2009 | OriginalPaper | Buchkapitel
Scaling CFL-Reachability-Based Points-To Analysis Using Context-Sensitive Must-Not-Alias Analysis
verfasst von : Guoqing Xu, Atanas Rountev, Manu Sridharan
Erschienen in: ECOOP 2009 – Object-Oriented Programming
Verlag: Springer Berlin Heidelberg
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
Pointer analyses derived from a Context-Free-Language (CFL) reachability formulation achieve very high precision, but they do not scale well to compute the points-to solution for an entire large program. Our goal is to increase significantly the scalability of the currently most precise points-to analysis for Java. This CFL-reachability analysis depends on determining whether two program variables may be aliases. We propose an efficient but less precise pre-analysis that computes context-sensitive
must-not-alias
information for all pairs of variables. Later, these results can be used to quickly filter out infeasible CFL-paths during the more precise points-to analysis. Several novel techniques are employed to achieve precision and efficiency, including a new approximate CFL-reachability formulation of alias analysis, as well as a carefully-chosen trade-off in context sensitivity. The approach effectively reduces the search space of the points-to analysis: the modified points-to analysis is more than three times faster than the original analysis.