Skip to main content
SpringerLink
Log in

LineFlowDP: A Deep Learning-Based Two-Phase Approach for Line-Level Defect Prediction

  • Published:
Empirical Software Engineering Aims and scope Submit manuscript

Abstract

Software defect prediction plays a key role in guiding resource allocation for software testing. However, previous defect prediction studies still have some limitations: (1) the granularity of defect prediction is still coarse, so high-risk code statements cannot be accurately located; (2) in fine-grained defect prediction, the semantic and structural information available in a single line of code is limited, and the content of code semantic information is not sufficient to achieve semantic differentiation. To address the above problems, we propose a two-phase line-level defect prediction method based on deep learning called LineFlowDP. We first extract the program dependency graph (PDG) of the source files. The lines of code corresponding to the nodes in the PDG are extended semantically with data flow and control flow information and embedded as nodes, and the model is further trained using an relational graph convolutional network. Finally, a graph interpreter GNNExplainer and a social network analysis method are used to rank the lines of code in the defective file according to risk. On 32 datasets from 9 projects, the experimental results show that LineFlowDP is 13%-404% more cost-effective than four state-of-the-art line-level defect prediction methods. The effectiveness of the flow information extension and code line risk ranking methods was also verified via ablation experiments.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

Data availability

The dataset used in this paper at https://github.com/awsm-research/line-level-defect-prediction. To foster the replication of our study, we will publish the implementation of our LineFlowDP and the baselines at https://github.com/LineFlowDP/LineFlowDP.

References

  • Abdous S, Abdollahzadeh R, Rohban M H (2023) KS-GNNExplainer: Global Model Interpretation Through Instance Explanations On Histopathology images[J]. arXiv preprint arXiv:2304.08240. https://doi.org/10.48550/arXiv.2304.08240

  • Aftandilian E, Sauciuc R, Priya S et al (2012) Building Useful Program Analysis Tools using An Extensible Java Compiler[C]//2012 IEEE 12th International Working Conference on Source Code Analysis and Manipulation. IEEE:14–23. https://doi.org/10.1109/SCAM.2012.28

  • Allamanis M, Brockschmidt M, Khademi M (2017) Learning to Represent Programs with Graphs[J]. arXiv preprint arXiv:1711.00740. https://doi.org/10.48550/arXiv.1711.00740

  • Cao S, Sun X, Bo L, et al. (2022) MVD: Memory-Related Vulnerability Detection Based on Flow-Sensitive Graph Neural Networks[J]. arXiv preprint arXiv:2203.02660. https://doi.org/10.1145/3510003.3510219

  • Chawla NV, Bowyer KW, Hall LO et al (2002) SMOTE: Synthetic Minority Over-sampling Technique[J]. J Artif Intell Res 16:321–357. https://doi.org/10.1613/jair.953

    Article  Google Scholar 

  • Chen X, Zhao YQ, Gu Q, Ni C, Wang Z (2019) Empirical Studies on Multi-objective File-level Software Defect Prediction Method. Ruan Jian Xue Bao/J Software 30(12):3694–3713 (in Chinese). https://doi.org/10.13328/j.cnki.jos.005604

    Article  Google Scholar 

  • Cheng X, Wang H, Hua J et al (2021) Deepwukong: Statically Detecting Software Vulnerabilities using Deep Graph Neural Network[J]. ACM Trans Software Engin Methodology (TOSEM) 30(3):1–33. https://doi.org/10.1145/3436877

    Article  Google Scholar 

  • Cho K, Van Merriënboer B, Gulcehre C, et al. (2014) Learning Phrase Representations Using RNN Encoder-Decoder for Statistical Machine Translation[J]. arXiv preprint arXiv:1406.1078. https://doi.org/10.48550/arXiv.1406.1078

  • Cohen J (2013) Statistical Power Analysis for the Behavioral Sciences[M]. Academic press

    Book  Google Scholar 

  • Dam HK, Tran T, Pham T et al (2018) Automatic Feature Learning for Predicting Vulnerable Software Components[J]. IEEE Trans Softw Eng 47(1):67–85. https://doi.org/10.1109/TSE.2018.2881961

    Article  Google Scholar 

  • Ferrante J , Ottenstein K J , Warren JD . The Program Dependence Graph and Its Use in Optimization[J]. International Symposium on Programming, 6th Colloquium, Toulouse, April 17-19, 1984, Proceedings, 1984. https://doi.org/10.1145/24039.24041

  • Freeman LC (1978) Centrality in Social Networks Conceptual Clarification[J]. Soc Networks 1(3):215–239. https://doi.org/10.1016/0378-8733(78)90021-7

    Article  Google Scholar 

  • Hata H, Mizuno O, Kikuno T (2010) Fault-prone Module Detection using Large-scale Text Features based on Spam Filtering[J]. Empir Softw Eng 15(2):147–165. https://doi.org/10.1007/s10664-009-9117-9

    Article  Google Scholar 

  • Hellendoorn VJ, Devanbu P (2017) Are Deep Neural Networks the Best Choice for Modeling Source Code?[C]//Proceedings of the 2017 11th Joint Meeting on Foundations of. Softw Eng:763–773. https://doi.org/10.1145/3106237.3106290

  • Hin D, Kan A, Chen H, et al. (2022) LineVD: Statement-level Vulnerability Detection using Graph Neural Networks[C]//Proceedings of the 19th International Conference on Mining Software Repositories. 596-607. https://doi.org/10.1145/3524842.3527949

  • Hindle A, Godfrey MW, Holt RC (2008) Reading Beside the Lines: Indentation as A Proxy for Complexity Metric[C]//2008 16th IEEE International Conference on Program Comprehension. IEEE:133–142. https://doi.org/10.1109/ICPC.2008.13

  • Huang Q, Xia X, Lo D (2017) Supervised vs unsupervised Models: A Holistic Look at Effort-aware Just-in-time Defect Prediction[C]//2017 IEEE International Conference on Software Maintenance and Evolution (ICSME). IEEE:159–170. https://doi.org/10.1109/ICSME.2017.51

  • Ieee S (1994) IEEE Standard Classification for Software Anomalies.[J]. IEEE Standard Indus 9(2):1–4

    Google Scholar 

  • Jian L, He P, Zhu J et al (2017) Software Defect Prediction via Convolutional Neural Network[C]//. In: 2017 IEEE International Conference on Software Quality, Reliability and Security (QRS). IEEE. https://doi.org/10.1109/QRS.2017.42

    Chapter  Google Scholar 

  • Kamei Y, Shihab E (2016) Defect Prediction: Accomplishments and Future Challenges. Leaders Tomorrow Symposium: Future Software Engineering FOSE@SANER 2016:33–45. https://doi.org/10.1109/SANER.2016.56

    Article  Google Scholar 

  • Katz L (1953) A New Status Index Derived From Sociometric Analysis[J]. Psychometrika 18(1):39–43. https://doi.org/10.1007/BF02289026

    Article  Google Scholar 

  • Khakhar P, Dubey RK (2022) The Integrity of Machine Learning Algorithms Against Software Defect Prediction[M]//Artificial Intelligence and Machine Learning for EDGE Computing. Academic Press:65–74. https://doi.org/10.1016/B978-0-12-824054-0.00027-7

  • Kingma DP, Adam BJ (2014) A Method for Stochastic Optimization[J]. arXiv preprint arXiv:1412.6980. https://doi.org/10.48550/arXiv.1412.6980

  • Kondo M, German DM, Mizuno O et al (2020) The Impact of Context Metrics on Just-In-Time Defect Prediction[J]. Empir Softw Eng 25:890–939. https://doi.org/10.1007/s10664-019-09736-3

    Article  Google Scholar 

  • Korel B (1987) The Program Dependence Graph in Static Program Testing[J]. Inf Process Lett 24(2):103–108. https://doi.org/10.1016/0020-0190(87)90102-5

    Article  Google Scholar 

  • Le Q, Mikolov T (2014) Distributed Representations of Sentences and Documents[C]//International conference on machine learning. PMLR:1188–1196 10.48550/ arXiv.1405.4053

  • Li XZ, Qing DJ, He YP, Ma HT (2022) Fine-grained Bug Location Method Based on Source Code Extension Information. Ruan Jian Xue Bao/J Software 33(11):4008–4026 (in Chinese). https://doi.org/10.13328/j.cnki.jos.006339

    Article  Google Scholar 

  • Li Y, Wang S, Nguyen T N (2021) Vulnerability Detection with Fine-grained Interpretations[C]//Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering. 292-303. https://doi.org/10.1145/3468264.3468597

  • Lou Y, Zhu Q, Dong J, et al. (2021) Boosting Coverage-based Fault Localization via Graph-based Representation Learning[C]//Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering. 664-676. https://doi.org/10.1145/3468264.3468580

  • Lucic A, Ter Hoeve MA, Tolomei G et al (2022) Cf-gnnexplainer: Counterfactual Explanations for Graph Neural Networks[C]//International Conference on Artificial Intelligence and Statistics. PMLR:4499–4511

  • Lundberg S M, Lee S I (2017) A Unified Approach to Interpreting Model Predictions[J]. Advances in Neural Information Processing Systems, 30.

  • Luo D, Cheng W, Xu D, et al. (2020) Parameterized Explainer for Graph Geural Getwork[J]. Advances in Neural Information Processing Systems, 33: 19620-19631. https://doi.org/10.48550/arXiv.2011.04573

  • Miletić M, Vukušić M, Mauša G, et al. Cross-release Code Churn Impact on Effort-aware Software Defect Prediction[C]//2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO) IEEE, 2018: 1460-1466. https://doi.org/10.23919/MIPRO.2018.8400263

  • Morasca S, Lavazza L (2020) On the Assessment of Software Defect Prediction Models via ROC Curves[J]. Empir Softw Eng 25:3977–4019. https://doi.org/10.1007/s10664-020-09861-4

    Article  Google Scholar 

  • Nguyen HH et al (2022) MANDO: Multi-Level Heterogeneous Graph Embeddings for Fine-Grained Detection of Smart Contract Vulnerabilities. In: 2022 IEEE 9th International Conference on Data Science and Advanced Analytics (DSAA), Shenzhen, China, pp 1–10. https://doi.org/10.1109/DSAA54385.2022.10032337

  • Parnin C, Orso A (2011) Are Automated Debugging Techniques Actually Helping Programmers?[C] //Proceedings of the 2011 international symposium on software testing and analysis. 199-209. https://doi.org/10.1145/2001420.2001445

  • Pornprasit C, Tantithamthavorn C (2022) DeepLineDP: Towards A Deep Learning Approach for Line-Level Defect Prediction[J]. IEEE Trans Softw Eng. https://doi.org/10.1109/TSE.2022.3144348

  • Pornprasit C, Tantithamthavorn CK (2021) JITLine: A Simpler, Better, Faster, Finer-grained Just-in-time Defect Prediction[C]//2021 IEEE/ACM 18th International Conference on Mining Software Repositories (MSR). IEEE:369–379. https://doi.org/10.1109/MSR52588.2021.00049

  • Pradel M, Sen K (2018) Deepbugs: A Learning Approach to Name-based Bug Detection[J]. Proc ACM Prog Lang 2(OOPSLA):1–25. https://doi.org/10.1145/3276517

    Article  Google Scholar 

  • Rahman F, Posnett D, Devanbu P (2012) Recalling the “Imprecision” of Cross-project Defect Prediction[C]//Proceedings of the ACM SIGSOFT 20th International Symposium on the Foundations of Software Engineering. 1-11. https://doi.org/10.1145/2393596.2393669

  • Ray B, Hellendoorn V, Godhane S, et al. (2016) On the “Naturalness” of Buggy Code[C]/ /Proceedings of the 38th International Conference on Software Engineering. 428-439. https://doi.org/10.1145/2884781.2884848

  • Ribeiro MT, Singh S, Guestrin C (2016) “Why Should I Trust You?” Explaining the predictions of any classifier[C]//Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. 1135-1144. https://doi.org/10.1145/2939672.2939778

  • Robbins H, Monro S (1951) A Stochastic Approximation Method[J]. Ann Math Stat 22(3):400–407. https://doi.org/10.1214/aoms/1177729586

    Article  MathSciNet  Google Scholar 

  • Schlichtkrull M, Kipf T N, Bloem P, et al. Modeling Relational Data with Graph Convolutional Networks[C]//The Semantic Web: 15th International Conference, ESWC 2018, Heraklion, Crete, Greece, June 3–7, 2018, Proceedings 15. Springer International Publishing, 2018: 593-607. https://doi.org/10.1007/978-3-319-93417-4_38

  • Sohn J, Kamei Y, McIntosh S et al (2021) Leveraging Fault Localisation to Enhance Defect Prediction[C]//2021 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER). IEEE:284–294. https://doi.org/10.1109/SANER50967.2021.00034

  • Staniak M, Biecek P (2018) Explanations of Model Predictions with Live and BreakDown Packages[J]. arXiv preprint arXiv:1804.01955. 10.48550/ arXiv.1804.01955

  • Tang L, Tao C, Guo H et al (2022) Software Defect Prediction via GCN based on Structural and Context Information[C]//2022 9th International Conference on Dependable Systems and Their Applications (DSA). IEEE:310–319. https://doi.org/10.1109/DSA56465.2022.00049

  • Tantithamthavorn C, Hassan AE, Matsumoto K (2018b) The Impact of Class Rebalancing Techniques on the Performance and Interpretation of Defect Prediction Models[J]. IEEE Trans Softw Eng 46(11):1200–1219. https://doi.org/10.1109/TSE.2018.2876537

    Article  Google Scholar 

  • Tantithamthavorn C, McIntosh S, Hassan A E, et al. (2016a) Automated Parameter Optimization of Classification Techniques for Defect Prediction Models[C]//Proceedings of the 38th International Conference on Software Engineering. 321-332. https://doi.org/10.1145/2884781.2884857

  • Tantithamthavorn C, McIntosh S, Hassan AE et al (2016b) An Empirical Comparison of Model Validation Techniques for Defect Prediction Models[J]. IEEE Trans Softw Eng 43(1):1–18. https://doi.org/10.1109/TSE.2016.2584050

    Article  Google Scholar 

  • Tantithamthavorn C, McIntosh S, Hassan AE et al (2018a) The Impact of Automated Parameter Optimization on Defect Prediction Models[J]. IEEE Trans Softw Eng 45(7):683–711. https://doi.org/10.1109/TSE.2018.2794977

    Article  Google Scholar 

  • Uddin S, Hossain L, Wigand RT (2014) New Direction in Degree Centrality Measure: Towards a Time-variant Approach[J]. Int J Inf Technol Decis Mak 13(04):865–878. https://doi.org/10.1109/SANER.2016.56

    Article  Google Scholar 

  • Vaswani A, Shazeer N, Parmar N, et al. (2017) Attention is All You Need[J]. Advances in neural information processing systems, 30. https://doi.org/10.48550/arXiv.1706.03762

  • Wan Z , Xia X , Hassan A E , et al. (2018a) Perceptions, Expectations, and Challenges in Defect Prediction[J]. IEEE Transactions on Software Engineering, PP:1-1. https://doi.org/10.1109/TSE.2018.2877678

  • Wan Z, Xia X, Hassan AE et al (2018b) Perceptions, Expectations, and Challenges in Defect Prediction[J]. IEEE Trans Softw Eng 46(11):1241–1266. https://doi.org/10.1109/TSE.2018.2877678

    Article  Google Scholar 

  • Wang H, Khoshgoftaar TM, Napolitano A (2010) A comparative Study of Ensemble Feature Selection Techniques for Software Defect Prediction[C]//2010 Ninth International Conference on Machine Learning and Applications. IEEE:135–140. https://doi.org/10.1109/ICMLA.2010.27

  • Wang S, Chollak D, Movshovitz-Attias D, et al. (2016) Bugram: Bug Detection with N-gram Language Models[C]//Proceedings of the 31st IEEE/ACM International Conference on Automated Software Engineering. 708-719. https://doi.org/10.1145/2970276.2970341

  • Wang S, Liu T, Nam J et al (2018) Deep Semantic Feature Learning for Software Defect Prediction[J]. IEEE Trans Softw Eng 46(12):1267–1293. https://doi.org/10.1109/TSE.2018.2877612

    Article  Google Scholar 

  • Wang W, Li G, Ma B et al (2020) Detecting Code Clones with Graph Neural Network and Flow-augmented Abstract Syntax Tree[C]//2020 IEEE 27th International Conference on Software Analysis, Evolution and Reengineering (SANER). IEEE:261–271. https://doi.org/10.1109/SANER48275.2020.9054857

  • Wang Y, Wang W, Joty S, et al. (2021) Codet5: Identifier-aware Unified Pre-trained Encoder-decoder Models for Code Understanding and Generation[J]. arXiv preprint arXiv:2109.00859. https://doi.org/10.48550/arXiv.2305.07922

  • Wattanakriengkrai S, Thongtanunam P, Tantithamthavorn C et al (2020) Predicting Defective Lines Using a Model-Agnostic Technique[J]. IEEE Transac Software Eng. https://doi.org/10.1109/TSE.2020.3023177

  • Wong WE, Debroy V, Surampudi A et al (2010) Recent Catastrophic Accidents. In: Investigating How Software was Responsible[C]// Fourth International Conference on Secure Software Integration & Reliability Improvement. IEEE Computer Society. https://doi.org/10.1109/TSE.2012.56

    Chapter  Google Scholar 

  • Wong WE, Li X, Laplante PA (2017) Be More Familiar with Our Enemies and Pave the Way Forward: A Review of the Roles Bugs Played in Software Failures[J]. J Syst Softw 133:68–94. https://doi.org/10.1016/j.jss.2017.06.069

    Article  Google Scholar 

  • Wu B, Liang X, Zhang SS, Xu R (2022a) Advances and Applications in Graph Neural Network[J]. Chinese J Comput 45(01):35–68(in Chinese with English abstract). https://doi.org/10.11897/SP.J.1016.2022.00035

    Article  Google Scholar 

  • Wu Y, Zou D, Dou S, et al. (2022b) VulCNN: An Image-inspired Scalable Vulnerability Detection System[C]//Proceedings of the 44th International Conference on Software Engineering. 2365-2376. https://doi.org/10.1145/3510003.3510229

  • Wu Z, Pan S, Chen F et al (2020) A Comprehensive Survey on Graph Neural Networks[J]. IEEE Transac Neural Networks Learn Syst 32(1):4–24. https://doi.org/10.1109/TNNLS.2020.2978386

    Article  MathSciNet  Google Scholar 

  • Xiao Y, Jin L, Yang Z et al (2017) The Bayesian Network Based Program Dependence Graph and Its Application to Fault Localization[J]. J Syst Softw:134. https://doi.org/10.1016/j.jss.2017.08.025

  • Xu J, Ai J, Liu J et al (2022) ACGDP: An Augmented Code Graph-Based System for Software Defect Prediction[J]. IEEE Trans Reliab. https://doi.org/10.1109/TR.2022.3161581

  • Yan M, Xia X, Fan Y et al (2020) Just-in-time Defect Identification and Localization: A two-phase Framework[J]. IEEE Trans Softw Eng 48(1):82–101. https://doi.org/10.1109/TSE.2020.2978819

    Article  Google Scholar 

  • Yang F Y, Zeng G D, Zhong F, et al. (2023) Interpretable Software Defect Prediction Incorporating Multiple Rules[C]//2023 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER). IEEE. 940-947.

  • Yang Z, Yang D, Dyer C, et al. (2016) Hierarchical Attention Networks for Document Classification[C]//Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 1480-1489. https://doi.org/10.18653/v1/N16-1174

  • Ying R , Bourgeois D , You J , et al. (2019) GNNExplainer: Generating Explanations for Graph Neural Networks[J]. Advances in Neural Information Processing Systems, 32:9240-9251. https://doi.org/10.48550/arXiv.1903.03894

  • Zeng C, Zhou CY, Lv SK et al (2021) GCN2defect: Graph Convolutional Networks for SMOTETomek-based Software Defect Prediction[C]//2021 IEEE 32nd International Symposium on Software Reliability Engineering (ISSRE). IEEE:69–79. https://doi.org/10.1109/ISSRE52982.2021.00020

  • Zhang Z, Lei Y, Yan M, et al. (2022) Reentrancy Vulnerability Detection and Localization: A Deep Learning Based Two-phase Approach[C]//Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering. 1-13. https://doi.org/10.1145/3551349.3560428

  • Zheng W, Chen TF, Hu MT et al (2023) Hybrid Defect Prediction Model Based on Counterfactual Feature Optimization[J]. Human-Centric Intel Syst:1–15

  • Zhu Q (2020) On the Performance of Matthews Correlation Coefficient (MCC) for Imbalanced Dataset[J]. Pattern Recogn Lett 136:71–80. https://doi.org/10.1016/j.patrec.2020.03.030

    Article  Google Scholar 

Download references

Acknowledgements

This paper is supported by the Jiangxi Provincial Key R&D Program Project(20202BBEL53002).

Funding

This paper is supported by the Jiangxi Provincial Key R&D Program Project(20202BBEL53002).

Author information

Authors and Affiliations

  1. School of Software, Nanchang Hangkong University, Nanchang, 330063, People’s Republic of China

    Fengyu Yang, Fa Zhong, Guangdong Zeng, Peng Xiao & Wei Zheng

  2. Software Testing and Evaluation Center, Nanchang Hangkong University, Nanchang, 330063, People’s Republic of China

    Fengyu Yang, Fa Zhong, Guangdong Zeng, Peng Xiao & Wei Zheng

Authors
  1. Fengyu Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fa Zhong
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Guangdong Zeng
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Peng Xiao
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Wei Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors are contributed equally.

Corresponding author

Correspondence to Fa Zhong.

Ethics declarations

Competing interests

The authors declare they have no conflicts of interest.

Additional information

Communicated by: Leandro L. Minku

Appendix

Appendix

In this section, we present detailed experimental results for the four research questions. Bold and shaded values indicate the method that performs best on each dataset, while Win/Tie/Loss (W/T/L) indicates the number of datasets on which LineFlowDP outperforms/equally performs/worse than the other methods in terms of the metric values. “↑” indicates that the higher the values are, the better the approach, and “↓” indicates that the lower the values are, the better the approach.

1.1 Detailed Results for RQ1

Table 2 Comparison of AUC metric(↑)
Full size table
Table 3 Comparison of BA metric(↑)
Full size table
Table 4 Comparison of MCC metric(↑)
Full size table
Table 5 Comparison of GM metric(↑)
Full size table

1.2 Detailed Results for RQ2

Results for RQ2.1 are shown in Tables 6, 7 and 8.

Table 6 The results of Recall@Top20%LOC(↑)
Full size table
Table 7 The results of Effort@Top20%Recall(↓)
Full size table
Table 8 The results of IFA(↓)
Full size table

Results for RQ2.2 are shown in Tables 9, 10, 11 and 12.

Table 9 The results of Top-1 Accuracy(↑)
Full size table
Table 10 The results of Top-5 Accuracy(↑)
Full size table
Table 11 The results of Top-10 Accuracy(↑)
Full size table
Table 12 The results of Top-20 Accuracy(↑)
Full size table

1.3 Detailed Results for RQ3

Results for RQ3.1 are shown in Tables 13, 14 and 15.

Table 13 Comparison table of Recall@Top 20% LOC ablation experiment (↑)
Full size table
Table 14 Comparison table of Effort@Top20%Recall ablation experiment (↓)
Full size table
Table 15 Comparison table of IFA ablation experiment (↓)
Full size table

Results for RQ3.2 are shown in Tables 16, 17 and 18.

Table 16 Comparison table of Recall@Top20%LOC ablation experiment (↑)
Full size table
Table 17 Comparison table of Effort@Top20%Recall ablation experiment (↓)
Full size table
Table 18 Comparison table of Initial False Alarms ablation experiment (↓)
Full size table

1.4 Detailed Results for RQ4

Table 19 Time Efficiency
Full size table

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yang, F., Zhong, F., Zeng, G. et al. LineFlowDP: A Deep Learning-Based Two-Phase Approach for Line-Level Defect Prediction. Empir Software Eng 29, 50 (2024). https://doi.org/10.1007/s10664-023-10439-z

Download citation

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10664-023-10439-z

Keywords

Search

Navigation