Hybrid Context-Sensitivity for Points-To Analysis
Context-sensitive points-to analysis is valuable for achieving high precision with good performance.
Authors: George Kastrinis´, Yannis Smaragdakis. 2013.
In Proceedings of the 34th ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI ‘13).
Context-sensitive points-to analysis is valuable for achieving high precision with good performance. The standard flavors of context-sensitivity are call-site-sensitivity (kCFA) and object-sensitivity. Combining both flavors of context-sensitivity increases precision but at an infeasibly high cost. We show that a selective combination of call-site- and object-sensitivity for Java points-to analysis is highly profitable.
Worst-case Optimal Join Algorithms
Efficient join processing is one of the most fundamental and well-studied tasks in database research. In this work, we examine algorithms for natural join queries over many relations and describe a new algorithm to process these queries optimally in terms of worst-case data complexity. Our result builds on recent work by Atserias, Grohe, and Marx, who gave bounds on the size of a natural join query in terms of the sizes of the individual relations in the body of the query
Pick Your Contexts Well: Understanding Object-Sensitivity
Object-sensitivity has emerged as an excellent context abstraction for points-to analysis in object-oriented languages. Despite its practical success, however, object-sensitivity is poorly understood.