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...[ Read more ]

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.