Dynamic languages such as Python allow programs to be written more easily using high-level constructs such as comprehensions for queries and using generic code. Efficient execution of programs then requires optimizations powerfulincrementalization of expensive queries and specialization of generic code. Effective incrementalization and specialization of dynamic languages require precise and scalable alias analysis.
This paper describes the development and experimental evaluation of a may-alias analysis for a full dynamic object-oriented language, for program optimization by incrementalization and specialization. The analysis is flow-sensitive; we show that this is necessary for effective optimization of dynamic languages. It uses precise type analysis and a powerful form of context sensitivity, called trace sensitivity, to further improve analysis precision. It uses a compressed representation to significantly reduce the memory used by flow-sensitive analyses. We evaluate the effectiveness of this analysis and several variants of it for incrementalization and specialization of Python programs, and we evaluate the precision, memory usage, and running time of these analyses on programs of diverse sizes. The results show that our analysis has acceptable precision and efficiency and represents the best trade-off between them.