Μονάδα:
Τμήμα Πληροφορικής & Τηλεπικοινωνιών
Τίτλος:
Scalability-First Pointer Analysis with Self-Tuning Context-Sensitivity
Γλώσσες Τεκμηρίου:
Αγγλικά
Περίληψη:
Context-sensitivity is important in pointer analysis to ensure high
precision, but existing techniques suffer from unpredictable scala-
bility. Many variants of context-sensitivity exist, and it is difficult
to choose one that leads to reasonable analysis time and obtains
high precision, without running the analysis multiple times.
We present the Scaler framework that addresses this problem.
Scaler efficiently estimates the amount of points-to information
that would be needed to analyze each method with different variants
of context-sensitivity. It then selects an appropriate variant for
each method so that the total amount of points-to information is
bounded, while utilizing the available space to maximize precision.
Our experimental results demonstrate that Scaler achieves pre-
dictable scalability for all the evaluated programs (e.g., speedups
can reach 10x for 2-object-sensitivity), while providing a precision
that matches or even exceeds that of the best alternative techniques.
Συγγραφείς:
Yue Li
Tian Tan
Anders Mοller
Yannis Smaragdakis
Εκδότης:
Association for Computing Machinery (ACM)
Τίτλος συνεδρίου:
Foundations of Software Engineering
Λέξεις-κλειδιά:
static analysis, points-to analysis, Java
Κύρια θεματική κατηγορία:
Τεχνολογία – Πληροφορική
