Understanding LDA in source code analysis

22nd International Conference on Program Comprehension, ICPC 2014 - Proceedings

Volumn , Issue , 2014, Pages 26-36

  Binkley, David ^a   Heinz, Daniel ^a   Lawrie, Dawn ^a   Overfelt, Justin ^b  

^a LOYOLA UNIVERSITY MARYLAND   (United States)

^b BOOZ ALLEN HAMILTON   (United States)

Author keywords

Hyper parameters; Latent Dirichlet Allocation; Source code topic models

Indexed keywords

PROFESSIONAL ASPECTS; STATISTICS;

EXPRESSIVE POWER; HYPER-PARAMETER; LATENT DIRICHLET ALLOCATION; LATENT DIRICHLET ALLOCATIONS; SOURCE CODE ANALYSIS; TOPIC MODEL; TUNING PARAMETER; UNIFORM DISTRIBUTION;

COMPUTER PROGRAMMING LANGUAGES;

EID: 84931027268     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2597008.2597150     Document Type: Conference Paper

References (14)

1. H. Asuncion, A. Asuncion, and R. Taylor. Software traceability with topic modeling. In Proceedings of the 32nd ACM/IEEE International Conference on Software Engineering-Volume 1, pages 95-104. ACM, 2010.
2. D. Blei, A. Ng, and M. Jordan. Latent dirichlet allocation. Journal of Machine Learning Research, 3, 2003.
3. S. Grant, J. Cordy, and D. Skillicorn. Using heuristics to estimate an appropriate number of latent topics in source code analysis. Science of Computer Programming, http://dx.doi.org/10.1016/j.scico.2013.03.015, 4 2013.
4. D. Lawrie and D. Binkley. Expanding identifiers to normalizing source code vocabulary. In ICSM '11: Proceedings of the 27th IEEE International Conference on Software Maintenance, 2011.
5. Erik Linstead, Paul Rigor, Sushil Bajracharya, Cristina Lopes, and Pierre Baldi. Mining concepts from code with probabilistic topic models. In Proceedings of the twenty-second IEEE/ACM international conference on Automated software engineering, pages 461-464. ACM, 2007.
6. Stacy K Lukins, Nicholas A Kraft, and Letha H Etzkorn. Source code retrieval for bug localization using latent dirichlet allocation. In Reverse Engineering, 2008. WCRE'08. 15th Working Conference on, pages 155-164. IEEE, 2008.
7. A. McCallum. Mallet: A machine learning for language toolkit, 2002.
8. R. Oliveto, M. Gethers, D. Poshyvanyk, and A. De Lucia. On the equivalence of information retrieval methods for automated traceability link recovery. In Program Comprehension (ICPC), 2010 IEEE 18th International Conference on, pages 68-71, 2010.
9. 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 ACM/IEEE International Conference on Software Engineering (ICSE'13), May 2013.
10. I. Porteous, D. Newman, A. Ihler, A. Asuncion, P. Smyth, and M. Welling. Fast collapsed gibbs sampling for latent dirichlet allocation. 14th ACM SIGKDD international conference on Knowledge discovery and data mining, 3, 2008.
11. M. Steyvers and T. Griffiths. Probabilistic Topic Models in Latent Semantic Analysis: A Road to Meaning, Landauer, T. and Mc Namara, D. and Dennis, S. and Kintsch, W., eds. Lawrence Erlbaum, 2007.
12. Stephen W Thomas. Mining software repositories using topic models. In Software Engineering (ICSE), 2011 33rd International Conference on, pages 1138-1139. IEEE, 2011.
13. Stephen W. Thomas, Bram Adams, Ahmed E. Hassan, and Dorothea Blostein. Modeling the evolution of topics in source code histories. In Proceedings of the 8th Working Conference on Mining Software Repositories, MSR '11, 2011.
14. Kai Tian, Meghan Revelle, and Denys Poshyvanyk. Using latent dirichlet allocation for automatic categorization of software. In Mining Software Repositories, 2009. MSR'09. 6th IEEE International Working Conference on, pages 163-166. IEEE, 2009.