THESIS
2018
xi pages, 1 unnumbered page, 40 pages : illustrations ; 30 cm
Abstract
WALA framework is a static analysis framework for Java bytecodes which has capabilities such as dataflow analysis, class hierarchy analysis, bytecode instrumentation
and points-to analysis. It provides a
flow-insensitive context-insensitive Anderson-style points-to analysis with on-the-fly call graph construction. Its points-to analysis
is efficient with various optimizations. However, it does not provide a precise and
scalable context-sensitive points-to analysis.
In this thesis, we propose implementing a context-sensitive points-to analysis with
a scalable technique called Geometric Encoding in the WALA framework. Geometric
Encoding is a encoding technique which can evaluate contexts of pointer information
in a compressed form. The technique is capable of increasing the scala...[
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WALA framework is a static analysis framework for Java bytecodes which has capabilities such as dataflow analysis, class hierarchy analysis, bytecode instrumentation
and points-to analysis. It provides a
flow-insensitive context-insensitive Anderson-style points-to analysis with on-the-fly call graph construction. Its points-to analysis
is efficient with various optimizations. However, it does not provide a precise and
scalable context-sensitive points-to analysis.
In this thesis, we propose implementing a context-sensitive points-to analysis with
a scalable technique called Geometric Encoding in the WALA framework. Geometric
Encoding is a encoding technique which can evaluate contexts of pointer information
in a compressed form. The technique is capable of increasing the scalability of
context-sensitive points-to analysis by greatly reducing the redundancies of function
contexts. The evaluation results show that our points-to analysis is efficient and
effective.
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