research-article

node2defect: using network embedding to improve software defect prediction

Authors:

Qinghua ZhengAuthors Info & Claims

ASE '18: Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering

Pages 844 - 849

https://doi.org/10.1145/3238147.3240469

Published: 03 September 2018 Publication History

Abstract

Network measures have been proved to be useful in predicting software defects. Leveraging the dependency relationships between software modules, network measures can capture various structural features of software systems. However, existing studies have relied on user-defined network measures (e.g., degree statistics or centrality metrics), which are inflexible and require high computation cost, to describe the structural features. In this paper, we propose a new method called node2defect which uses a newly proposed network embedding technique, node2vec, to automatically learn to encode dependency network structure into low-dimensional vector spaces to improve software defect prediction. Specifically, we firstly construct a program's Class Dependency Network. Then node2vec is used to automatically learn structural features of the network. After that, we combine the learned features with traditional software engineering features, for accurate defect prediction. We evaluate our method on 15 open source programs. The experimental results show that in average, node2defect improves the state-of-the-art approach by 9.15% in terms of F-measure.

References

[1]

Ethem Alpaydin. 2004. Introduction to Machine Learning. MIT Press,. 28 pages.

Digital Library

[2]

Gareth Baxter, Marcus Frean, James Noble, Mark Rickerby, Hayden Smith, Matt Visser, Hayden Melton, and Ewan Tempero. 2006. Understanding the shape of Java software. Acm Sigplan Notices 41, 10 (2006), 397–412.

Digital Library

[3]

Ulrik Brandes. 2001. A faster algorithm for betweenness centrality. Journal of mathematical sociology 25, 2 (2001), 163–177.

[4]

Leo Breiman. 2001. Random Forests. Machine Learning 45, 1 (2001), 5–32.

Digital Library

[5]

Hongyun Cai, Vincent W Zheng, and Kevin Chang. 2018. A comprehensive survey of graph embedding: problems, techniques and applications. IEEE Transactions on Knowledge and Data Engineering (2018).

[6]

Lin Chen, Wanwangying Ma, Yuming Zhou, Lei Xu, Ziyuan Wang, Zhifei Chen, and Baowen Xu. 2016. Empirical analysis of network measures for predicting high severity software faults. Science China Information Sciences 59, 12 (2016), 122901.

[7]

Shyam R Chidamber and Chris F Kemerer. 1994. A metrics suite for object oriented design. IEEE Transactions on software engineering 20, 6 (1994), 476–493.

Digital Library

[8]

Giulio Concas, Michele Marchesi, Cristina Monni, Matteo Orrù, and Roberto Tonelli. 2017. Software Quality and Community Structure in Java Software Networks. International Journal of Software Engineering and Knowledge Engineering 27, 07 (2017), 1063–1096.

[9]

Sergio Di Martino, Filomena Ferrucci, Carmine Gravino, and Federica Sarro. 2011. A genetic algorithm to configure support vector machines for predicting fault-prone components. In International Conference on Product Focused Software Process Improvement. Springer, 247–261.

Digital Library

[10]

Palash Goyal and Emilio Ferrara. 2018. Graph embedding techniques, applications, and performance: A survey. Knowledge-Based Systems 151 (2018), 78–94.

[11]

Aditya Grover and Jure Leskovec. 2016. node2vec: Scalable Feature Learning for Networks. In Acm Sigkdd International Conference on Knowledge Discovery & Data Mining. 855–864.

Digital Library

[12]

William L Hamilton, Rex Ying, and Jure Leskovec. 2017. Representation Learning on Graphs: Methods and Applications. arXiv preprint arXiv:1709.05584 (2017).

[13]

Marian Jureczko and Lech Madeyski. 2010. Towards identifying software project clusters with regard to defect prediction. In Proceedings of the 6th International Conference on Predictive Models in Software Engineering. ACM, 9.

Digital Library

[14]

Yihao Li. 2017. Applying Social Network Analysis to Software Fault-Proneness Prediction. Ph.D. Dissertation. University of Texas at Dallas.

[15]

Panagiotis Louridas, Diomidis Spinellis, and Vasileios Vlachos. 2008. Power laws in software. ACM Transactions on Software Engineering and Methodology (TOSEM) 18, 1 (2008), 2.

Digital Library

[16]

Wanwangying Ma, Lin Chen, Yibiao Yang, Yuming Zhou, and Baowen Xu. 2016. Empirical analysis of network measures for effort-aware fault-proneness prediction. Information and Software Technology 69 (2016), 50–70.

Digital Library

[17]

Tim Menzies, Rahul Krishna, and David Pryor. 2015. The Promise Repository of Empirical Software Engineering Data. http://openscience.us/repo. North Carolina State University, Department of Computer Science.

[18]

Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013).

[19]

Thanh HD Nguyen, Bram Adams, and Ahmed E Hassan. 2010. Studying the impact of dependency network measures on software quality. In Software Maintenance (ICSM), 2010 IEEE International Conference on. IEEE, 1–10.

Digital Library

[20]

Dario Di Nucci, Fabio Palomba, Rocco Oliveto, and Andrea De Lucia. 2017. Dynamic Selection of Classifiers in Bug Prediction: An Adaptive Method. IEEE Transactions on Emerging Topics in Computational Intelligence 1, 3 (2017), 202– 212.

[21]

Rahul Premraj and Kim Herzig. 2011. Network Versus Code Metrics to Predict Defects: A Replication Study. In International Symposium on Empirical Software Engineering and Measurement. 215–224.

Digital Library

[22]

Thomas Shippey, Tracy Hall, Steve Counsell, and David Bowes. 2016. So You Need More Method Level Datasets for Your Software Defect Prediction?: Voilà!. In Proceedings of the 10th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement. ACM, 12.

Digital Library

[23]

Lovro Šubelj and Marko Bajec. 2011. Community structure of complex software systems: Analysis and applications. Physica A: Statistical Mechanics and its Applications 390, 16 (2011), 2968–2975.

[24]

Ayşe Tosun, Burak Turhan, and Ayşe Bener. 2009. Validation of network measures as indicators of defective modules in software systems. In Proceedings of the 5th international conference on predictor models in software engineering. ACM, 5.

Digital Library

[25]

Ian H Witten, Eibe Frank, Mark A Hall, and Christopher J Pal. 2016. Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann.

Digital Library

Cited By

Zou YWang HLv HZhao STian H(2024)A Cross-Project Defect Prediction Approach Based on Code Semantics and Cross-Version Structural InformationInternational Journal of Software Engineering and Knowledge Engineering10.1142/S021819402450016534:07(1135-1171)Online publication date: 27-May-2024
https://doi.org/10.1142/S0218194024500165
Qiu SHuang MLiang YPeng CYuan Y(2024)Code Multiview Hypergraph Representation Learning for Software Defect PredictionIEEE Transactions on Reliability10.1109/TR.2024.339341573:4(1863-1876)Online publication date: Dec-2024
https://doi.org/10.1109/TR.2024.3393415
Wang QLi ZLiang HPan XLi HLi TLi XLi CGuo S(2024)Graph Confident Learning for Software Vulnerability DetectionEngineering Applications of Artificial Intelligence10.1016/j.engappai.2024.108296133:PCOnline publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1016/j.engappai.2024.108296
Show More Cited By

Index Terms

node2defect: using network embedding to improve software defect prediction
1. Software and its engineering
  1. Software creation and management
    1. Software development process management
      1. Risk management
    2. Software post-development issues
      1. Maintaining software

Recommendations

Modeling Structural Model for Defect Categories Based On Software Metrics for Categorical Defect Prediction
ICCCT '15: Proceedings of the Sixth International Conference on Computer and Communication Technology 2015

Software Defect prediction is the pre-eminent area of software engineering which has witnessed huge importance over last decades. The identification of defects in the early stages of software development not only improve the quality of the software ...
Identifying Effective Software Metrics for Categorical Defect Prediction Using Structural Equation Modeling
WCI '15: Proceedings of the Third International Symposium on Women in Computing and Informatics

Software Defect prediction is the pre-eminent area of software engineering which has witnessed huge importance over last decades. The identification of defects in the early stages of software development improve the quality of the software system and ...
A fuzzy logic based approach for phase-wise software defects prediction using software metrics

Display Omitted We present a fuzzy logic based approach for phase-wise software defects prediction.Top-most reliability relevant software metrics of SDLC are considered.The proposed model is validated on 20 real software projects.The sensitivity ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

ASE '18: Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering

September 2018

955 pages

ISBN:9781450359375

DOI:10.1145/3238147

General Chair:
Marianne Huchard
University of Montpellier, France
,
Program Chairs:
Christian Kästner
Carnegie Mellon University, USA
,
Gordon Fraser
University of Passau, Germany

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

SIGAI: ACM Special Interest Group on Artificial Intelligence
CNRS: Centre National De La Rechercue Scientifique
SIGSOFT: ACM Special Interest Group on Software Engineering
IEEE-CS: Computer Society

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 03 September 2018

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

National Natural Science Foundation of China

Conference

ASE '18

Sponsor:

SIGAI
CNRS
SIGSOFT
IEEE-CS

ASE '18: 33rd ACM/IEEE International Conference on Automated Software Engineering

September 3 - 7, 2018

Montpellier, France

Acceptance Rates

Overall Acceptance Rate 82 of 337 submissions, 24%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

19
Total Citations
View Citations
524
Total Downloads

Downloads (Last 12 months)32
Downloads (Last 6 weeks)1

Reflects downloads up to 26 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Zou YWang HLv HZhao STian H(2024)A Cross-Project Defect Prediction Approach Based on Code Semantics and Cross-Version Structural InformationInternational Journal of Software Engineering and Knowledge Engineering10.1142/S021819402450016534:07(1135-1171)Online publication date: 27-May-2024
https://doi.org/10.1142/S0218194024500165
Qiu SHuang MLiang YPeng CYuan Y(2024)Code Multiview Hypergraph Representation Learning for Software Defect PredictionIEEE Transactions on Reliability10.1109/TR.2024.339341573:4(1863-1876)Online publication date: Dec-2024
https://doi.org/10.1109/TR.2024.3393415
Wang QLi ZLiang HPan XLi HLi TLi XLi CGuo S(2024)Graph Confident Learning for Software Vulnerability DetectionEngineering Applications of Artificial Intelligence10.1016/j.engappai.2024.108296133:PCOnline publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1016/j.engappai.2024.108296
Zhang MHe P(2024)Graph Neural Network for Critical Class Identification in Software SystemAlgorithms and Architectures for Parallel Processing10.1007/978-981-97-0798-0_11(174-190)Online publication date: 1-Mar-2024
https://doi.org/10.1007/978-981-97-0798-0_11
MON KKONDO MCHOI EMIZUNO O(2023)Commit-Based Class-Level Defect Prediction for Python ProjectsIEICE Transactions on Information and Systems10.1587/transinf.2022MPP0003E106.D:2(157-165)Online publication date: 1-Feb-2023
https://doi.org/10.1587/transinf.2022MPP0003
Zhu YGao YYu Q(2023)An Empirical Study on Model-Agnostic Techniques for Source Code-Based Defect PredictionInternational Journal of Software Engineering and Knowledge Engineering10.1142/S021819402350057234:03(511-544)Online publication date: 4-Nov-2023
https://doi.org/10.1142/S0218194023500572
Zhang ZLiu LChang JWang LLiao L(2023)Commit Classification via Diff-Code GCN based on System Dependency Graph2023 IEEE 23rd International Conference on Software Quality, Reliability, and Security (QRS)10.1109/QRS60937.2023.00053(476-487)Online publication date: 22-Oct-2023
https://doi.org/10.1109/QRS60937.2023.00053
Liubchenko V(2023)Applications of Machine Learning in Software Defect Prediction: A Literature Review2023 IEEE 18th International Conference on Computer Science and Information Technologies (CSIT)10.1109/CSIT61576.2023.10324288(1-4)Online publication date: 19-Oct-2023
https://doi.org/10.1109/CSIT61576.2023.10324288
Yang FXu HXiao PZhong FZeng G(2023)A Method-Level Defect Prediction Approach Based on Structural Features of Method-Calling NetworkIEEE Access10.1109/ACCESS.2023.323926611(7933-7946)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3239266
Du XWang TWang LPan WChai CXu XJiang BWang J(2022)CoreBug: Improving Effort-Aware Bug Prediction in Software Systems Using Generalized k-Core Decomposition in Class Dependency NetworksAxioms10.3390/axioms1105020511:5(205)Online publication date: 27-Apr-2022
https://doi.org/10.3390/axioms11050205
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents