinese with English abstract). http://www.jos.org.cn/1000-9825/5727.htm
    [21] Bettenburg N, Premraj R, Zimmermann T, Kim S. Duplicate bug reports considered harmful… really? In:Proc. of the 24th IEEE Int'l Conf. on Software Maintenance. Beijing:IEEE, 2008. 337-345.[doi:10.1109/ICSM.2008.4658082]
    [22] Breu S, Premraj R, Sillito J, Zimmermann T. Frequently asked questions in bug reports. Technical Report, Calgary:University of Calgary, 2009.
    [23] Laukkanen EI, Mantyla MV. Survey reproduction of defect reporting in industrial software development. In:Proc. of the 2011 Int'l Symp. on Empirical Software Engineering and Measurement. Banff:IEEE, 2011. 197-206.
    [24] Davies S, Roper M. What's in a bug report? In:Proc. of the 8th ACM/IEEE Int'l Symp. on Empirical Software Engineering and Measurement. Torino:ACM, 2014. 1-10.
    [25] Garousi V, Ergezer EG, Herkiloğlu K. Usage, usefulness and quality of defect reports:An industrial case study. In:Proc. of the 20th Int'l Conf. on Evaluation and Assessment in Software Engineering. Limerick:ACM, 2016. 1-6.
    [26] Yusop NSM, Grundy J, Vasa R. Reporting usability defects:Do reporters report what software developers need? In:Proc. of the 20th Int'l Conf. on Evaluation and Assessment in Software Engineering. Limerick:ACM, 2016. 1-10.
    [27] Zanetti MS, Scholtes I, Tessone CJ, Schweitzer F. Categorizing bugs with social networks:A case study on four open source software communities. In:Proc. of the 35th Int'l Conf. on Software Engineering (ICSE). San Francisco:IEEE, 2013. 1032-1041.
    [28] Fan YR, Xia X, Lo D, Hassan AE. Chaff from the wheat:Characterizing and determining valid bug reports. IEEE Transactions on Software Engineering, 2020, 46(5):495-525.[doi:10.1109/TSE.2018.2864217]
    [29] Aversano L, Tedeschi E. Bug report quality evaluation considering the effect of submitter reputation. In:Proc. of the 11th Int'l Joint Conf. on Software Technologies. Lisbon:ICSOFT-EA, 2016. 194-201.
    [30] Bhattacharya P, Ulanova L, Neamtiu I, Koduru SC. An empirical analysis of bug reports and bug fixing in open source Android APPs. In:Proc. of the 17th European Conf. on Software Maintenance and Reengineering. Genova:IEEE, 2013. 133-143.
    [31] Chen X, Jiang H, Li XC, Nie LM, Yu DJ, He TK, Chen ZY. A systemic framework for crowdsourced test report quality assessment. Empirical Software Engineering, 2020, 25(2):1382-1418.[doi:10.1007/s10664-019-09793-8]
    [32] He JJ, Xu L, Fan YR, Xu Z, Yan M, Lei Y. Deep learning based valid bug reports determination and explanation. In:Proc. of the 31st IEEE Int'l Symp. on Software Reliability Engineering (ISSRE). Coimbra:IEEE, 2020. 184-194.
    [33] Linstead E, Baldi P. Mining the coherence of GNOME bug reports with statistical topic models. In:Proc. of the 6th IEEE Int'l Working Conf. on Mining Software Repositories. Vancouver:IEEE, 2009. 99-102.
    [34] Schugerl P, Rilling J, Charland P. Mining bug repositories-A quality assessment. In:Proc. of the 2008 Int'l Conf. on Computational Intelligence for Modelling Control & Automation. Vienna:IEEE, 2008. 1105-1110.
    [35] Bettenburg N, Just S, Schröter A, Weiß C, Premraj R, Zimmermann T. Quality of bug reports in eclipse. In:Proc. of the 2007 OOPSLA Workshop on Eclipse Technology EXchange. Montreal:ACM, 2007. 21-25.
    [36] Dit B, Poshyvanyk D, Marcus A. Measuring the semantic similarity of comments in bug reports. In:Proc. of the 1st Int'l Workshop on Semantic Technologies in System Maintenance (STSM2008). STSM, 2008. 265-280
    [37] Hooimeijer P, Weimer W. Modeling bug report quality. In:Proc. of the 22nd IEEE/ACM Int'l Conf. on Automated Software Engineering. Atlanta:ACM, 2007. 34-43.
    [38] Gromova A, Itkin I, Pavlov S, Korovayev A. Raising the quality of bug reports by predicting software defect indicators. In:Proc. of the 19th IEEE Int'l Conf. on Software Quality, Reliability and Security Companion (QRS-C). Sofia:IEEE, 2019. 198-204.
    [39] Wang JJ, Cui Q, Wang Q, Wang S. Towards effectively test report classification to assist crowdsourced testing. In:Proc. of the 10th ACM/IEEE Int'l Symp. on Empirical Software Engineering and Measurement. Ciudad Real:ACM, 2016. 1-10.
    [40] Feng Y, Jones JA, Chen ZY, Fang CR. Multi-objective test report prioritization using image understanding. In:Proc. of the 31st IEEE/ACM Int'l Conf. on Automated Software Engineering. Singapore:ACM, 2016. 202-213.
    [41] Feng Y, Chen ZY, Jones JA, Fang CR, Xu BW. Test report prioritization to assist crowdsourced testing. In:Proc. of the 10th Joint Meeting on Foundations of Software Engineering. Bergamo:ACM, 2015. 225-236.
    [42] Anvik J, Hiew L, Murphy GC. Coping with an open bug repository. In:Proc. of the OOPSLA Workshop on Eclipse Technology Exchange. San Diego:ACM, 2005. 35-39.
    [43] Breu S, Premraj R, Sillito J, Zimmermann T. Information needs in bug reports:Improving cooperation between developers and users. In:Proc. of the 2010 ACM Conf. on Computer Supported Cooperative Work. Savannah:ACM, 2010. 301-310.
    [44] GitHub Open Source Project Maintainers. An open letter to GitHub from the maintainers of open source projects. 2019. https://github.com/dear-github/dear-github
    [45] Dal Sasso T, Mocci A, Lanza M. What makes a satisficing bug report? In:Proc. of the 2016 IEEE Int'l Conf. on Software Quality, Reliability and Security (QRS). Vienna:IEEE, 2016. 164-174.
    [46] Herraiz I, German DM, Gonzalez-Barahona JM, Robles G. Towards a simplification of the bug report form in Eclipse. In:Proc. of the 2008 Int'l Working Conf. on Mining Software Repositories. Leipzig:ACM, 2008. 145-148.
    [47] Twidale MB, Nichols DM. Exploring usability discussions in open source development. In:Proc. of the 38th Annual Hawaii Int'l Conf. on System Sciences. Big Island:IEEE, 2005. 198c.
    [48] Nichols DM, Twidale MB. Usability processes in open source projects. Software Process:Improvement and Practice, 2006, 11(2):149-162.[doi:10.1002/spip.256]
    [49] Castro M, Costa M, Martin JP. Better bug reporting with better privacy. ACM SIGOPS Operating Systems Review, 2008, 42(2):319-328.[doi:10.1145/1353535.1346322]
    [50] Yusop NSM, Grundy J, Vasa R. Reporting usability defects:Limitations of open source defect repositories and suggestions for improvement. In:Proc. of the 24th ASWEC Australasian Software Engineering Conf. Adelaide:ACM, 2015. 38-43.
    [51] Song Y, Chaparro O. BEE:A tool for structuring and analyzing bug reports. In:Proc. of the 28th ACM Joint Meeting on European Software Engineering Conf. and Symp. on the Foundations of Software Engineering. Online:ACM, 2020. 1551-1555.
    [52] Chaparro O, Lu J, Zampetti F, Moreno L, Di Penta M, Marcus A, Bavota G, Ng V. Detecting missing information in bug descriptions. In:Proc. of the 11th Joint Meeting on Foundations of Software Engineering. Paderborn:ACM, 2017. 396-407.
    [53] Karim R, Ihara A, Choi E, Iida H. Identifying and predicting key features to support bug reporting. Journal of Software:Evolution and Process, 2019, 31(12):e2184.[doi:10.1002/smr.2184]
    [54] Lotufo R, Passos L, Czarnecki K. Towards improving bug tracking systems with game mechanisms. In:Proc. of the 9th IEEE Working Conf. on Mining Software Repositories (MSR). Zurich:IEEE, 2012. 2-11.
    [55] Moran K, Poshyvanyk D. Fixing bug reporting for mobile and GUI-based applications. In:Proc. of the 38th Int'l Conf. on Software Engineering Companion. Austin:ACM, 2016. 831-834.
    [56] Moran K, Linares-Vásquez M, Bernal-Cárdenas C, Vendome C, Poshyvanyk D. Automatically discovering, reporting and reproducing Android application crashes. In:Proc. of the 2016 IEEE Int'l Conf. on Software Testing, Verification and Validation (ICST). Chicago:IEEE, 2016. 33-44.
    [57] Yu SC. Crowdsourced report generation via bug screenshot understanding. In:Proc. of the 34th IEEE/ACM Int'l Conf. on Automated Software Engineering (ASE). San Diego:IEEE, 2019. 1277-1279.
    [58] Herbold S, Grabowski J, Waack S, Bünting U. Improved bug reporting and reproduction through non-intrusive GUI usage monitoring and automated replaying. In:Proc. of the 4th IEEE Int'l Conf. on Software Testing, Verification and Validation Workshops. Berlin:IEEE, 2011. 232-241.
    [59] White M, Linares-Vásquez M, Johnson P, Bernal-Cárdenas C, Poshyvanyk D. Generating reproducible and replayable bug reports from Android application crashes. In:Proc. of the 23rd Int'l Conf. on Program Comprehension. Florence:IEEE, 2015. 48-59.
    [60] Zamfir C, Candea G. Low-overhead bug fingerprinting for fast debugging. In:Proc. of the 1st Int'l Conf. on Runtime Verification. Julians:Springer, 2010. 460-468.
    [61] Zhang T, Chen JC, Jiang H, Luo XP, Xia X. Bug report enrichment with application of automated fixer recommendation. In:Proc. of the 25th Int'l Conf. on Program Comprehension. Buenos:ACM, 2017. 230-240.
    [62] Weimer W. Patches as better bug reports. In:Proc. of the 5th Int'l Conf. on Generative Programming and Component Engineering. Portland:ACM, 2006. 181-190.
    [63] Anvik J, Murphy GC. Reducing the effort of bug report triage:Recommenders for development-oriented decisions. ACM Transactions on Software Engineering and Methodology, 2011, 20(3):1-35.[doi:10.1145/2000791.2000794]
    [64] Zhang W, Challis C. Software component prediction for bug reports. In:Proc. of the 11th Asian Conf. on Machine Learning. Nagoya:PMLR, 2019. 806-821.
    [65] Sureka A. Learning to classify bug reports into components. In:Proc. of the 50th Int'l Conf. on Objects, Models, Components, Patterns. Prague:Springer, 2012. 288-303.
    [66] Somasundaram K, Murphy GC. Automatic categorization of bug reports using latent Dirichlet allocation. In:Proc. of the 5th India Software Engineering Conf. Kanpur:ACM, 2012. 125-130.
    [67] Yan M, Zhang XH, Yang D, Xu L, Kymer JD. A component recommender for bug reports using discriminative probability latent semantic analysis. Information and Software Technology, 2016, 73:37-51.[doi:10.1016/j.infsof.2016.01.005]
    [68] Wang DQ, Zhang H, Liu R, Lin MX, Wu WJ. Predicting bugs' components via mining bug reports. Journal of Software, 2012, 7(5):1149-1154.
    [69] Lamkanfi A, Demeyer S. Predicting reassignments of bug reports-An exploratory investigation. In:Proc. of the 17th European Conf. on Software Maintenance and Reengineering. Genova:IEEE, 2013. 327-330.
    [70] Sabor KK, Nayrolles M, Trabelsi A, Hamou-Lhadj A. An approach for predicting bug report fields using a neural network learning model. In:Proc. of the 2018 IEEE Int'l Symp. on Software Reliability Engineering Workshops (ISSREW). Memphis:IEEE, 2018. 232-236.
    [71] Sabor KK, Hamou-Lhadj A, Trabelsi A, Hassine J. Predicting bug report fields using stack traces and categorical attributes. In:Proc. of the 29th Annual Int'l Conf. on Computer Science and Software Engineering. Markham:IBM, 2019. 224-233.
    [72] Zhang W, Challis C. Automatic bug priority prediction using DNN based regression. In:Proc. of the 15th Int'l Conf. on Natural Computation, Fuzzy Systems and Knowledge Discovery. Kunming:Springer, 2019. 333-340.
    [73] Umer Q, Liu H, Illahi I. CNN-based automatic prioritization of bug reports. IEEE Transactions on Reliability, 2020, 69(4):1341-1354.[doi:10.1109/TR.2019.2959624]
    [74] Kanwal J, Maqbool O. Bug prioritization to facilitate bug report triage. Journal of Computer Science and Technology, 2012, 27(2):397-412.[doi:10.1007/s11390-012-1230-3]
    [75] Tran HM, Le ST, Van Nguyen S, Ho PT. An analysis of software bug reports using machine learning techniques. SN Computer Science, 2020, 1:4.[doi:10.1007/s42979-019-0004-1]
    [76] Tian Y, Lo D, Xia X, Sun CN. Automated prediction of bug report priority using multi-factor analysis. Empirical Software Engineering, 2015, 20(5):1354-1383.[doi:10.1007/s10664-014-9331-y]
    [77] Tian Y, Lo D, Sun CN. Drone:Predicting priority of reported bugs by multi-factor analysis. In:Proc. of the 2013 IEEE Int'l Conf. on Software Maintenance. Eindhoven:IEEE, 2013. 200-209.
    [78] Goyal N, Aggarwal N, Dutta M. A novel way of assigning software bug priority using supervised classification on clustered bugs data. In:El-Alfy ESM, Thampi AM, Takagi H, Piramuthu S, Hanne T, eds. Advances in Intelligent Informatics. Cham:Springer, 2015. 493-501.
    [79] Yu L, Tsai WT, Zhao W, Wu F. Predicting defect priority based on neural networks. In:Proc. of the 6th Int'l Conf. on Advanced Data Mining and Applications. Chongqing:Springer, 2010. 356-367.
    [80] Sharma M, Kumari M, Singh VB. Bug priority assessment in cross-project context using entropy-based measure. In:Proc. of the 2019 ICMLCI on Advances in Machine Learning and Computational Intelligence. Bugis:Springer, 2020. 113-128.
    [81] Sharma M, Bedi P, Chaturvedi KK, Singh VB. Predicting the priority of a reported bug using machine learning techniques and cross project validation. In:Proc. of the 12th Int'l Conf. on Intelligent Systems Design and Applications (ISDA). Kochi:IEEE, 2012. 539-545.
    [82] Lamkanfi A, Demeyer S, Soetens QD, Verdonck T. Comparing mining algorithms for predicting the severity of a reported bug. In:Proc. of the 15th European Conf. on Software Maintenance and Reengineering. Oldenburg:IEEE, 2011. 249-258.
    [83] Chawla I, Singh SK. Improving bug report quality by predicting correct component in bug reports. International Journal of Computational Intelligence Studies, 2019, 8(1-2):143-157.
    [84] Kukkar A, Mohana R, Nayyar A, Kim J, Kang BG, Chilamkurti N. A novel deep-learning-based bug severity classification technique using convolutional neural networks and random forest with boosting. Sensors, 2019, 19(13):2964.[doi:10.3390/s19132964]
    [85] Menzies T, Marcus A. Automated severity assessment of software defect reports. In:Proc. of the 2008 IEEE Int'l Conf. on Software Maintenance. Beijing:IEEE, 2008. 346-355.
    [86] Sharma G, Sharma S, Gujral S. A novel way of assessing software bug severity using dictionary of critical terms. Procedia Computer Science, 2015, 70:632-639.[doi:10.1016/j.procs.2015.10.059]
    [87] Yang CZ, Hou CC, Kao WC, Chen IX. An empirical study on improving severity prediction of defect reports using feature selection. In:Proc. of the 19th Asia-Pacific Software Engineering Conf. Hong Kong:IEEE, 2012. 240-249.
    [88] Roy NKS, Rossi B. Towards an improvement of bug severity classification. In:Proc. of the 40th EUROMICRO Conf. on Software Engineering and Advanced Applications. Verona:IEEE, 2014. 269-276.
    [89] Liu WJ, Wang SS, Chen X, Jiang H. Predicting the severity of bug reports based on feature selection. International Journal of Software Engineering and Knowledge Engineering, 2018, 28(4):537-558.[doi:10.1142/S0218194018500158]
    [90] Zhang T, Chen JC, Yang G, Lee B, Luo XP. Towards more accurate severity prediction and fixer recommendation of software bugs. Journal of Systems and Software, 2016, 117:166-184.[doi:10.1016/j.jss.2016.02.034]
    [91] Yang G, Zhang T, Lee B. An emotion similarity based severity prediction of software bugs:A case study of open source projects. IEICE Transactions on Information and Systems, 2018, E101. D(8):2015-2026.[doi:10.1587/transinf.2017EDP7406]
    [92] Ramay WY, Umer Q, Yin XC, Zhu C, Illahi I. Deep neural network-based severity prediction of bug reports. IEEE Access, 2019, 7:46846-46857.[doi:10.1109/ACCESS.2019.2909746]
    [93] Tan YS, Xu SJ, Wang ZW, Zhang T, Xu Z, Luo XP. Bug severity prediction using question-and-answer pairs from stack overflow. Journal of Systems and Software, 2020, 165:110567.[doi:10.1016/j.jss.2020.110567]
    [94] Han BZ, Li XH, Xing ZC, Liu HT, Feng ZY. Learning to predict severity of software vulnerability using only vulnerability description. In:Proc. of the 2017 IEEE Int'l Conf. on Software Maintenance and Evolution (ICSME). Shanghai:IEEE, 2017. 125-136.
    [95] Iqbal S, Naseem R, Jan S, Alshmrany S, Yasar M, Ali A. Determining bug prioritization using feature reduction and clustering with classification. IEEE Access, 2020, 8:215661-215678.[doi:10.1109/ACCESS.2020.3035063]
    [96] Zhang TL, Chen R, Yang X, Zhu HY. An uncertainty based incremental learning for identifying the severity of bug report. International Journal of Machine Learning and Cybernetics, 2020, 11(1):123-136.[doi:10.1007/s13042-019-00961-2]
    [97] Roy NKS, Rossi B. Cost-sensitive strategies for data imbalance in bug severity classification:Experimental results. In:Proc. of the 43rd Euromicro Conf. on Software Engineering and Advanced Applications (SEAA). Vienna:IEEE, 2017. 426-429.
    [98] Guo SK, Chen R, Li H, Zhang TL, Liu YQ. Identify severity bug report with distribution imbalance by CR-SMOTE and ELM. International Journal of Software Engineering and Knowledge Engineering, 2019, 29(2):139-175.[doi:10.1142/S0218194019500074]
    [99] Hamdy A, El-Laithy A. SMOTE and feature selection for more effective bug severity prediction. International Journal of Software Engineering and Knowledge Engineering, 2019, 29(6):897-919.[doi:10.1142/S0218194019500311]
    [100] Tian Y, Ali N, Lo D, Hassan AE. On the unreliability of bug severity data. Empirical Software Engineering, 2016, 21(6):2298-2323.[doi:10.1007/s10664-015-9409-1]
    [101] Wu LL, Xie BY, Kaiser GE, Passonneau R. BugMiner:Software reliability analysis via data mining of bug reports. In:Proc. of the 23rd Int'l Conf. on Software Engineering & Knowledge Engineering (SEKE2011). Miami Beach:SEKE, 2011. 95-100.
    [102] Xia X, Lo D, Shihab E, Wang XY. Automated bug report field reassignment and refinement prediction. IEEE Transactions on Reliability, 2016, 65(3):1094-1113.[doi:10.1109/TR.2015.2484074]
    [103] Kumari M, Singh VB. An improved classifier based on entropy and deep learning for bug priority prediction. In:Proc. of the 18th Int'l Conf. on Intelligent Systems Design and Applications. Vellore:Springer, 2018. 571-580.
    [104] Alenezi M, Banitaan S. Bug reports prioritization:Which features and classifier to use? In:Proc. of the 12th Int'l Conf. on Machine Learning and Applications. Miami:IEEE, 2013. 112-116.
    [105] Umer Q, Liu H, Sultan Y. Emotion based automated priority prediction for bug reports. IEEE Access, 2018, 6:35743-35752.[doi:10.1109/ACCESS.2018.2850910]
    [106] Gao GF, Li H, Chen R, Ge X, Guo SK. Identification of high priority bug reports via integration method. In:Proc. of the 6th CCF Conf. on Big Data. Xi'an:Springer, 2018. 336-349.
    [107] Kanwal J, Maqbool O. Managing open bug repositories through bug report prioritization using SVMs. In:Proc. of the 4th Int'l Conf. on Open-source Systems and Technologies. Lahore:ICOSST, 2010. 1-7.
    [108] Yang G, Baek S, Lee JW, Lee B. Analyzing emotion words to predict severity of software bugs:A case study of open source projects. In:Proc. of the Symp. on Applied Computing. Marrakech:ACM, 2017. 1280-1287.
    [109] Iliev M, Karasneh B, Chaudron MRV, Essenius E. Automated prediction of defect severity based on codifying design knowledge using ontologies. In:Proc. of the 1st Int'l Workshop on Realizing AI Synergies in Software Engineering (RAISE). Zurich:IEEE, 2012. 7-11.
    [110] Sabor KK, Hamdaqa M, Hamou-Lhadj A. Automatic prediction of the severity of bugs using stack traces. In:Proc. of the 26th Annual Int'l Conf. on Computer Science and Software Engineering. Toronto:IBM, 2016. 96-105.
    [111] Arokiam J, Bradbury JS. Automatically predicting bug severity early in the development process. In:Prof. of the 42nd ACM/IEEE Int'l Conf. on Software Engineering:New Ideas and Emerging Results. Seoul:ACM, 2020. 17-20.
    [112] Sharmin S, Aktar F, Ali AA, Khan MAH, Shoyaib M. BFSp:A feature selection method for bug severity classification. In:Proc. of the IEEE Region 10 Humanitarian Technology Conf. (R10-HTC). Dhaka:IEEE, 2017. 750-754.
    [113] Singh VB, Misra S, Sharma M. Bug severity assessment in cross project context and identifying training candidates. Journal of Information & Knowledge Management, 2017, 16(1):1750005.[doi:10.1142/S0219649217500058]
    [114] Alia SS, Haque N, Sharmin S, Khaled SM, Shoyaib M. Bug severity classification based on class-membership information. In:Proc. of the 7th Joint Int'l Conf. on Informatics, Electronics & Vision (ICIEV) and the 2nd Int'l Conf. on Imaging, Vision & Pattern Recognition (icIVPR). Kitakyushu:IEEE, 2018. 520-525.
    [115] Chauhan A, Kumar R. Bug severity classification using semantic feature with convolution neural network. In:Proc. of the 2019 ICCET on Computing in Engineering and Technology. Bugis:Springer, 2020. 327-335.
    [116] Nnamoko N, Cabrera-Diego LA, Campbell D, Korkontzelos Y. Bug severity prediction using a hierarchical one-vs.-remainder approach. In:Proc. of the 24th Int'l Conf. on Applications of Natural Language to Information Systems. Salford:Springer, 2019. 247-260.
    [117] Chaturvedi KK, Singh VB. Determining bug severity using machine learning techniques. In:Proc. of the 6th CSI Int'l Conf. on Software Engineering (CONSEG). Indore:IEEE, 2012. 1-6.
    [118] Kukkar A, Mohana R, Kumar Y. Does bug report summarization help in enhancing the accuracy of bug severity classification? Procedia Computer Science, 2020, 167:1345-1353.
    [119] Kumari M, Singh UK, Sharma M. Entropy based machine learning models for software bug severity assessment in cross project context. In:Proc. of the 20th Int'l Conf. on Computational Science and Its Applications. Cagliari:Springer, 2020. 939-953.
    [120] Choeikiwong T, Vateekul P. Improve accuracy of defect severity categorization using semi-supervised approach on imbalanced data sets. In:Proc. of the Int'l MultiConf. of Engineers and Computer Scientists. Hong Kong:IMECS, 2016. 1-5.
    [121] Baarah A, Aloqaily A, Salah Z, Zamzeer M, Sallam M. Machine learning approaches for predicting the severity level of software bug reports in closed source projects. International Journal of Advanced Computer Science and Applications, 2019, 10(8):285-294.[doi:10.14569/IJACSA.2019.0100836]
    [122] Awad MA, ElNainay MY, Abougabal MS. Predicting bug severity using customized weighted majority voting algorithms. In:Proc. of the 2017 Japan-Africa Conf. on Electronics, Communications and Computers (JAC-ECC). Alexandria:IEEE, 2017. 170-175.
    [123] Zhang T, Yang G, Lee B, Chan ATS. Predicting severity of bug report by mining bug repository with concept profile. In:Proc. of the 30th Annual ACM Symp. on Applied Computing. Salamanca:ACM, 2015. 1553-1558.
    [124] Pushpalatha MN, Mrunalini M. Predicting the severity of bug reports using classification algorithms. In:Proc. of the 2016 Int'l Conf. on Circuits, Controls, Communications and Computing (I4C). Bangalore:IEEE, 2016. 1-4.
    [125] Pushpalatha MN, Mrunalini M. Predicting the severity of closed source bug reports using ensemble methods. In:Proc. of the 2nd Int'l Conf. on SICA. Bugis:Springer, 2019. 589-597.
    [126] Jindal R, Malhotra R, Jain A. Prediction of defect severity by mining software project reports. International Journal of System Assurance Engineering and Management, 2017, 8(2):334-351.[doi:10.1007/s13198-016-0438-y]
    [127] Hamdy A, El-Laithy A. Semantic categorization of software bug repositories for severity assignment automation. In:Jarzabek S, Poniszewska-Marańda A, Madeyski L, eds. Integrating Research and Practice in Software Engineering. Cham:Springer, 2020. 15-30.
    [128] Tian Y, Lo D, Sun CN. Information retrieval based nearest neighbor classification for fine-grained bug severity prediction. In:Proc. of the 19th Working Conf. on Reverse Engineering. Kingston:IEEE, 2012. 215-224.
    [129] Yang G, Zhang T, Lee B. Towards semi-automatic bug triage and severity prediction based on topic model and multi-feature of bug reports. In:Proc. of the 38th IEEE Annual Computer Software and Applications Conf. Vasteras:IEEE, 2014. 97-106.
    [130] Tatham S. How to report bugs effectively. 2008. https://www.chiark.greenend.org.uk/~sgtatham/bugs.html
    [131] Dit B, Marcus A. Improving the readability of defect reports. In:Proc. of the 2008 Int'l Workshop on Recommendation Systems for Software Engineering. Atlanta:ACM, 2008. 47-49.
    引证文献
    网友评论
    网友评论
    分享到微博
    发 布
引用本文

邹卫琴,张静宣,张霄炜,陈林,玄跻峰.缺陷报告质量研究综述.软件学报,2023,34(1):171-196

复制
分享
文章指标
  • 点击次数:1872
  • 下载次数: 6540
  • HTML阅读次数: 4382
  • 引用次数: 0
历史
  • 收稿日期:2021-05-08
  • 最后修改日期:2021-06-24
  • 在线发布日期: 2021-11-24
  • 出版日期: 2023-01-06
文章二维码
您是第19892371位访问者
版权所有:中国科学院软件研究所 京ICP备05046678号-3
地址:北京市海淀区中关村南四街4号,邮政编码:100190
电话:010-62562563 传真:010-62562533 Email:jos@iscas.ac.cn
技术支持:北京勤云科技发展有限公司

京公网安备 11040202500063号