More Web Proxy on the site http://driver.im/

research-article

A novel neural source code representation based on abstract syntax tree

Authors:

Xudong LiuAuthors Info & Claims

ICSE '19: Proceedings of the 41st International Conference on Software Engineering

Pages 783 - 794

https://doi.org/10.1109/ICSE.2019.00086

Published: 25 May 2019 Publication History

Abstract

Exploiting machine learning techniques for analyzing programs has attracted much attention. One key problem is how to represent code fragments well for follow-up analysis. Traditional information retrieval based methods often treat programs as natural language texts, which could miss important semantic information of source code. Recently, state-of-the-art studies demonstrate that abstract syntax tree (AST) based neural models can better represent source code. However, the sizes of ASTs are usually large and the existing models are prone to the long-term dependency problem. In this paper, we propose a novel AST-based Neural Network (ASTNN) for source code representation. Unlike existing models that work on entire ASTs, ASTNN splits each large AST into a sequence of small statement trees, and encodes the statement trees to vectors by capturing the lexical and syntactical knowledge of statements. Based on the sequence of statement vectors, a bidirectional RNN model is used to leverage the naturalness of statements and finally produce the vector representation of a code fragment. We have applied our neural network based source code representation method to two common program comprehension tasks: source code classification and code clone detection. Experimental results on the two tasks indicate that our model is superior to state-of-the-art approaches.

References

[1]

G. Frantzeskou, S. MacDonell, E. Stamatatos, and S. Gritzalis, "Examining the significance of high-level programming features in source code author classification," Journal of Systems and Software, vol. 81, no. 3, pp. 447--460, 2008.

Digital Library

[2]

L. Mou, G. Li, L. Zhang, T. Wang, and Z. Jin, "Convolutional neural networks over tree structures for programming language processing," in AAAI, vol. 2, no. 3, 2016, p. 4.

Digital Library

[3]

T. Kamiya, S. Kusumoto, and K. Inoue, "CCFinder: a multilinguistic token-based code clone detection system for large scale source code," IEEE Transactions on Software Engineering, vol. 28, no. 7, pp. 654--670, 2002.

Digital Library

[4]

H. Sajnani, V. Saini, J. Svajlenko, C. K. Roy, and C. V. Lopes, "SourcererCC: Scaling code clone detection to big-code," in Software Engineering (ICSE), 2016 IEEE/ACM 38th International Conference on. IEEE, 2016, pp. 1157--1168.

Digital Library

[5]

M. White, M. Tufano, C. Vendome, and D. Poshyvanyk, "Deep learning code fragments for code clone detection," in Proceedings of the 31st IEEE/ACM International Conference on Automated Software Engineering. ACM, 2016, pp. 87--98.

Digital Library

[6]

H.-H. Wei and M. Li, "Supervised deep features for software functional clone detection by exploiting lexical and syntactical information in source code," in Proceedings of the 26th International Joint Conference on Artificial Intelligence. AAAI Press, 2017, pp. 3034--3040.

Digital Library

[7]

M. DAmbros, M. Lanza, and R. Robbes, "Evaluating defect prediction approaches: a benchmark and an extensive comparison," Empirical Software Engineering, vol. 17, no. 4--5, pp. 531--577, 2012.

Digital Library

[8]

C. Tantithamthavorn, S. McIntosh, A. E. Hassan, and K. Matsumoto, "An empirical comparison of model validation techniques for defect prediction models," IEEE Transactions on Software Engineering, vol. 43, no. 1, pp. 1--18, 2017.

Digital Library

[9]

S. Haiduc, J. Aponte, and A. Marcus, "Supporting program comprehension with source code summarization," in Proceedings of the 32nd ACM/IEEE International Conference on Software Engineering-Volume 2. ACM, 2010, pp. 223--226.

Digital Library

[10]

S. Jiang, A. Armaly, and C. McMillan, "Automatically generating commit messages from diffs using neural machine translation," in Proceedings of the 32nd IEEE/ACM International Conference on Automated Software Engineering. IEEE Press, 2017, pp. 135--146.

Digital Library

[11]

J. Zhou, H. Zhang, and D. Lo, "Where should the bugs be fixed? more accurate information retrieval-based bug localization based on bug reports," in 2012 34th International Conference on Software Engineering (ICSE), June 2012, pp. 14--24.

Digital Library

[12]

S. Deerwester, S. T. Dumais, G. W. Furnas, T. K. Landauer, and R. Harshman, "Indexing by latent semantic analysis," Journal of the American society for information science, vol. 41, no. 6, p. 391, 1990.

[13]

D. M. Blei, A. Y. Ng, and M. I. Jordan, "Latent dirichlet allocation," Journal of machine Learning research, vol. 3, no. Jan, pp. 993--1022, 2003.

Digital Library

[14]

R. Tairas and J. Gray, "An information retrieval process to aid in the analysis of code clones," Empirical Software Engineering, vol. 14, no. 1, pp. 33--56, 2009.

Digital Library

[15]

Y. Liu, D. Poshyvanyk, R. Ferenc, T. Gyimóthy, and N. Chrisochoides, "Modeling class cohesion as mixtures of latent topics," in Software Maintenance, 2009. ICSM 2009. IEEE International Conference on. IEEE, 2009, pp. 233--242.

[16]

A. De Lucia, M. Di Penta, R. Oliveto, A. Panichella, and S. Panichella, "Using IR methods for labeling source code artifacts: Is it worthwhile?" in Program Comprehension (ICPC), 2012 IEEE 20th International Conference on. IEEE, 2012, pp. 193--202.

[17]

A. Panichella, B. Dit, R. Oliveto, M. Di Penta, D. Poshynanyk, and A. De Lucia, "How to effectively use topic models for software engineering tasks? an approach based on genetic algorithms," in Software Engineering (ICSE), 2013 35th International Conference on. IEEE, 2013, pp. 522--531.

Digital Library

[18]

J. F. Pane, B. A. Myers et al., "Studying the language and structure in non-programmers solutions to programming problems," International Journal of Human-Computer Studies, vol. 54, no. 2, pp. 237--264, 2001.

Digital Library

[19]

Y. Bengio, P. Simard, and P. Frasconi, "Learning long-term dependencies with gradient descent is difficult," IEEE transactions on neural networks, vol. 5, no. 2, pp. 157--166, 1994.

Digital Library

[20]

S. Hochreiter, "The vanishing gradient problem during learning recurrent neural nets and problem solutions," Int. J. Uncertain. Fuzziness Knowl.-Based Syst., vol. 6, no. 2, pp. 107--116, Apr. 1998. {Online}. Available

Digital Library

[21]

P. Le and W. H. Zuidema, "Quantifying the vanishing gradient and long distance dependency problem in recursive neural networks and recursive LSTMs," in Proceedings of the 1st Workshop on Representation Learning for NLP, Berlin, Germany, 2016, p. 8793.

[22]

S.-Y. Cho, Z. Chi, W.-C. Siu, and A. C. Tsoi, "An improved algorithm for learning long-term dependency problems in adaptive processing of data structures," IEEE Transactions on Neural Networks, vol. 14, no. 4, pp. 781--793, 2003.

Digital Library

[23]

X. Zhu, P. Sobhani, and H. Guo, "Long short-term memory over recursive structures," in Proceedings of the 32Nd International Conference on International Conference on Machine Learning - Volume 37, ser. ICML'15. JMLR.org, 2015, pp. 1604--1612. {Online}. Available: http://dl.acm.org/citation.cfm?id=3045118.3045289

Digital Library

[24]

M. Ou, P. Cui, J. Pei, Z. Zhang, and W. Zhu, "Asymmetric transitivity preserving graph embedding," in Proceedings of the 22Nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ser. KDD '16. New York, NY, USA: ACM, 2016, pp. 1105--1114. {Online}. Available

Digital Library

[25]

M. Allamanis, M. Brockschmidt, and M. Khademi, "Learning to represent programs with graphs," in International Conference on Learning Representations, 2018. {Online}. Available: https://openreview.net/forum?id=BJOFETxR-

[26]

G. B. M. D. P. M. W. Michele Tufano, Cody Watson and D. Poshyvanyk, "Deep learning similarities from different representations of source code," in 15th International Conference on Mining Software Repositories, 2018.

Digital Library

[27]

J. Ferrante, K. J. Ottenstein, and J. D. Warren, "The program dependence graph and its use in optimization," ACM Trans. Program. Lang. Syst., vol. 9, no. 3, pp. 319--349, Jul. 1987. {Online}. Available

Digital Library

[28]

E. M. Myers, "A precise inter-procedural data flow algorithm," in Proceedings of the 8th ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages, ser. POPL '81. New York, NY, USA: ACM, 1981, pp. 219--230. {Online}. Available

Digital Library

[29]

"Llvms analysis and transform passes," https://llvm.org/docs/Passes.html, accessed August 3, 2018.

[30]

J. Gosling, B. Joy, G. L. Steele, G. Bracha, and A. Buckley, The Java Language Specification, Java SE 8 Edition, 1st ed. Addison-Wesley Professional, 2014.

Digital Library

[31]

T. Mikolov, M. Karafiát, L. Burget, J. Černockỳ, and S. Khudanpur, "Recurrent neural network based language model," in Eleventh Annual Conference of the International Speech Communication Association, 2010.

[32]

A. Hindle, E. T. Barr, Z. Su, M. Gabel, and P. Devanbu, "On the naturalness of software," in Software Engineering (ICSE), 2012 34th International Conference on. IEEE, 2012, pp. 837--847.

Digital Library

[33]

B. Ray, V. Hellendoorn, S. Godhane, Z. Tu, A. Bacchelli, and P. Devanbu, "On the "naturalness" of buggy code," in Proceedings of the 38th International Conference on Software Engineering, ser. ICSE '16. New York, NY, USA: ACM, 2016, pp. 428--439. {Online}. Available

Digital Library

[34]

D. Bahdanau, K. Cho, and Y. Bengio, "Neural machine translation by jointly learning to align and translate," arXiv preprint arXiv:1409.0473, 2014.

[35]

D. Tang, B. Qin, and T. Liu, "Document modeling with gated recurrent neural network for sentiment classification," in Proceedings of the 2015 conference on empirical methods in natural language processing, 2015, pp. 1422--1432.

[36]

I. D. Baxter, A. Yahin, L. Moura, M. Sant' Anna, and L. Bier, "Clone detection using abstract syntax trees," in Software Maintenance, 1998. Proceedings., International Conference on. IEEE, 1998, pp. 368--377.

Digital Library

[37]

S. Paul and A. Prakash, "A framework for source code search using program patterns," IEEE Transactions on Software Engineering, vol. 20, no. 6, pp. 463--475, 1994.

Digital Library

[38]

W. Weimer, T. Nguyen, C. Le Goues, and S. Forrest, "Automatically finding patches using genetic programming," in Proceedings of the 31st International Conference on Software Engineering. IEEE Computer Society, 2009, pp. 364--374.

Digital Library

[39]

J.-R. Falleri, F. Morandat, X. Blanc, M. Martinez, and M. Monperrus, "Fine-grained and accurate source code differencing," in Proceedings of the 29th ACM/IEEE international conference on Automated software engineering. ACM, 2014, pp. 313--324.

Digital Library

[40]

R. Socher, C. C. Lin, C. Manning, and A. Y. Ng, "Parsing natural scenes and natural language with recursive neural networks," in Proceedings of the 28th international conference on machine learning (ICML-11), 2011, pp. 129--136.

Digital Library

[41]

K. S. Tai, R. Socher, and C. D. Manning, "Improved semantic representations from tree-structured long short-term memory networks," in Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), vol. 1, 2015, pp. 1556--1566.

[42]

T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, and J. Dean, "Distributed representations of words and phrases and their compositionality," in Advances in neural information processing systems, 2013, pp. 3111--3119.

Digital Library

[43]

X. Gu, H. Zhang, D. Zhang, and S. Kim, "Deep API learning," in Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering. ACM, 2016, pp. 631--642.

Digital Library

[44]

S. Kawaguchi, P. K. Garg, M. Matsushita, and K. Inoue, "Mudablue: An automatic categorization system for open source repositories," Journal of Systems and Software, vol. 79, no. 7, pp. 939--953, 2006.

Digital Library

[45]

M. Linares-Vásquez, C. McMillan, D. Poshyvanyk, and M. Grechanik, "On using machine learning to automatically classify software applications into domain categories," Empirical Software Engineering, vol. 19, no. 3, pp. 582--618, 2014.

Digital Library

[46]

D. P. Kingma and J. Ba, "Adam: A method for stochastic optimization," arXiv preprint arXiv:1412.6980, 2014.

[47]

J. Svajlenko, J. F. Islam, I. Keivanloo, C. K. Roy, and M. M. Mia, "Towards a big data curated benchmark of inter-project code clones," in Software Maintenance and Evolution (ICSME), 2014 IEEE International Conference on. IEEE, 2014, pp. 476--480.

Digital Library

[48]

A. Charpentier, J.-R. Falleri, D. Lo, and L. Réveillère, "An empirical assessment of bellon's clone benchmark," in Proceedings of the 19th International Conference on Evaluation and Assessment in Software Engineering. ACM, 2015, p. 20.

Digital Library

[49]

P. Accioly, P. Borba, and G. Cavalcanti, "Understanding semi-structured merge conflict characteristics in open-source java projects," Empirical Software Engineering, pp. 1--35, 2017.

Digital Library

[50]

Y. Kim, "Convolutional neural networks for sentence classification," in Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2014, pp. 1746--1751.

[51]

W. Zaremba and I. Sutskever, "Learning to execute," arXiv preprint arXiv:1410.4615, 2014.

[52]

X. Huo and M. Li, "Enhancing the unified features to locate buggy files by exploiting the sequential nature of source code," in Proceedings of the 26th International Joint Conference on Artificial Intelligence, ser. IJCAI'17. AAAI Press, 2017, pp. 1909--1915. {Online}. Available: http://dl.acm.org/citation.cfm?id=3172077.3172153

Digital Library

[53]

Y. Li, D. Tarlow, M. Brockschmidt, and R. Zemel, "Gated graph sequence neural networks," arXiv preprint arXiv:1511.05493, 2015.

[54]

C. K. Roy and J. R. Cordy, "A survey on software clone detection research," Queens School of Computing TR, vol. 541, no. 115, pp. 64--68, 2007.

[55]

L. Jiang, G. Misherghi, Z. Su, and S. Glondu, "DECKARD: Scalable and accurate tree-based detection of code clones," in Proceedings of the 29th International Conference on Software Engineering, ser. ICSE '07. Washington, DC, USA: IEEE Computer Society, 2007, pp. 96--105. {Online}. Available

Digital Library

[56]

J. I. Maletic and A. Marcus, "Supporting program comprehension using semantic and structural information," in Proceedings of the 23rd International Conference on Software Engineering. IEEE Computer Society, 2001, pp. 103--112.

Digital Library

[57]

M. Allamanis, E. T. Barr, P. Devanbu, and C. Sutton, "A survey of machine learning for big code and naturalness," arXiv preprint arXiv:1709.06182, 2017.

[58]

V. Raychev, M. Vechev, and E. Yahav, "Code completion with statistical language models," in Acm Sigplan Notices, vol. 49, no. 6. ACM, 2014, pp. 419--428.

Digital Library

[59]

M. Allamanis, E. T. Barr, C. Bird, and C. Sutton, "Suggesting accurate method and class names," in Proceedings of the 2015 10th Joint Meeting on Foundations of Software Engineering. ACM, 2015, pp. 38--49.

Digital Library

[60]

S. Wang, T. Liu, and L. Tan, "Automatically learning semantic features for defect prediction," in Proceedings of the 38th International Conference on Software Engineering. ACM, 2016, pp. 297--308.

Digital Library

[61]

M. Pradel and K. Sen, "DeepBugs: A learning approach to name-based bug detection," Proc. ACM Program. Lang., vol. 2, no. OOPSLA, pp. 147:1--147:25, Oct. 2018. {Online}. Available

Digital Library

[62]

G. Zhao and J. Huang, "DeepSim: Deep learning code functional similarity," in Proceedings of the 2018 26th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering, ser. ESEC/FSE 2018. New York, NY, USA: ACM, 2018, pp. 141--151. {Online}. Available

Digital Library

[63]

A. N. Lam, A. T. Nguyen, H. A. Nguyen, and T. N. Nguyen, "Combining deep learning with information retrieval to localize buggy files for bug reports (n)," in Automated Software Engineering (ASE), 2015 30th IEEE/ACM International Conference on. IEEE, 2015, pp. 476--481.

Digital Library

[64]

B. Xu, D. Ye, Z. Xing, X. Xia, G. Chen, and S. Li, "Predicting semantically linkable knowledge in developer online forums via convolutional neural network," in Proceedings of the 31st IEEE/ACM International Conference on Automated Software Engineering. ACM, 2016, pp. 51--62.

Digital Library

[65]

J. Guo, J. Cheng, and J. Cleland-Huang, "Semantically enhanced software traceability using deep learning techniques," in Proceedings of the 39th International Conference on Software Engineering. IEEE Press, 2017, pp. 3--14.

Digital Library

[66]

X. Gu, H. Zhang, and S. Kim, "Deep code search," in Proceedings of the 2018 40th International Conference on Software Engineering (ICSE 2018). ACM, 2018.

Digital Library

Cited By

Yang QYu DWang SXu YChen XChen JHu B(2025)Enhancing structural knowledge in code smell identificationExpert Systems with Applications: An International Journal10.1016/j.eswa.2024.125725263:COnline publication date: 5-Mar-2025
https://dl.acm.org/doi/10.1016/j.eswa.2024.125725
Liang CWei QDu JWang YJiang Z(2025)Survey of source code vulnerability analysis based on deep learningComputers and Security10.1016/j.cose.2024.104098148:COnline publication date: 1-Jan-2025
https://dl.acm.org/doi/10.1016/j.cose.2024.104098
Feng SWu YXue WPan SZou DLiu YJin HBalzarotti DXu W(2024)FIREProceedings of the 33rd USENIX Conference on Security Symposium10.5555/3698900.3699005(1867-1884)Online publication date: 14-Aug-2024
https://dl.acm.org/doi/10.5555/3698900.3699005
Show More Cited By

Recommendations

Transformer-based networks over tree structures for code classification
Abstract
In software engineering (SE), code classification and related tasks, such as code clone detection are still challenging problems. Due to the elusive syntax and complicated semantics in software programs, existing traditional SE approaches still ...
On the impact of multiple source code representations on software engineering tasks — An empirical study
Abstract
Efficiently representing source code is crucial for various software engineering tasks such as code classification and clone detection. Existing approaches primarily use Abstract Syntax Tree (AST), and only a few focus on semantic graphs such as ...
Highlights
- Emphasizes and demonstrates the idea of integrating multiple source code representations for software engineering tasks.
- Leverages the advantages of semantic code representations (CFG and PDG) along with syntactic code representations (...
Deep learning with class-level abstract syntax tree and code histories for detecting code modification requirements
Abstract
Improving code quality is one of the most significant issues in the software industry. Deep learning is an emerging area of research for detecting code smells and addressing refactoring requirements. The aim of this study is to develop a deep ...
Graphical abstract

Display Omitted
Highlights
- A new code embedding method based on adding class level to the abstract syntax tree.
- Detection of the refactoring requirements with code history and deep learning.
- An experimental code quality dataset that provides code and the ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

ICSE '19: Proceedings of the 41st International Conference on Software Engineering

May 2019

1318 pages

General Chair:
Joanne M. Atlee
University of Waterloo, Canada
,
Program Chairs:
Tevfik Bultan
University of California, Santa Barbara
,
Jon Whittle
Monash University, Australia

Sponsors

SIGSOFT: ACM Special Interest Group on Software Engineering
IEEE-CS: Computer Society

Publisher

IEEE Press

Publication History

Published: 25 May 2019

Check for updates

Badges

Artifacts Available

Author Tags

Qualifiers

Research-article

Conference

ICSE '19

Sponsor:

SIGSOFT
IEEE-CS

ICSE '19: 41st International Conference on Software Engineering

May 25 - 31, 2019

Quebec, Montreal, Canada

Acceptance Rates

Overall Acceptance Rate 276 of 1,856 submissions, 15%

Upcoming Conference

ICSE 2025

2025 IEEE/ACM 46th International Conference on Software Engineering

April 26 - May 3, 2025

Ottawa , ON , Canada

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

138
Total Citations
View Citations
931
Total Downloads

Downloads (Last 12 months)39
Downloads (Last 6 weeks)6

Reflects downloads up to 01 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Yang QYu DWang SXu YChen XChen JHu B(2025)Enhancing structural knowledge in code smell identificationExpert Systems with Applications: An International Journal10.1016/j.eswa.2024.125725263:COnline publication date: 5-Mar-2025
https://dl.acm.org/doi/10.1016/j.eswa.2024.125725
Liang CWei QDu JWang YJiang Z(2025)Survey of source code vulnerability analysis based on deep learningComputers and Security10.1016/j.cose.2024.104098148:COnline publication date: 1-Jan-2025
https://dl.acm.org/doi/10.1016/j.cose.2024.104098
Feng SWu YXue WPan SZou DLiu YJin HBalzarotti DXu W(2024)FIREProceedings of the 33rd USENIX Conference on Security Symposium10.5555/3698900.3699005(1867-1884)Online publication date: 14-Aug-2024
https://dl.acm.org/doi/10.5555/3698900.3699005
Fang CMiao NSrivastav SLiu JZhang RFang RAsmita Tsang RNazari NWang HHomayoun HBalzarotti DXu W(2024)Large language models for code analysisProceedings of the 33rd USENIX Conference on Security Symposium10.5555/3698900.3698947(829-846)Online publication date: 14-Aug-2024
https://dl.acm.org/doi/10.5555/3698900.3698947
Gao ZSu YHu XXia X(2024)Automating TODO-missed Methods Detection and PatchingACM Transactions on Software Engineering and Methodology10.1145/370079334:1(1-28)Online publication date: 19-Dec-2024
https://dl.acm.org/doi/10.1145/3700793
Zaidi SKhan SVyas PAafer YFilkov VRay BZhou M(2024)A Longitudinal Analysis Of Replicas in the Wild Wild AndroidProceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering10.1145/3691620.3695546(1821-1833)Online publication date: 27-Oct-2024
https://dl.acm.org/doi/10.1145/3691620.3695546
Wang YChen YZhao YGong ZChen JHao DFilkov VRay BZhou M(2024)Mutual Learning-Based Framework for Enhancing Robustness of Code Models via Adversarial TrainingProceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering10.1145/3691620.3695519(1484-1496)Online publication date: 27-Oct-2024
https://dl.acm.org/doi/10.1145/3691620.3695519
Li ZZhang CPan MZhang TLi XFilkov VRay BZhou M(2024)AACEGEN: Attention Guided Adversarial Code Example Generation for Deep Code ModelsProceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering10.1145/3691620.3695500(1245-1257)Online publication date: 27-Oct-2024
https://dl.acm.org/doi/10.1145/3691620.3695500
Li YXu ZZhou MWan HZhao XFilkov VRay BZhou M(2024)Trident: Detecting SQL Injection Attacks via Abstract Syntax Tree-based Neural NetworkProceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering10.1145/3691620.3695289(2225-2229)Online publication date: 27-Oct-2024
https://dl.acm.org/doi/10.1145/3691620.3695289
Fan MZhang WZhao HLiang GJin ZFilkov VRay BZhou M(2024)Detect Hidden Dependency to Untangle CommitsProceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering10.1145/3691620.3694996(179-190)Online publication date: 27-Oct-2024
https://dl.acm.org/doi/10.1145/3691620.3694996
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.

Figures

Tables

Media

View Table of Conten