Massachusetts Institute of Technology Computer Science and Artificial Intelligence Laboratory
Abstract
Knowing which method parameters may be mutated during a method's execution is useful for many software engineering tasks. We present an approach to discovering parameter immutability, in which several lightweight, scalable analyses are combined in stages, with each stage rening the overall result. The resulting analysis is scalable and combines the strengths of its component analyses. As one of the component analyses, we present a novel, dynamic mutability analysis and show how its results can be improved by random input generation. Experimental results on programs of up to 185 kLOC show that, compared to previous approaches, our approach increases both scalability and overall accuracy.
