Detecting memory leaks through introspective dynamic behavior modelling using machine learning

Proceedings - International Conference on Software Engineering

Volumn , Issue 1, 2014, Pages 814-824

  Lee, Sangho ^a   Jung, Changhee ^b   Pande, Santosh ^a  

^a Georgia Institute of Technology   (United States)

^b State University   (United States)

Author keywords

Machine learning; Memory leak detection; Runtime analysis

Indexed keywords

ARTIFICIAL INTELLIGENCE; LEAK DETECTION; SOFTWARE ENGINEERING;

DYNAMIC BEHAVIORS; HIGH-ACCURACY; MEMORY LEAK DETECTIONS; MODEL-BASED PREDICTION; OBSERVED DATA; OPTIMAL ACCURACY; RUN-TIME ANALYSIS; TRAINING EXAMPLE;

LEARNING SYSTEMS;

EID: 84994156602     PISSN: 02705257     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2568225.2568307     Document Type: Conference Paper

References (35)

1. Bash patch report. http: //ftp. gnu.org/gnu/bash/ bash-4. 0-patches/bash40-033.
2. lighttpd-y light. http: //www. lighttpd. net/.
3. lighttpd issue tracker. http: //redmine. lighttpd. net/issues/1775.
4. D. A. Barrett and B. G. Zorn. Using lifetime predictors to improve memory allocation performance. In Proceedings of the ACM SIGPLAN 1993 Conference on Programming Language Design and Implementation, PLDI '93, pages 187-196, New York, NY, USA, 1993. ACM.
5. H. J. Boehm. Space eficient conservative garbage collection. SIGPLAN Not., 39: 490-501, April 2004.
6. M. Bond and K. McKinley. Tolerating memory leaks. In Proceedings of the 23rd ACM SIGPLAN OOPSLA. ACM, 2008.
7. M. D. Bond and K. McKinley. Leak pruning. In Proceeding of the 14th ASPLOS, pages 277-288. ACM, 2009.
8. M. D. Bond and K. S. McKinley. Bell: bit-encoding online memory leak detection. In Proc. of the 12th ASPLOS, New York, USA, 2006.
9. D. Bruening and Q. Zhao. Practical memory checking with dr. memory. In Proc. of the 9th CGO, pages 213-223, 2011.
10. C. J. Burges. A tutorial on support vector machines for pattern recognition. Data mining and knowledge discovery, 2 (2): 121-167, 1998.
11. B. Catanzaro, N. Sundaram, and K. Keutzer. Fast support vector machine training and classification on graphics processors. In Proceedings of the 25th international conference on Machine learning, ICML '08, pages 104-111, New York, NY, USA, 2008. ACM.
12. C.-C. Chang and C.-J. Lin. LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2: 27: 1-27: 27, 2011. Software available at http: //www. csie. ntu. edu. tw/~cjlin/libsvm.
13. S. Cherem, L. Princehouse, and R. Rugina. Practical memory leak detection using guarded value-ow analysis. In Proc. of 28th PLDI'07.
14. T. M. Chilimbi and M. Hauswirth. Low-overhead memory leak detection using adaptive statistical pro-ling. In Proc. of 11th ASPLOS'04.
15. J. Clause and A. Orso. Leakpoint: pinpointing the causes of memory leaks. In Proc. of the 32nd ICSE, New York, NY, USA, 2010.
16. C. Cortes and V. Vapnik. Support-vector networks. Machine Learning, 20 (3): 273-297, 1995.
17. H. P. Graf, E. Cosatto, L. Bottou, I. Dourdanovic, and V. Vapnik. Parallel support vector machines: The cascade svm. In Advances in neural information processing systems, pages 521-528, 2004.
18. D. L. Heine and M. S. Lam. A practical ow-and context-sensitive c/c++ memory leak detector. In Proc. of the 23rd PLDI, 2003.
19. C.-W. Hsu, C.-C. Chang, and C.-J. Lin. A practical guide to support vector classification, 2010.
20. H. Inoue, D. Stefanovic, and S. Forrest. On the prediction of Java object lifetimes. Computers, IEEE Transactions on, 55 (7): 880-892, July 2006.
21. C. Jung and N. Clark. Ddt: design and evaluation of a dynamic program analysis for optimizing data structure usage. In MICRO 42: Proceedings of the 42nd Annual IEEE/ACM International Symposium on Microarchitecture, pages 56-66, New York, NY, USA, 2009. ACM.
22. C. Jung, S. Lee, E. Raman, and S. Pande. Automated memory leak detection for production use. In Proceedings of the 36th International Conference on Software Engineering, 2014.
23. C. Jung, D.-K. Woo, K. Kim, and S.-S. Lim. Performance characterization of prelinking and preloading for embedded systems. In Proceedings of the 7th ACM & IEEE international conference on Embedded software, EMSOFT '07, pages 213-220, New York, NY, USA, 2007. ACM.
24. Y. Jung and K. Yi. Practical memory leak detector based on parameterized procedural summaries. In Proc. of the 7th ISMM, 2008.
25. W. Lim, S. Park, and H. Han. Memory leak detection with context awareness. In Proceedings of the 2012 ACM Research in Applied Computation Symposium, RACS '12, pages 276-281, New York, NY, USA, 2012. ACM.
26. G. Novark, E. D. Berger, and B. G. Zorn. Eficiently and precisely locating memory leaks and bloat. In Proc. of the 30th PLDI, 2009.
27. M. Orlovich and R. Rugina. Memory leak analysis by contradiction. In In Proceedings of the 13th International Static Analysis Symposium, pages 405-424, 2006.
28. F. Qin, S. Lu, and Y. Zhou. Safemem: Exploiting ecc-memory for detecting memory leaks and memory corruption during production runs. In Proc. of the 11th HPCA, 2005.
29. Y. Sui, D. Ye, and J. Xue. Static memory leak detection using full-sparse value-ow analysis. In Proceedings of the 2012 International Symposium on Software Testing and Analysis, ISSTA 2012, pages 254-264, New York, NY, USA, 2012. ACM.
30. Y. Tang, Q. Gao, and F. Qin. Leaksurvivor: towards safely tolerating memory leaks for garbage-collected languages. In Proc. of USENIX 2008 Annual Technical Conference.
31. J. Whittaker. How to Break Software Security. Addision Wesley.
32. A. Williams. Amazon web services outage caused by memory leak and failure in monitoring alarm. http: //techcrunch. com/2012/10/27/amazon-web-services-outage-caused-by-memory-leak-and-failure-in-monitoring-alarm/, 10 2012.
33. Y. Xie and A. Aiken. Context-and path-sensitive memory leak detection. In Proc. of ESEC/FSE 2005. ACM Press, 2005.
34. G. Xu, M. D. Bond, F. Qin, and A. Rountev. Leakchaser: helping programmers narrow down causes of memory leaks. In Proc. of the 32nd PLDI, 2011.
35. G. Xu and A. Rountev. Precise memory leak detection for Java software using container pro-ling. In Proc. of the 30th ICSE, 2008.