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| Mary Jean Harrold, Gregg Rothermel, "Separate Computation of Alias Information for Reuse," IEEE Transactions on Software Engineering, vol. 22, no. 7, pp. 442-460, July, 1996. | |||
| BibTex | x | ||
| @article{ 10.1109/32.538603, author = {Mary Jean Harrold and Gregg Rothermel}, title = {Separate Computation of Alias Information for Reuse}, journal ={IEEE Transactions on Software Engineering}, volume = {22}, number = {7}, issn = {0098-5589}, year = {1996}, pages = {442-460}, doi = {http://doi.ieeecomputersociety.org/10.1109/32.538603}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, } | |||
| RefWorks Procite/RefMan/Endnote | x | ||
| TY - JOUR JO - IEEE Transactions on Software Engineering TI - Separate Computation of Alias Information for Reuse IS - 7 SN - 0098-5589 SP442 EP460 EPD - 442-460 A1 - Mary Jean Harrold, A1 - Gregg Rothermel, PY - 1996 KW - Aliasing KW - data flow analysis KW - pointers KW - reuse KW - separate analysis KW - static analysis. VL - 22 JA - IEEE Transactions on Software Engineering ER - | |||
Abstract—Interprocedural data flow information is useful for many software testing and analysis techniques, including data flow testing, regression testing, program slicing, and impact analysis. For programs with aliases, these testing and analysis techniques can yield invalid results, unless the data flow information accounts for aliasing effects. Recent research provides algorithms for performing interprocedural data flow analysis in the presence of aliases; however, these algorithms are expensive, and achieve precise results only on complete programs. This paper presents an algorithm for performing alias analysis on incomplete programs that lets individual software components such as library routines, subroutines, or subsystems be independently analyzed. The paper also presents an algorithm for reusing the results of this separate analysis when the individual software components are linked with calling modules. Our algorithms let us analyze frequently used software components, such as library routines or classes, independently, and reuse the results of that analysis when analyzing calling programs, without incurring the expense of completely reanalyzing each calling program. Our algorithms also provide a way to analyze large systems incrementally.
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