A Fine-Grained Defect Prediction Method Based on Drift-Immune Graph Neural Networks
Source abstract
Abstract
The primary goal of software defect prediction (SDP) is to pinpoint code modules that are likely to contain defects, thereby enabling software quality assurance teams to strategically allocate their resources and manpower. Wi... | Find, read and cite all the research you need on Tech Science Press
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Industrial Vision Systems and Defect Detection · Image Processing Techniques and Applications · Advanced Measurement and Detection Methods
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (47)
Show source-provided references
1. Research progress of software defect prediction
2. N. Nagappan and T. Ball, “Static analysis tools as early indicators of pre-release defect density,” presented at the 27th Int. Conf. Softw. Eng., St. Louis, MO, USA, May 15–21, 2005.10.1145/1062455.1062558 ↗
3. Evaluating and comparing memory error vulnerability detectors10.1016/j.infsof.2021.106614 ↗
4. D. R. Ibrahim, R. Ghnemat, and A. Hudaib, “Software defect prediction using feature selection and random forest algorithm,” presented at the 2017 Int. Conf. New Trends Comput. Sci., Amman, Jordan, Oct. 252–257, 2017.10.1109/ICTCS.2017.39 ↗
5. D. Gray, D. Bowes, N. Davey, Y. Sun, and B. Christianson, “Using the support vector machine as a classification method for software defect prediction with static code metrics,” presented at the 2009 11th Int. Conf. Eng. Appl. Neural Networks (EANN), London, UK, Aug. 2009. doi: 10.1007/978-3-642-03969-0.10.1007/978-3-642-03969-0_21 ↗
6. Deep learning based software defect prediction10.1016/j.neucom.2019.11.067 ↗
7. S. Wang, J. Wang, J. Nam, and N. Nagappan, “Continuous software bug prediction,” presented at the 2021 15th ACM/IEEE Int. Symp. Empirical Softw. Eng. Meas., Bari, Italy, Oct. 1–12, 2021.10.1145/3475716.3475790 ↗
8. G. G. Cabral, L. L. Minku, E. Shihab, and S. Mujahid, “Class imbalance evolution and verification latency in just-in-time software defect prediction,” presented at the 2019 41st Int. Conf. Softw. Eng., Montreal, QC, Canada, May 2019, pp. 666–676.10.1109/ICSE.2019.00076 ↗
9. Y. Kamei, S. Matsumoto, A. Monden, K. Matsumoto, B. Adams and A. E. Hassan, “Revisiting common bug prediction findings using effort-aware models,” presented at the 2010 IEEE Int. Conf. Softw. Maint., Timisoara, Romania, Sep. 1–10, 2010.10.1109/ICSM.2010.5609530 ↗
10. M. Yan, Y. Fang, D. Lo, X. Xia, and X. Zhang, “File-level defect prediction: Unsupervised vs. supervised models,” presented at the 2017 ACM/IEEE Int. Symp. Empirical Softw. Eng. Meas. (ESEM), Toronto, ON, Canada, Nov. 2017, pp. 344–353.10.1109/ESEM.2017.48 ↗
11. Software defect prediction based on gated hierarchical LSTMs10.1109/TR.2020.3047396 ↗
12. Predicting the number of defects in a new software version
13. C. Pornprasit and C. K. Tantithamthavorn, “JITLine: A simpler, better, faster, finer-grained just-in-time defect prediction,” presented at the 2021 IEEE/ACM 18th Int. Conf. Min. Softw. Repos., Madrid, Spain, May 2021, pp. 369–379.10.1109/MSR52588.2021.00049 ↗
14. Predicting defective lines using a model-agnostic technique10.1109/TSE.2020.3023177 ↗
15. S. Miao, M. Liu, and P. Li, “Interpretable and generalizable graph learning via stochastic attention mechanism,” presented at the 2022 Int. Conf. Mach. Learn., Baltimore, MD, USA, Jul. 2022, pp. 15524–15543.
16. N. Tishby, F. C. Pereira, and W. Bialek, “The information bottleneck method,” 2000, arXiv:0004057.
17. N. Tishby and N. Zaslavsky, “Deep learning and the information bottleneck principle,” presented at the 2015 IEEE Inf. Theory Workshop, Jerusalem, Israel, Apr. 1–5, 2015.10.1109/ITW.2015.7133169 ↗
18. A developer centered bug prediction model10.1109/TSE.2017.2659747 ↗
19. Deep semantic feature learning for software defect prediction10.1109/TSE.2018.2877612 ↗
20. S. Ackerman, O. Raz, M. Zalmanovici, and A. Zlotnick, “Automatically detecting data drift in machine learning classifiers,” 2021, arXiv:2111.05672.
21. S. Amasaki, “On applicability of cross-project defect prediction method for multi-versions projects,” presented at the 2017 13th Int. Conf. Predict. Models Data Anal. Softw. Eng., Toronto, ON, Canada, Sep. 2017, pp. 93–96.10.1145/3127005.3127015 ↗
22. J. Chen et al., “Software visualization and deep transfer learning for effective software defect prediction,” presented at the 2020 ACM/IEEE 42nd Int. Conf. Softw. Eng., Seoul, Republic of Korea, Jul. 2020, pp. 578–589.10.1145/3377811.3380389 ↗
23. Precise learning of source code contextual semantics via hierarchical dependence structure and graph attention networks
24. ACGDP: An augmented code graph-based system for software defect prediction10.1109/TR.2022.3161581 ↗
25. Graph-based feature learning for cross-project software defect prediction
26. Software defect prediction based on deep representation learning of source code from contextual syntax and semantic graph10.1109/TR.2024.3354965 ↗
27. T. Wu, H. Ren, P. Li, and J. Leskovec, “Graph information bottleneck,” presented at the 2020 Adv. Neural Inf. Process. Syst., Vancouver, BC, Canada, Dec. 6–12, 2020.
28. S. Cao, X. Sun, L. Bo, R. Wu, B. Li and C. Tao, “MVD: Memory-related vulnerability detection based on flow-sensitive graph neural networks,” presented at the 44th Int. Conf. Softw. Eng., Pittsburgh, PA, USA, May 2022, pp. 1456–1468.10.1145/3510003.3510219 ↗
29. Q. Le and T. Mikolov, “Distributed representations of sentences and documents,” presented at the Int. Conf. Mach. Learn., Beijing, China, Jun. 2014, pp. 1188–1196.
30. Y. Wu, D. Zou, S. Dou, W. Yang, D. Xu and H. Jin, “VulCNN: An image-inspired scalable vulnerability detection system,” presented at the 44th Int. Conf. Softw. Eng., Pittsburgh, PA, USA, May 2022, pp. 2365–2376.10.1145/3510003.3510229 ↗
31. Centrality in social networks: Conceptual clarification
32. D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” presented at the 2015 3rd Int. Conf. Learn. Represent., San Diego, CA, USA, May 7–9, 2015.
33. DeeplineDP: Towards a deep learning approach for line-level defect prediction10.1109/TSE.2022.3144348 ↗
34. Q. Huang, X. Xia, and D. Lo, “Supervised vs unsupervised models: A holistic look at effort-aware just-in-time defect prediction,” presented at the 2017 IEEE Int. Conf. Softw. Maint. Evol., Shanghai, China, Sep. 2017, pp. 159–170.10.1109/ICSME.2017.51 ↗
35. C. Parnin and A. Orso, “Are automated debugging techniques actually helping programmers?” presented at the 2011 Int. Symp. Softw. Test. Anal., Toronto, ON, Canada, Jul. 2011, pp. 199–209.10.1145/2001420.2001445 ↗
36. Software defect prediction using Bayesian networks10.1007/s10664-012-9218-8 ↗
37. J. Li, P. He, J. Zhu, and M. R. Lyu, “Software defect prediction via convolutional neural network,” presented at the 2017 IEEE Int. Conf. Softw. Qual. Rel. Secur., Prague, Czech Republic, Jul. 2017, pp. 318–328.10.1109/QRS.2017.42 ↗
38. Seml: A semantic LSTM model for software defect prediction10.1109/ACCESS.2019.2925313 ↗
39. Y. Li, S. Wang, T. N. Nguyen, and S. V. Nguyen, “Improving bug detection via context-based code representation learning and attention-based neural networks,” presented at the 2019 ACM on Program. Lang., Athens, Greece, 2019, vol. 3, no. OOPSLA, pp. 1–30.10.1145/3360588 ↗
40. Software defect prediction via LSTM10.1049/iet-sen.2019.0149 ↗
41. The impact of automated parameter optimization on defect prediction models10.1109/TSE.2018.2794977 ↗
42. C. Tantithamthavorn, S. McIntosh, A. E. Hassan, and K. Matsumoto, “Automated parameter optimization of classification techniques for defect prediction models,” presented at the 2016 38th Int. Conf. Softw. Eng., Austin, TX, USA, May 2016, pp. 321–332.10.1145/2884781.2884857 ↗
43. Y. Li, S. Wang, and T. N. Nguyen, “Vulnerability detection with fine-grained interpretations,” presented at the 2021 29th ACM Joint Meet. Eur. Softw. Eng. Conf. Symp. Found. Softw. Eng., Athens, Greece, Aug. 2021, pp. 292–303.10.1145/3468264.3468597 ↗
44. D. Hin, A. Kan, H. Chen, and M. Babar, “LineVD: Statement-level vulnerability detection using graph neural networks,” presented at the 2022 19th Int. Conf. Min. Softw. Repos., Pittsburgh, PA, USA, May 2022, pp. 596–607.10.1145/3524842.3527949 ↗
45. E. Aftandilian, R. Sauciuc, S. Priya, and S. Krishnan, “Building useful program analysis tools using an extensible java compiler,” presented at the 2012 IEEE 12th Int. Working Conf. Source Code Anal. Manipul., Riva del Garda, Italy, Sep. 14–23, 2012.10.1109/SCAM.2012.28 ↗
46. W. He, G. Sun, J. Lu, and X. S. Fang, “Candidate-aware graph contrastive learning for recommendation,” presented at the 2023 46th Int. ACM SIGIR Conf. Res. Dev. Inf. Retr., Taipei, Taiwan, Jul. 2023, pp. 1670–1679.10.1145/3539618.3591647 ↗
47. Y. Jiang, C. Huang, and L. Huang, “Adaptive graph contrastive learning for recommendation,” presented at the 2023 29th ACM SIGKDD Conf. Knowl. Discov. Data Min., Long Beach, CA, USA, Aug. 2023, pp. 4252–4261.10.1145/3580305.3599768 ↗