面向版本演化的APP软件缺陷跟踪分析方法
作者:
作者简介:

刘海毅(1997—),男,博士生,CCF学生会员,主要研究领域为智能软件工程,软件质量保障与测试;姜瑛(1974—),女,博士,教授,博士生导师,CCF杰出会员,主要研究领域为软件质量保证与测试,云计算,大数据分析,智能软件工程;赵泽江(1998—),女,硕士生,CCF学生会员,主要研究领域为智能软件工程,软件质量保障与测试.

通讯作者:

姜瑛,E-mail:jy_910@163.com

基金项目:

国家自然科学基金(62162038,61462049,61063006,60703116);国家重点研发计划(2018YFB1003904);云南省计算机技术应用重点实验室开放基金(2020101)


APP Software Defect Tracking and Analysis Method Oriented to Version Evolution
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    摘要:

    移动应用(APP)软件的版本更新速度正在加快, 对软件缺陷的有效分析, 可以帮助开发人员理解和及时修复软件缺陷. 然而, 现有研究的分析对象大多较为单一, 存在信息孤立、零散、质量差等问题, 并且没有充分考虑数据验证及版本失配问题, 分析结果存在较大误差, 导致无效的软件演化. 为了提供更有效的缺陷分析结果, 提出一种面向版本演化的APP软件缺陷跟踪分析方法(ASD-TAOVE). 首先, 从多源、异构的APP软件数据中抽取APP软件缺陷内容并挖掘缺陷事件的因果关系; 接着, 设计了一种APP软件缺陷内容验证方法, 基于信息熵结合文本特征和结构特征定量分析缺陷怀疑度, 用于缺陷内容验证并构建APP软件缺陷内容异构图; 然后, 为了考虑版本演化带来的影响, 设计了一个APP软件缺陷跟踪分析方法, 用于在版本演化中分析缺陷的演化关系, 并将其转化为缺陷/演化元路径; 最后, 通过一个基于深度学习的异构信息网络完成APP软件缺陷分析. 针对4个研究问题(RQ)的实验结果, 证实了ASD-TAOVE方法在面向版本演化过程中对缺陷内容验证与跟踪分析的有效性, 缺陷识别准确率分别提升约9.9%和5% (平均7.5%). 与同类基线方法相比, ASD-TAOVE方法可分析丰富的APP软件数据, 提供有效的缺陷信息.

    Abstract:

    The speed of evolution in mobile application (APP) software market is accelerating. Effective analysis of software defects can help developers understand and repair software defects in time. However, the analysis object of existing research is not enough, which leads to isolated, fragmented information, and poor information quality. In addition, because of insufficient consideration of data verification and version mismatch issues, there are some errors in the analysis results, resulting in invalid software evolution. In order to provide more effective defect analysis results, an APP software defect tracking and analysis method oriented to version evolution (ASD-TAOVE) is proposed. First, the content of APP software defects is extracted from multi-source, heterogeneous APP software data, and the causal relationship of defect events is discovered. Then, a verification method for APP software defect content is designed, which is based on information entropy combined with text features and structural features to calculate the defect suspicious formula for verification and construction of APP software defect content heterogeneity graph. In order to consider the impact of version evolution, an APP software defect tracking analysis method is designed to analyze the evolution relationship of defects in version evolution. The evolution relationship can be transformed into the defect/evolutionary meta-paths which are useful for defect analysis. Finally, this study designs a heterogeneous information network based on deep learning to complete APP software defect analysis. The experimental results of four research questions (RQ) confirmed the effectiveness of ASD-TAOVE method of defect content verification and tracking analysis in the process of version-oriented evolution, and the accuracy of defect identification increased by about 9.9% and 5% respectively (average 7.5%). Compared with baseline methods, the ASD-TAOVE method can analyze more APP software data and provide effective defect information.

    参考文献
    [1] 钱宇, 曹恩叶, 邓文君, 袁华. 海量用户评论在APP更新设计中的参与作用挖掘. 系统工程理论与实践, 2021, 41(3): 554-564. [doi: 10.12011/SETP2019-1136]
    Qian Y, Cao EY, Deng WJ, Yuan H. Mining the participatory role of massive user reviews in the update design of APP software. Systems Engineering-theory & Practice, 2021, 41(3): 554-564(in Chinese with English abstract). [doi: 10.12011/SETP2019-1136]
    [2] 肖建茂, 陈世展, 冯志勇, 刘朋立, 薛霄. 一种基于用户评论自动分析的APP维护和演化方法. 计算机学报, 2020, 43(11): 2184-2202. [doi: 10.11897/SP.J.1016.2020.02184]
    Xiao JM, Chen SZ, Feng ZY, Liu PL, Xue X. An automatic analysis of user reviews method for APP evolution and maintenance. Chinese Journal of Computers, 2020, 43(11): 2184-2202(in Chinese with English abstract). [doi: 10.11897/SP.J.1016.2020.02184]
    [3] 王亚文, 王俊杰, 石琳, 王青. 一种语义感知的细粒度App评论缺陷挖掘方法. 软件学报, 2023, 34(4): 1613-1629. http://www.jos.org.cn/1000-9825/6697.htm [doi: 10.13328/j.cnki.jos.006697]
    Wang YW, Wang JJ, Shi L, Wang Q. Semantic-aware and fine-grained App review bug mining approach. Ruan Jian Xue Bao/ Journal of Software, 2023, 34(4): 1613-1629(in Chinese with English abstract). http://www.jos.org.cn/1000-9825/6697.htm [doi: 10.13328/j.cnki.jos.006697]
    [4] 段文静, 姜瑛. 基于用户反馈的APP软件缺陷识别. 计算机科学, 2020, 47(6): 44-50. [doi: 10.11896/jsjkx.191100133]
    Duan WJ, Jiang Y. Defect recognition of APP software based on user feedback. Computer Science, 2020, 47(6): 44-50(in Chinese with English abstract). [doi: 10.11896/jsjkx.191100133]
    [5] 周慧聪, 郭肇强, 梅元清, 李言辉, 陈林, 周毓明. 版本失配和数据泄露对基于缺陷报告的缺陷定位模型的影响. 软件学报, 2023, 34(5): 2196-2217. http://www.jos.org.cn/1000-9825/6401.htm [doi: 10.13328/j.cnki.jos.006401]
    Zhou HC, Guo ZQ, Mei YQ, Li YH, Chen L, Zhou YM. Watch out for version mismtaching and data leakage! A case study of their influence in bug report based bug localization models. Ruan Jian Xue Bao/Journal of Software, 2023, 34(5): 2196-2217(in Chinese with English abstract). http://www.jos.org.cn/1000-9825/6401.htm [doi: 10.13328/j.cnki.jos.006401]
    [6] Wen M, Wu RX, Cheung SC. Locus: Locating bugs from software changes. In: Proc. of the 31st IEEE/ACM Int'l Conf. on Automated Software Engineering. Singapore: ACM, 2016. 262-273. [doi: 10.1145/2970276.2970359]
    [7] Lam AN, Nguyen AT, Nguyen HA, Nguyen TN. Bug localization with combination of deep learning and information retrieval. In: Proc. of the 25th IEEE/ACM Int'l Conf. on Program Comprehension. Buenos Aires: IEEE, 2017. 218-299. [doi: 10.1109/ICPC. 2017.24]
    [8] Xiao Y, Keung J. Improving bug localization with character-level convolutional neural network and recurrent neural network. In: Proc. of the 25th Asia-Pacific Software Engineering Conf. Nara: IEEE, 2018. 703-704. [doi: 10.1109/APSEC.2018.00097]
    [9] 钟仁毅, 王翀, 梁鹏, 罗忠. 基于版本更新日志的移动应用演化趋势自动分析. 计算机研究与发展, 2021, 58(4): 763-776. [doi: 10.7544/issn1000-1239.2021.20200756]
    Zhong RY, Wang C, Liang P, Luo Z. Automatic trend analysis of mobile app updates based on app changelogs. Journal of Computer Research and Development, 2021, 58(4): 763-776(in Chinese with English abstract). [doi: 10.7544/issn1000-1239.2021. 20200756]
    [10] Siers MJ, Islam MZ. Software defect prediction using a cost sensitive decision forest and voting, and a potential solution to the class imbalance problem. Information Systems, 2015, 51: 62-71. [doi: 10.1016/j.is.2015.02.006]
    [11] Wu F, Jing XY, Dong X, Cao JC, Xu MW, Zhang HY, Ying S, Xu BW. Cross-project and within-project semi-supervised software defect prediction problems study using a unified solution. In: Proc. of the 39th IEEE/ACM Int'l Conf. on Software Engineering Companion (ICSE-C). IEEE, 2017. 195-197. [doi: 10.1109/ICSE-C.2017.72]
    [12] Wijayasekara D, Manic M, Wright JL, McQueen M. Mining bug databases for unidentified software vulnerabilities. In: Proc. of the 5th Int'l Conf. on Human System Interactions. IEEE, 2012. 89-96. [doi: 10.1109/HSI.2012.22]
    [13] 郑炜, 陈军正, 吴潇雪, 陈翔, 夏鑫. 基于深度学习的安全缺陷报告预测方法实证研究. 软件学报, 2020, 31(5): 1294-1313. http://www.jos.org.cn/1000-9825/5954.htm [doi: 10.13328/j.cnki.jos.005954]
    Zheng W, Chen JZ, Wu XX, Chen X, Xia X. Empirical studies on deep-learning-based security bug report prediction methods. Ruan Jian Xue Bao/Journal of Software, 2020, 31(5): 1294-1313(in Chinese with English abstract). http://www.jos.org.cn/1000-9825/5954.htm [doi: 10.13328/j.cnki.jos.005954]
    [14] 蒋远, 牟辰光, 苏小红, 王甜甜. 噪音过滤和深度学习相结合的安全缺陷报告识别. 计算机学报, 2022, 45(8): 1794-1813. [doi: 10.11897/SP.J.1016.2022.01794]
    Jiang Y, Mu CG, Su XH, Wang TT. Security bug report detection via noise filtering and deep learning. Chinese Journal of Computers, 2022, 45(8): 1794-1813(in Chinese with English abstract). [doi: 10.11897/SP.J.1016.2022.01794]
    [15] Chen JF, Wang XL, Cai SH, Xu JP, Chen JY, Chen HB. A software defect prediction method with metric compensation based on featureselection and transfer learning. Frontiers of Information Technology & Electronic Engineering, 2022, 23(5): 715-732. [doi: 10.1631/fitee.2100468]
    [16] Wan Z, Xia X, Hassan AE, Lo D, Yin JW, Yang XH. Perceptions, expectations, and challenges in defect prediction. IEEE Trans. on Software Engineering, 2018, 46(11): 1241-1266. [doi: 10.1109/TSE.2018.2877678]
    [17] Zhang T, Sun XB, Zheng ZB, Li G. Intelligent analysis for software data: Research and applications. Frontiers of Information Technology & Electronic Engineering, 2022, 23(5): 661-666. [doi: 10.1631/fitee.2230000]
    [18] Peters F, Tun TT, Yu Y, Nuseibeh B. Text filtering and ranking for security bug report prediction. IEEE Trans. on Software Engineering, 2017, 45(6): 615-631. [doi: 10.1109/TSE.2017.2787653]
    [19] 姜淑娟, 张旭, 王荣存, 黄颖, 张艳梅, 薛猛. 基于路径分析和信息熵的错误定位方法. 软件学报, 2021, 32(7): 2166-2182. http://www.jos.org.cn/1000-9825/6262.htm [doi: 10.13328/j.cnki.jos.006262]
    Jiang SJ, Zhang X, Wang RC, Huang Y, Zhang YM, Xue M. Fault localization approach based on path analysis and information entropy. Ruan Jian Xue Bao/Journal of Software, 2021, 32(7): 2166-2182(in Chinese with English abstract). http://www.jos.org.cn/1000-9825/6262.htm [doi: 10.13328/j.cnki.jos.006262]
    [20] 李铮, 崔展齐, 陈翔, 王荣存, 刘建宾, 郑丽伟. Deep-SBFL: 基于频谱的深度神经网络缺陷定位方法. 软件学报, 2023, 34(5): 2231-2250. http://www.jos.org.cn/1000-9825/6403.htm [doi: 10.13328/j.cnki.jos.006403]
    Li Z, Cui ZQ, Chen X, Wang RC, Liu JB, Zheng LW. Deep-SBFL: Spectrum-based fault localization approach for deep neural networks. Ruan Jian Xue Bao/Journal of Software, 2023, 34(5): 2231-2250(in Chinese with English abstract). http://www.jos.org.cn/1000-9825/6403.htm [doi: 10.13328/j.cnki.jos.006403]
    [21] Saidani I, Ouni A, Ahasanuzzaman M, Hassan S, MKaouer MW, Hassan AE. Tracking bad updates in mobile apps: A search-based approach. Empirical Software Engineering, 2022, 27(4): 81. [doi: 10.1007/s10664-022-10125-6]
    [22] Zhou W, Wang Y, Gao CY, Yang F. Emerging topic identification from app reviews via adaptive online biterm topic modeling. Frontiers of Information Technology & Electronic Engineering, 2022, 23(5): 678-692. [doi: 10.1631/fitee.2100465]
    [23] Zhao Y, Yu TT, Su T, Liu Y, Zheng W, Zhang JZ, Halfond WGJ. ReCDroid: Automatically reproducing Android application crashes from bug reports. In: Proc. of the 41st IEEE/ACM Int'l Conf. on Software Engineering (ICSE). Montreal, 2019. 128-139. [doi: 10.1109/ICSE.2019.00030]
    [24] Fazzini M, Moran K, Bernal-Cardenas C, Wendland T, Orso A, Poshyvanyk D. Enhancing mobile app bug reporting via real-time0-9825/6268.htm [doi: 10.13328/j.cnki.jos.006268]
    Wang L, Li QS, Lü WQ, Zhang H, Li H. Self-adaptation analysis method for recognition quality assurance using event relationships. Ruan Jian Xue Bao/Journal of Software, 2021, 32(7): 1978-1998(in Chinese with English abstract). http://www.jos.org.cn/1000-9825/6268.htm [doi: 10.13328/j.cnki.jos.006268]
    [41] Dong Y, Chawla NV, Swami A. metapath2vec: Scalable representation learning for heterogeneous networks. In: Proc. of the 23rd ACM SIGKDD Int'l Conf. on Knowledge Discovery and Data Mining. 2017. 135-144. [doi: 10.1145/3097983.3098036]
    [42] Song Y, Shi S, Li J, Zhang HS. Directional skip-gram: Explicitly distinguishing left and right context for word embeddings. In: Proc. of the 2018 Conf. of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Vol.2(Short Papers). 2018. 175-180. [doi: 10.18653/v1/N18-2028]
    [43] Guo H, Singh MP. Caspar: Extracting and synthesizing user stories of problems from app reviews. In: Proc. of the 42nd ACM/IEEE Int'l Conf. on Software Engineering. 2020. 628-640. [doi: 10.1145/3377811.3380924]23(5): 715-731. [doi: 10.1631/fitee.2100468]
    [29] 李伟湋, 陈翔, 张恒伟, 黄志球, 贾修一. 一种基于同步语义对齐的异构缺陷预测方法. 软件学报, 2023, 34(6): 2669-2689. http://www.jos.org.cn/1000-9825/6495.htm [doi: 10.13328/j.cnki.jos.006495]
    Li WW, Chen X, Zhang HW, Huang ZQ, Jia XY. Heterogeneous defect prediction based on simultaneous semantic alignment. Ruan Jian Xue Bao/Journal of Software, 2023, 34(6): 2669-2689(in Chinese with English abstract). http://www.jos.org.cn/1000-9825/6495.htm [doi: 10.13328/j.cnki.jos.006495]
    [30] Bahdanau D, Cho K, Bengio Y. Neural machine translation by jointly learning to align and translate. arXiv:1409.0473, 2014.
    [31] Zhang Y, Wallace B. A sensitivity analysis of (and practitioners' guide to) convolutional neural networks for sentence classification. arXiv:1510.03820, 2015.
    [32] Liu P, Qiu X, Huang X. Recurrent neural network for text classification with multi-task learning. arXiv:1605.05101, 2016.
    [33] Greff K, Srivastava RK, Koutník J, Steunebrink BR, Schmidhuber J. LSTM: A search space odyssey. IEEE Trans. on Neural Networks and Learning Systems, 2016, 28(10): 2222-2232. [doi: 10.1109/tnnls.2016.2582924]
    [34] Mani S, Sankaran A, Aralikatte R. Deeptriage: Exploring the effectiveness of deep learning for bug triaging. In: Proc. of the ACM India Joint Int'l Conf. on Data Science and Management of Data. 2019. 171-179. [doi: 10.1145/3297001.3297023]
    [35] Shi C, Li YT, Zhang JW, Sun YZ, Philip SY. A survey of heterogeneous information network analysis. IEEE Trans. on Knowledge and Data Engineering, 2016, 29(1): 17-37. [doi: 10.1109/TKDE.2016.2598561]
    [36] Wang X, Ji H, Shi C, Wang B, Ye YF, Cui P, Yu PS. Heterogeneous graph attention network. In: Proc. of the World Wide Web Conf. 2019. 2022-2032.
    [37] Wang X, Liu N, Han H, Shi C. Self-supervised heterogeneous graph neural network with co-contrastive learning. In: Proc. of the 27th ACM SIGKDD Conf. on Knowledge Discovery & Data Mining. 2021. 1726-1736. [doi: 10.1145/3447548.3467415]
    [38] 石美惠, 申德荣, 寇月, 聂铁铮, 于戈. 融合全局和局部特征的下一个兴趣点推荐方法. 软件学报, 2023, 34(2): 786-801. http://www.jos.org.cn/1000-9825/6712.htm [doi: 10.13328/j.cnki.jos.006712]
    Shi MH, Shen DR, Cou Y, Nie TZ, Yu G. Next point-of-interest recommendation approach with global and local feature fusion. Ruan Jian Xue Bao/Journal of Software, 2023, 34(2): 786-801(in Chinese with English abstract). http://www.jos.org.cn/1000-9825/6712.htm [doi: 10.13328/j.cnki.jos.006712]
    [39] 胡甜媛, 姜瑛. 体现使用反馈的APP软件用户评论挖掘. 软件学报, 2019, 30(10): 3168-3185. http://www.jos.org.cn/1000-9825/5794.htm [doi: 10.13328/j.cnki.jos.005794]
    Hu TY, Jiang Y. Mining of user's comments reflecting usage feedback for APP software. Ruan Jian Xue Bao/Journal of Software, 2019, 30(10): 3168-3185(in Chinese with English abstract). http://www.jos.org.cn/1000-9825/5794.htm [doi: 10.13328/j.cnki. jos.005794]
    [40] 王璐, 李青山, 吕文琪, 张河, 李昊. 基于事件关系保障识别质量的自适应分析方法. 软件学报, 2021, 32(7): 1978-1998. http://www.jos.org.cn/100????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????
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刘海毅,姜瑛,赵泽江.面向版本演化的APP软件缺陷跟踪分析方法.软件学报,2024,35(7):3180-3203

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  • 收稿日期:2023-09-10
  • 最后修改日期:2023-10-30
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