微信服务号

微信订阅号

2025年5月1日 13:35 星期四
首页 > 过刊浏览>2025年第36卷第4期 >1530-1556. DOI:10.13328/j.cnki.jos.007288
PDF HTML阅读 XML下载 导出引用 引用提醒
深度学习在基于信息检索的缺陷定位中的应用综述
作者:
中图分类号:

TP311

基金项目:

国家重点研发计划(2022YFF0711404); 江苏省自然科学基金(BK20201250, BK20210279)


Survey on Deep Learning Applications in Information Retrieval-based Bug Localization
Author:
  • 摘要
    • | |
  • 访问统计
    • |
  • 参考文献 [100]
    • |
  • 相似文献
    • | | |
  • 文章评论
    摘要:

    缺陷自动定位方法可以极大程度减轻开发人员调试和维护软件程序的负担. 基于信息检索的缺陷定位方法是广泛研究的缺陷自动定位方法之一, 并已取得了较好的成果. 随着深度学习的普及, 将深度学习应用于基于信息检索的缺陷定位成为近年来的研究趋势之一. 系统梳理和总结了52篇近年来将深度学习引入基于信息检索缺陷定位的工作. 首先, 总结该类缺陷定位的数据集和评价指标, 接着从不同粒度和可迁移性分析了该类技术的定位效果, 随后着重梳理了相关工作中信息编码表征方法和特征提取方法. 最后总结对比分析了各领域最先进的定位方法, 并展望了使用深度学习的基于信息检索的缺陷定位方法的未来发展方向.

    Abstract:

    Automatic bug localization technologies can significantly alleviate the burden of debugging and maintaining software programs for developers. As a widely studied automatic bug localization technology, information retrieval-based bug localization has yielded promising performance of bug localization. In recent years, the utilization of deep learning for information retrieval-based bug localization has emerged as a research trend due to the widespread adoption of deep learning. This study systematically categorizes and summarizes 52 studies that have introduced deep learning to information retrieval-based bug localization in recent years. Firstly, a summary of datasets and evaluation indexes in this kind of bug localization is provided. Then, the localization performance of these techniques is analyzed from the perspectives of different granularity and transportability. Subsequently, information coding characterization methods and feature extraction methods employed in related studies are summarized. Finally, this study summarizes and compares the most advanced bug localization methods, and provides insights into the future directions of utilizing deep learning in information retrieval-based bug localization methods.

    参考文献
    [1] Tassey G. The economic impacts of inadequate infrastructure for software testing. Planning Report 02-3, Gaithersburg: National Institute of Standards and Technology, 2002.
    [2] Jones JA. Semi-automatic Fault Localization. Atlanta: Georgia Institute of Technology, 2008.
    [3] Bissyandé TF, Réveillère L, Lawall JL, Muller G. Diagnosys: Automatic generation of a debugging interface to the Linux kernel. In: Proc. of the 27th IEEE/ACM Int’l Conf. on Automated Software Engineering. Essen: ACM, 2012. 60–69. [doi: 10.1145/2351676.2351686]
    [4] Britton T, Jeng L, Carver G, Cheak P, Katzenellenbogen T. Reversible debugging software. Technical Report, Cambridge: University of Cambridge, 2013.
    [5] Akbar SA, Kak AC. A large-scale comparative evaluation of IR-based tools for bug localization. In: Proc. of the 17th Int’l Conf. on Mining Software Repositories. Seoul: ACM, 2020. 21–31. [doi: 10.1145/3379597.3387474]
    [6] Robertson SE, Spärck Jones K. Simple, proven approaches to text retrieval. Cambridge: University of Cambridge, 1994.
    [7] Zhou J, Zhang HY, Lo D. Where should the bugs be fixed? More accurate information retrieval-based bug localization based on bug reports. In: Proc. of the 34th Int’l Conf. on Software Engineering. Zurich: IEEE, 2012. 14–24. [doi: 10.1109/icse.2012.6227210]
    [8] Sisman B, Kak AC. Incorporating version histories in information retrieval based bug localization. In: Proc. of the 9th IEEE Working Conf. on Mining Software Repositories. Zurich: IEEE, 2012. 50–59. [doi: 10.1109/msr.2012.6224299]
    [9] Saha RK, Lease M, Khurshid S, Perry DE. Improving bug localization using structured information retrieval. In: Proc. of the 28th IEEE/ACM Int’l Conf. on Automated Software Engineering. Silicon Valley: IEEE, 2013. 345–355. [doi: 10.1109/ase.2013.6693093]
    [10] Wong CP, Xiong YF, Zhang HY, Hao D, Zhang L, Mei H. Boosting bug-report-oriented fault localization with segmentation and stack-trace analysis. In: Proc. of the 2014 IEEE Int’l Conf. on Software Maintenance and evolution. Victoria: IEEE, 2014. 181–190. [doi: 10.1109/icsme.2014.40]
    [11] Wang SW, Lo D. Version history, similar report, and structure: Putting them together for improved bug localization. In: Proc. of the 22nd Int’l Conf. on Program Comprehension. Hyderabad: ACM, 2014. 53–63. [doi: 10.1145/2597008.2597148]
    [12] Youm KC, Ahn J, Kim J, Lee E. Bug localization based on code change histories and bug reports. In: Proc. of the 2015 Asia-Pacific Software Engineering Conf. New Delhi: IEEE, 2015. 190–197. [doi: 10.1109/apsec.2015.23]
    [13] 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]
    [14] Lam AN, Nguyen AT, Nguyen HA, Nguyen TN. Combining deep learning with information retrieval to localize buggy files for bug reports. In: Proc. of the 30th IEEE/ACM Int’l Conf. on Automated Software Engineering. Lincoln: IEEE, 2015. 476–481. [doi: 10.1109/ase.2015.73]
    [15] Huo X, Li M, Zhou ZH. Learning unified features from natural and programming languages for locating buggy source code. In: Proc. of the 25th Int’l Joint Conf. on Artificial Intelligence. New York: IJCAI/AAAI Press, 2016. 1606–1612.
    [16] Xiao Y, Keung J, Mi Q, Bennin KE. Bug localization with semantic and structural features using convolutional neural network and cascade forest. In: Proc. of the 22nd Int’l Conf. on Evaluation and Assessment in Software Engineering. Christchurch: ACM, 2018. 101–111. [doi: 10.1145/3210459.3210469]
    [17] Cao JM, Yang SL, Jiang WH, Zeng HS, Shen BJ, Zhong H. BugPecker: Locating faulty methods with deep learning on revision graphs. In: Proc. of the 35th IEEE/ACM Int’l Conf. on Automated Software Engineering. ACM, 2020. 1214–1218.
    [18] Zhu ZY, Wang Y, Li Y. TROBO: A novel deep transfer model for enhancing cross-project bug localization. In: Proc. of the 14th Int’l Conf. on Knowledge Science, Engineering and Management. Tokyo: Springer, 2021. 529–541. [doi: 10.1007/978-3-030-82136-4_43]
    [19] Han JX, Huang C, Sun SQ, Liu ZL, Liu JY. bjXnet: An improved bug localization model based on code property graph and attention mechanism. Automated Software Engineering, 2023, 30(1): 12.
    [20] Wong WE, Gao RZ, Li YH, Abreu R, Wotawa F. A survey on software fault localization. IEEE Trans. on Software Engineering, 2016, 42(8): 707–740.
    [21] Lee J, Kim D, Bissyandé TF, Jung W, Le Traon Y. Bench4BL: Reproducibility study on the performance of IR-based bug localization. In: Proc. of the 27th ACM SIGSOFT Int’l Symp. on Software Testing and Analysis. Amsterdam: ACM, 2018. 61–72.
    [22] 张芸, 刘佳琨, 夏鑫, 吴明晖, 颜晖. 基于信息检索的软件缺陷定位技术研究进展. 软件学报, 2020, 31(8): 2432–2452. http://www.jos.org.cn/1000-9825/6081.htm
    Zhang Y, Liu JK, Xia X, Wu MH, Yan H. Research progress on software bug localization technology based on information retrieval. Ruan Jian Xue Bao/Journal of Software, 2020, 31(8): 2432–2452 (in Chinese with English abstract). http://www.jos.org.cn/1000-9825/6081.htm
    [23] 郭肇强, 周慧聪, 刘释然, 李言辉, 陈林, 周毓明, 徐宝文. 基于信息检索的缺陷定位: 问题、进展与挑战. 软件学报, 2020, 31(9): 2826–2854. http://www.jos.org.cn/1000-9825/6087.htm
    Guo ZQ, Zhou HC, Liu SR, Li YH, Chen L, Zhou YM, Xu BW. Information retrieval based bug localization: Research problem, progress, and challenges. Ruan Jian Xue Bao/Journal of Software, 2020, 31(9): 2826–2854 (in Chinese with English abstract). http://www.jos.org.cn/1000-9825/6087.htm
    [24] 李政亮, 陈翔, 蒋智威, 顾庆. 基于信息检索的软件缺陷定位方法综述. 软件学报, 2021, 32(2): 247–276. http://www.jos.org.cn/1000-9825/6130.htm
    Li ZL, Chen X, Jiang ZW, Gu Q. Survey on information retrieval-based software bug localization methods. Ruan Jian Xue Bao/Journal of Software, 2021, 32(2): 247–276 (in Chinese with English abstract). http://www.jos.org.cn/1000-9825/6130.htm
    [25] 倪珍, 李斌, 孙小兵, 李必信, 朱程. 面向软件缺陷报告的缺陷定位方法研究与进展. 计算机科学, 2022, 49(11): 8–23.
    Ni Z, Li B, Sun XB, Li BX, Zhu C. Research and progress on bug report-oriented bug localization techniques. Computer Science, 2022, 49(11): 8–23 (in Chinese with English abstract).
    [26] Mohsen AM, Hassan H, Moawad R, Makady S. A review on software bug localization techniques using a motivational example. Int’l Journal of Advanced Computer Science and Applications (IJACSA), 2022, 13(2): 251–261.
    [27] Zamfirov F. A literature review on different types of empirically evaluated bug localization approaches. arXiv:2212.11774, 2022.
    [28] Huo X, Li M. Enhancing the unified features to locate buggy files by exploiting the sequential nature of source code. In: Proc. of the 26th Int’l Joint Conf. on Artificial Intelligence. Melbourne: ijcai.org, 2017. 1909–1915. [doi: 10.24963/ijcai.2017/265]
    [29] 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. 218–229. [doi: 10.1109/icpc.2017.24]
    [30] Xiao Y, Keung J, Mi Q, Bennin KE. Improving bug localization with an enhanced convolutional neural network. In: Proc. of the 24th Asia-Pacific Software Engineering Conf. Nanjing: IEEE, 2017. 338–347. [doi: 10.1109/apsec.2017.40]
    [31] Xiao Y, Keung J, Bennin KE, Mi Q. Machine translation-based bug localization technique for bridging lexical gap. Information and Software Technology, 2018, 99: 58–61.
    [32] Loyola P, Gajananan K, Satoh F. Bug localization by learning to rank and represent bug inducing changes. In: Proc. of the 27th ACM Int’l Conf. on Information and Knowledge Management. Torino: ACM, 2018. 657–665. [doi: 10.1145/3269206.3271811]
    [33] 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]
    [34] Huo X, Thung F, Li M, Lo D, Shi ST. Deep transfer bug localization. IEEE Trans. on Software Engineering, 2021, 47(7): 1368–1380.
    [35] Liang HL, Sun L, Wang ML, Yang YX. Deep learning with customized abstract syntax tree for bug localization. IEEE Access, 2019, 7: 116309–116320.
    [36] Xiao Y, Keung J, Bennin KE, Mi Q. Improving bug localization with word embedding and enhanced convolutional neural networks. Information and Software Technology, 2019, 105: 17–29.
    [37] Liu GL, Lu Y, Shi K, Chang JF, Wei X. Convolutional neural networks-based locating relevant buggy code files for bug reports affected by data imbalance. IEEE Access, 2019, 7: 131304–131316.
    [38] Polisetty S, Miranskyy A, Başar A. On usefulness of the deep-learning-based bug localization models to practitioners. In: Proc. of the 15th Int’l Conf. on Predictive Models and Data Analytics in Software Engineering. Recife: ACM, 2019. 16–25.
    [39] Huo X, Li M, Zhou ZH. Control flow graph embedding based on multi-instance decomposition for bug localization. In: Proc. of the 34th AAAI Conf. on Artificial Intelligence. New York: AAAI, 2020. 4223–4230. [doi: 10.1609/aaai.v34i04.5844]
    [40] Zhu ZY, Li Y, Tong HH, Wang Y. CooBa: Cross-project bug localization via adversarial transfer learning. In: Proc. of the 29th Int’l Joint Conf. on Artificial Intelligence. Yokohama: ijcai.org, 2020. 3565–3571. [doi: 10.24963/ijcai.2020/493]
    [41] Wang B, Xu L, Yan M, Liu C, Liu L. Multi-dimension convolutional neural network for bug localization. IEEE Trans. on Services Computing, 2022, 15(3): 1649–1663.
    [42] Jiang B, Liu PF, Xu J. A deep learning approach to locate buggy files. In: Proc. of the 11th IEEE Int’l Conf. on Dependable Systems, Services and Technologies. Kyiv: IEEE, 2020. 219–223. [doi: 10.1109/dessert50317.2020.9125003]
    [43] Zhang JL, Xie R, Ye W, Zhang YH, Zhang SK. Exploiting code knowledge graph for bug localization via bi-directional attention. In: Proc. of the 28th Int’l Conf. on Program Comprehension. Seoul: ACM, 2020. 219–229. [doi: 10.1145/3387904.3389281]
    [44] Yang G, Min K, Lee B. Applying deep learning algorithm to automatic bug localization and repair. In: Proc. of the 35th Annual ACM Symp. on Applied Computing. Brno: ACM, 2020. 1634–1641. [doi: 10.1145/3341105.3374005]
    [45] Yuan W, Qi BH, Sun HL, Liu XD. Dependloc: A dependency-based framework for bug localization. In: Proc. of the 27th Asia-Pacific Software Engineering Conf. Singapore: IEEE, 2020. 61–70. [doi: 10.1109/apsec51365.2020.00014]
    [46] Sangle S, Muvva S, Chimalakonda S, Ponnalagu K, Venkoparao VG. DRAST—A deep learning and AST based approach for bug localization. arXiv:2011.03449, 2020.
    [47] Zhu ZY, Li Y, Wang Y, Wang YJ, Tong HH. A deep multimodal model for bug localization. Data Mining and Knowledge Discovery, 2021, 35(4): 1369–1392.
    [48] Yang SL, Cao JM, Zeng HS, Shen BJ, Zhong H. Locating faulty methods with a mixed RNN and attention model. In: Proc. of the 29th IEEE/ACM Int’l Conf. on Program Comprehension. Madrid: IEEE, 2021. 207–218. [doi: 10.1109/icpc52881.2021.00028]
    [49] Qi BH, Sun HL, Yuan W, Zhang HY, Meng XX. DreamLoc: A deep relevance matching-based framework for bug localization. IEEE Trans. on Reliability, 2022, 71(1): 235–249.
    [50] Anh BTM, Luyen NV. An imbalanced deep learning model for bug localization. In: Proc. of the 28th Asia-Pacific Software Engineering Conf. Workshops. IEEE, 2021. 32–40. [doi: 10.1109/apsecw53869.2021.00017]
    [51] Yang G, Lee B. Utilizing topic-based similar commit information and CNN-LSTM algorithm for bug localization. Symmetry, 2021, 13(3): 406.
    [52] Ciborowska A, Damevski K. Fast changeset-based bug localization with BERT. In: Proc. of the 44th Int’l Conf. on Software Engineering. Pittsburgh: ACM, 2022. 946–957. [doi: 10.1145/3510003.3510042]
    [53] Zhu ZY, Tong HH, Wang Y, Li Y. BL-GAN: Semi-supervised bug localization via generative adversarial network. IEEE Trans. on Knowledge and Data Engineering, 2023, 35(11): 11112–11125.
    [54] Liang HL, Hang DJ, Li XY. Modeling function-level interactions for file-level bug localization. Empirical Software Engineering, 2022, 27(7): 186.
    [55] Luo ZM, Wang WY, Cen CC. Improving bug localization with effective contrastive learning representation. IEEE Access, 2023, 11: 32523–32533.
    [56] Ma YF, Li M. Learning from the multi-level abstraction of the control flow graph via alternating propagation for bug localization. In: Proc. of the 2022 IEEE Int’l Conf. on Data Mining. Orlando: IEEE, 2022. 299–308. [doi: 10.1109/icdm54844.2022.00040]
    [57] Ma YF, Li M. The flowing nature matters: Feature learning from the control flow graph of source code for bug localization. Machine Learning, 2022, 111(3): 853–870.
    [58] Chen H, Yang HY, Yan ZL, Kuang L, Zhang LY. CGMBL: Combining GAN and method name for bug localization. In: Proc. of the 22nd IEEE Int’l Conf. on Software Quality, Reliability and Security. Guangzhou: IEEE, 2022. 231–241.
    [59] Zhu ZY, Tong HH, Wang Y, Li Y. Enhancing bug localization with bug report decomposition and code hierarchical network. Knowledge-based Systems, 2022, 248: 108741.
    [60] Shi XY, Ju XL, Chen X, Lu GL, Xu MQ. SemirFL: Boosting fault localization via combining semantic information and information retrieval. In: Proc. of the 22nd IEEE Int’l Conf. on Software Quality, Reliability, and Security Companion. Guangzhou: IEEE, 2022. 324–332. [doi: 10.1109/qrs-c57518.2022.00055]
    [61] Kim M, Kim Y, Lee E. An empirical study of IR-based bug localization for deep learning-based software. In: Proc. of the 2022 IEEE Conf. on Software Testing, Verification and Validation. Valencia: IEEE, 2022. 128–139. [doi: 10.1109/icst53961.2022.00024]
    [62] Huang XX, Xiang C, Li H, He P. SBuglocater: Bug localization based on deep matching and information retrieval. Mathematical Problems in Engineering, 2022, 2022: 3987981.
    [63] Chakraborty P, Alfadel M, Nagappan M. RLocator: Reinforcement learning for bug localization. IEEE Trans. on Software Engineering, 2024, 50(10): 2695–2708.
    [64] Al-Aidaroos AS, Bamzahem SM. The impact of GloVe and Word2Vec word-embedding technologies on bug localization with convolutional neural network. Int’l Journal of Science and Engineering Applications, 2023, 12(1): 108–111.
    [65] Ciborowska A, Damevski K. Too few bug reports? Exploring data augmentation for improved changeset-based bug localization. arXiv: 2305.16430, 2023.
    [66] Ahmad AA, Yu LS, Kholief M, Garba A. AttentiveBugLocator: A bug localization model using attention-based semanticfeatures and information retrieval. 2023. [doi: 10.21203/rs.3.rs-3348519/v1]
    [67] Xiao X, Xiao RJ, Li Q, Lv JH, Cui SY, Liu QX. BugRadar: Bug localization by knowledge graph link prediction. Information and Software Technology, 2023, 162: 107274.
    [68] Ma YF, Du YL, Li M. Capturing the long-distance dependency in the control flow graph via structural-guided attention for bug localization. In: Proc. of the 32nd Int’l Joint Conf. on Artificial Intelligence. 2023. 2242–2250.
    [69] Mohsen AM, Hassan HA, Wassif KT, Moawad R, Makady SH. Enhancing bug localization using phase-based approach. IEEE Access, 2023, 11: 35901–35913.
    [70] Du YL, Yu ZX. Pre-training code representation with semantic flow graph for effective bug localization. In: Proc. of the 31st ACM Joint European Software Engineering Conf. and Symp. on the Foundations of Software Engineering. New York: ACM, 2023. 579–591. [doi: 10.1145/3611643.3616338]
    [71] Ali W, Bo LL, Sun XB, Wu XX, Memon S, Siraj S, Suwaree Ashton A. Automated software bug localization enabled by meta-heuristic-based convolutional neural network and improved deep neural network. Expert Systems with Applications, 2023, 232: 120562.
    [72] Xu GQ, Wang XQ, Wei D, Shao YL, Chen B. Bug localization with features crossing and structured semantic information matching. Int’l Journal of Software Engineering and Knowledge Engineering, 2023, 33(8): 1261–1291.
    [73] Rahman F, Posnett D, Hindle A, Barr E, Devanbu P. BugCache for inspections: Hit or miss? In: Proc. of the 19th ACM SIGSOFT Symp. and the 13th European Conf. on Foundations of Software Engineering. Szeged: ACM, 2011. 322–331. [doi: 10.1145/2025113.2025157]
    [74] Voorhees EM. The TREC-8 question answering track report. In: Proc. of the 2nd LREC. 2000. 77–82.
    [75] Manning CD, Raghavan P, Schütze H. Introduction to Information Retrieval. Cambridge: Cambridge University Press, 2008.
    [76] Kochhar PS, Xia X, Lo D, LI SP, Claims A. Practitioners’ expectations on automated fault localization. In: Proc. of the 25th Int’l Symp. on Software Testing and Analysis. Saarbrücken: ACM, 2016. 165–176. [doi: 10.1145/2931037.2931051]
    [77] Youm KC, Ahn J, Lee E. Improved bug localization based on code change histories and bug reports. Information and Software Technology, 2017, 82: 177–192.
    [78] Rahman S, Rahman MM, Sakib K. A statement level bug localization technique using statement dependency graph. In: Proc. of the 12th Int’l Conf. on Evaluation of Novel Approaches to Software Engineering. Porto: SciTePress, 2017. 171–178.
    [79] Hinton GE, Salakhutdinov RR. Reducing the dimensionality of data with neural networks. Science, 2006, 313(5786): 504–507.
    [80] Kim D, Tao YD, Kim S, Zeller A. Where should we fix this bug? A two-phase recommendation model. IEEE Trans. on Software Engineering, 2013, 39(11): 1597–1610.
    [81] Perozzi B, Al-Rfou R, Skiena S. Deepwalk: Online learning of social representations. In: Proc. of the 20th ACM SIGKDD Int’l Conf. on Knowledge Discovery and Data Mining. New York: ACM, 2014. 701–710. [doi: 10.1145/2623330.2623732]
    [82] Ye X, Bunescu R, Liu C. Learning to rank relevant files for bug reports using domain knowledge. In: Proc. of the 22nd ACM SIGSOFT Int’l Symp. on Foundations of Software Engineering. ACM, 2014. 689–699. [doi: 10.1145/2635868.2635874]
    [83] Lin JF, Liu YL, Zeng QK, Jiang M, Cleland-Huang J. Traceability transformed: Generating more accurate links with pre-trained BERT models. In: Proc. of the 43rd IEEE/ACM Int’l Conf. on Software Engineering. Madrid: IEEE, 2021. 324–335.
    [84] Fejzer M, Narębski J, Przymus P, Stencel K. Tracking buggy files: New efficient adaptive bug localization algorithm. IEEE Trans. on Software Engineering, 2022, 48(7): 2557–2569.
    [85] Ye X, Bunescu R, Liu C. Mapping bug reports to relevant files: A ranking model, a fine-grained benchmark, and feature evaluation. IEEE Trans. on Software Engineering, 2016, 42(4): 379–402.
    [86] Gay G, Haiduc S, Marcus A, Menzies T. On the use of relevance feedback in IR-based concept location. In: Proc. of Int’l Conf. on Software Maintenance. 2009. 351–360. [doi: 10.1109/icsm.2009.5306315]
    [87] Turhan B, Menzies T, Bener AB, Di Stefano J. On the relative value of cross-company and within-company data for defect prediction. Empirical Software Engineering, 2009, 14(5): 540–578.
    [88] Rao S, Kak A. Retrieval from software libraries for bug localization: A comparative study of generic and composite text models. In: Proc. of the 8th Working Conf. on Mining Software Repositories. Honolulu: ACM, 2011. 43–52. [doi: 10.1145/1985441.1985451]
    [89] Nam J, Pan SJ, Kim S. Transfer defect learning. In: Proc. of the 35th Int’l Conf. on Software Engineering. San Francisco: IEEE, 2013. 382–391. [doi: 10.1109/ICSE.2013.6606584]
    [90] Ye X, Shen H, Ma X, Bunescu R, Liu C. From word embeddings to document similarities for improved information retrieval in software engineering. In: Proc. of the 38th Int’l Conf. on Software Engineering. Austin: ACM, 2016. 404–415. [doi: 10.1145/2884781.2884862]
    [91] Zhang W, Li ZQ, Wang Q, Li J. FineLocator: A novel approach to method-level fine-grained bug localization by query expansion. Information and Software Technology, 2019, 110: 121–135.
    [92] Rahman MM, Roy CK. Improving IR-based bug localization with context-aware query reformulation. In: Proc. of the 26th ACM Joint Meeting on European Software Engineering Conf. and Symp. on the Foundations of Software Engineering. Lake Buena: ACM, 2018. 621–632. [doi: 10.1145/3236024.3236065]
    [93] Donahue J, Anne Hendricks L, Guadarrama S, Rohrbach M, Venugopalan S, Darrell T. Long-term recurrent convolutional networks for visual recognition and description. In: Proc. of the 2015 IEEE Conf. on Computer Vision and Pattern Recognition. Boston: IEEE, 2015. 2625–2634. [doi: 10.1109/CVPR.2015.7298878]
    [94] Just R, Jalali D, Ernst MD. Defects4J: A database of existing faults to enable controlled testing studies for Java programs. In: Proc. of the 2014 Int’l Symp. on Software Testing and Analysis. San Jose: ACM, 2014. 437–440. [doi: 10.1145/2610384.2628055]
    [95] Ding YRB, Chakraborty S, Buratti L, Pujar S, Morari A, Kaiser G, Ray B. CONCORD: Clone-aware contrastive learning for source code. In: Proc. of the 32nd ACM SIGSOFT Int’l Symp. on Software Testing and Analysis. Seattle: ACM, 2023. 26–38.
    [96] Zeng C, Yu Y, Li SS, Xia X, Wang ZM, Geng MY, Bai LX, Dong W, Liao XK. DEGRAPHCS: Embedding variable-based flow graph for neural code search. ACM Trans. on Software Engineering and Methodology, 2023, 32(2): 34.
    相似文献
    引证文献
    网友评论
    网友评论
    分享到微博
    发 布
引用本文

曹帅,牛菲菲,李传艺,陈俊洁,刘逵,葛季栋,骆斌.深度学习在基于信息检索的缺陷定位中的应用综述.软件学报,2025,36(4):1530-1556

复制
分享
文章指标
  • 点击次数:209
  • 下载次数: 566
  • HTML阅读次数: 95
  • 引用次数: 0
历史
  • 收稿日期:2024-02-05
  • 最后修改日期:2024-04-23
  • 在线发布日期: 2025-01-16
文章二维码
友情链接:中国科学院软件研究所中国计算机学会中国科学院国家自然科学基金委员会CCF数字图书馆Int'l J of Softw Info计算机系统应用
您是第19893543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