Dynamic inference of abstract types

Proceedings of the 2006 International Symposium on Software Testing and Analysis, ISSTA 2006

Volumn 2006, Issue , 2006, Pages 255-265

  Guo, Philip J ^a   Perkins, Jeff H ^a   McCamant, Stephen ^a   Ernst, Michael D ^a  

^a MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

Abstract types; C; C++; Dynamic analysis; Interaction; Java; Mixed level analysis; Type inference; Units; Values and variables

Indexed keywords

BINARY CODES; C (PROGRAMMING LANGUAGE); JAVA PROGRAMMING LANGUAGE; SOFTWARE TESTING;

ABSTRACT TYPES; MIXED-LEVEL ANALYSIS; PROGRAM ANALYSIS; TYPE INFERENCE;

ABSTRACTING;

EID: 34247363267     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1146238.1146268     Document Type: Conference Paper

References (37)

1. O. Agesen. The cartesian product algorithm: Simple and precise type inference of parametric polymorphism. In ECOOP, pages 2-26, Aug. 1996.
2. H. Agrawal and J. R. Horgan. Dynamic program slicing. In PLDI, pages 246-256, White Plains, NY, June 20-22, 1990.
3. E. Alien, D. Chase, V. Luchangco, J.-W. Maessen, and G. L. Steele Jr. Object-oriented units of measurement. In OOPSLA, pages 384-403, Oct. 2004.
4. C.-C. Chang and C.-J. Lin. LIBSVM: a library far support vector machines, 2001. Software available at http://www.csie.ntu.edu.tw/~cjlin/ libsvm.
5. M. Das, B. Liblit, M. Fähndrich, and J. Rehof. Estimating the impact of scalable pointer analysis on optimization. In SAS, pages 260-278, July 2001.
6. T. Eisenbarth, R. Koschke, and D. Simon. Locating features in source code. Computer, 29(3):210-224, Mar. 2003.
7. M. D. Ernst, J. Cockrell, W. G. Griswold, and D. Notkin. Dynamically discovering likely program invariants to support program evolution. IEEE TSE, 27(2):99-123, Feb. 2001.
8. M. D. Ernst, A. Czeisler, W. G. Griswold, and D. Notkin. Quickly detecting relevant program invariants. In ICSE, pages 449-458, June 2000.
9. M. Erwig and M. M. Burnett. Adding apples and oranges. In PADL, pages 171-191, Jan. 2002.
10. C. Flanagan, M. Flatt, S. Krishnamurthi, S. Weirich, and M. Felleisen. Catching bugs in the web of program invariants. In PLDI, pages 23-32, May 1996.
11. O. Greevy and S. Ducasse. Correlating features and code using a compact two-sided trace analysis approach. In CMSR, pages 314-323, Mar. 2005.
12. O. Greevy, S. Ducasse, and T. Gîrba. Analyzing feature traces to incorporate the semantics of change in software evolution analysis. In ICSM, pages 347-356, Sept. 2005.
13. J. Henkel and A. Diwan. Discovering algebraic specifications from Java classes. In ECOOP, pages 431-456, July 2003.
14. I. Hofacker. Vienna RNA package. httpi//www.tbi.univie.ac.at/~ivo/RNA/.
15. T. Joachims. Making large-scale support vector machine learning practical. In Advances in kernel methods: support vector learning, pages 169-184. MIT Press, Cambridge, MA, USA, 1999.
16. A. Kennedy. Programming Languages and Dimensions. PhD thesis, University of Cambridge, April 1996.
17. A. Kuhn, O. Greevy, and T. Gîrba. Applying semantic analysis to feature execution traces. In PCODA, pages 48-53, Nov. 2005.
18. J. R. Larus and S. Chandra. Using tracing and dynamic slicing to tune compilers. Technical Report 1174, University of Wisconsin Madison, Madison, WI, Aug. 26, 1993.
19. B. Liblit, A. Aiken, A. X. Zheng, and M. I. Jordan. Bug isolation via remote program sampling. In PLDI, pages 141-154, June 2003.
20. R. Milner. A theory of type polymorphism in programming. J. Comp. Syst. Sci., 17(3):348-375, 1978.
21. R. Milner, M. Tofte, R. Harper, and D. MacQueen. The Definition of Standard ML (Revised). MIT Press, 1997.
22. M. Mock, M. Das, C. Chambers, and S. Eggers. Dynamic points-to sets: A comparison with static analyses and potential applications in program understanding and optimization. In PASTE, pages 66-72, June 2001.
23. N. Nethercote and J. Seward. Valgrind: A program supervision framework. In RV, July 2003.
24. R. O'Callahan. Generalized Aliasing as a Basis for Program Analysis Tools. PhD thesis, Carnegie-Mellon University, Pittsburgh, PA, May 2001.
25. R. O'Callahan and D. Jackson. Lackwit: A program understanding tool based on type inference. In ICSE, pages 338-348, May 1997.
26. J. H. Perkins and M. D. Ernst. Efficient incremental algorithms for dynamic detection of likely invariants. In FSE, pages 23-32, Nov. 2004.
27. P. Rapicault, M. Blay-Fornarino, S. Ducasse, and A.-M. Dery. Dynamic type inference to support object-oriented reengineering in Smalltalk. In ECOOP '98 Workshop on OO Reengineering, pages 76-77, July 1998.
28. T. Reps, T. Ball, M. Das, and J. Larus. The use of program profiling for software maintenance with applications to the year 2000 problem. In ESEC/FSE, pages 432-449, Sept. 1997.
29. D. Saff, S. Artzi, J. H. Perkins, and M. D. Ernst. Automatic test factoring for Java. In ASE, pages 114-123, Nov. 2005.
30. S. A. Spoon and O. Shivers. Demand-driven type inference with subgoal pruning: Trading precision for scalability. In ECOOP, pages 51-74, June 2004.
31. B. Steensgaard. Points-to analysis in almost linear time. In POPL, pages 32-41, Jan. 1996.
32. F. Tip. A survey of program slicing techniques. Report CS-R9438, Centrum voor Wiskunde en Informatica (CWI), Amsterdam, 1994.
33. G. A. Venkatesh. Experimental results from dynamic slicing of C programs. ACM TOPLAS, 17(2): 197-216, Mar. 1995.
34. M. Weiser. Program slicing. IEEE TSE, SE-10(4):352-357, July 1984.
35. J. Whaley and M. S. Lam. Cloning-based context-sensitive pointer alias analysis using binary decision diagrams. In PLDI, pages 131-144, June 2005.
36. N. Wilde and M. C. Scully. Software reconnaissance: mapping program features to code. Journal of Software Maintenance, 7(l):49-62, 1995.
37. W. E. Wong, S. S. Gokhale, and J. R. Horgan. Quantifying the closeness between program components and features. J. Syst. Softw., 54(2):87-98, Oct. 2000.