Practical extensions to the IFDS algorithm

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6011 LNCS, Issue , 2010, Pages 124-144

  Naeem, Nomair A ^a   Lhoták, Ondřej ^a   Rodriguez, Jonathan ^a  

^a UNIVERSITY OF WATERLOO   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

ANALYSIS PROBLEMS; CONTEXT-SENSITIVE; DATAFLOW; DYNAMIC PROGRAMMING ALGORITHM; EXTENSION SPEED; INTER-PROCEDURAL; INTERPROCEDURAL DATAFLOW ANALYSIS; MULTIOBJECT; ON-DEMAND; PROGRAM STATE; RETURN FLOW; SUPERGRAPH; TYPE ANALYSIS; TYPESTATE;

ALGORITHMS; COMPUTER SOFTWARE; PROGRAM COMPILERS;

DATA FLOW ANALYSIS;

EID: 77951576976     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-11970-5_8     Document Type: Conference Paper

References (23)

1. Blackburn, S.M., Garner, R., Hoffman, C., Khan, A.M., McKinley, K.S., Bentzur, R., Diwan, A., Feinberg, D., Frampton, D., Guyer, S.Z., Hirzel, M., Hosking, A., Jump, M., Lee, H., Moss, J.E.B., Phansalkar, A., Stefanović, D., Van Drunen, T., von Dincklage, D., Wiedermann, B.: The DaCapo benchmarks: Java benchmarking development and analysis. In: OOPSLA 2006, pp. 169-190 (2006)
2. Cytron, R., Ferrante, J., Rosen, B.K., Wegman, M.N., Zadeck, F.K.: An efficient method of computing static single assignment form. In: POPL 1989, pp. 25-35 (1989)
3. Duesterwald, E., Gupta, R., Soffa, M.L.: Demand-driven computation of interprocedural data flow. In: POPL 1995, pp. 37-48 (1995)
4. Duesterwald, E., Gupta, R., Soffa, M.L.: A practical framework for demand-driven interprocedural data flow analysis. ACM Trans. Program. Lang. Syst. 19(6), 992-1030 (1997)
5. Dufour, B.: Objective quantification of program behaviour using dynamic metrics. Master's thesis, McGill University (June 2004)
6. Fink, S., Yahav, E., Dor, N., Ramalingam, G., Geay, E.: Effective typestate verification in the presence of aliasing. In: ISSTA 2006, pp. 133-144 (2006)
7. Fink, S.J., Yahav, E., Dor, N., Ramalingam, G., Geay, E.: Effective typestate verification in the presence of aliasing. ACM Trans. Softw. Eng. Methodol. 17(2), 1-34 (2008)
8. Horwitz, S., Reps, T., Sagiv, M.: Demand interprocedural dataflow analysis. In: SIGSOFT FSE 1995, pp. 104-115 (1995)
9. Kam, J.B., Ullman, J.D.: Monotone data flow analysis frameworks. Acta Inf. 7, 305-317 (1977)
10. Kildall, G.A.: A unified approach to global program optimization. In: POPL 1973, pp. 194-206 (1973)
11. Lhoták, O.: Comparing call graphs. In: PASTE 2007, pp. 37-42 (2007)
12. Naeem, N.A., Lhoták, O.: Typestate-like analysis of multiple interacting objects. In: OOPSLA 2008, pp. 347-366 (2008)
13. Naeem, N.A., Lhoták, O.: Efficient alias set analysis using SSA form. In: ISMM 2009, pp. 79-88 (2009)
14. Odersky, M., Spoon, L., Venners, B.: Programming in Scala. Artima Press (2008)
15. Reps, T., Horwitz, S., Sagiv, M.: Precise interprocedural dataflow analysis via graph reachability. In: POPL 1995, pp. 49-61 (1995)
16. Reps, T.W.: Solving demand versions of interprocedural analysis problems. In: Fritzson, P.A. (ed.) CC 1994. LNCS, vol. 786, pp. 389-403. Springer, Heidelberg (1994)
17. Rinetzky, N., Sagiv, M., Yahav, E.: Interprocedural shape analysis for cutpoint-free programs. In: Hankin, C., Siveroni, I. (eds.) SAS 2005. LNCS, vol. 3672, pp. 284-302. Springer, Heidelberg (2005)
18. Sagiv, M., Reps, T., Horwitz, S.: Precise interprocedural dataflow analysis with applications to constant propagation. Theoretical Computer Science 167(1-2), 131-170 (1996)
19. Sharir, M., Pnueli, A.: Two approaches to interprocedural data flow analysis. In: Muchnick, S.S., Jones, N.D. (eds.) Program Flow Analysis: Theory and Applications, ch. 7, pp. 189-233. Prentice-Hall, Englewood Cliffs (1981)
20. Shoham, S., Yahav, E., Fink, S., Pistoia, M.: Static specification mining using automata-based abstractions. In: ISSTA 2007, pp. 174-184 (2007)
21. Sundaresan, V., Hendren, L., Razafimahefa, C., Vallée-Rai, R., Lam, P., Gagnon, E., Godin, C.: Practical virtual method call resolution for Java. In: OOPSLA 2000, pp. 264-280 (2000)
22. Vallée-Rai, R., Gagnon, E., Hendren, L.J., Lam, P., Pominville, P., Sundaresan, V.: Optimizing Java bytecode using the Soot framework: is it feasible? In: Watt, D.A. (ed.) CC 2000. LNCS, vol. 1781, pp. 18-34. Springer, Heidelberg (2000)
23. Yang, H., Lee, O., Berdine, J., Calcagno, C., Cook, B., Distefano, D., O'Hearn, P.W.: Scalable shape analysis for systems code. In: Gupta, A., Malik, S. (eds.) CAV 2008. LNCS, vol. 5123, pp. 385-398. Springer, Heidelberg (2008)