Combined static and dynamic mutability analysis

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“Combined static and dynamic mutability analysis” by Shay Artzi, Adam Kieżun, David Glasser, and Michael D. Ernst. In ASE 2007: Proceedings of the 22nd Annual International Conference on Automated Software Engineering, (Atlanta, GA, USA), November 7-9, 2007, pp. 104-113.
A previous version appeared as MIT Computer Science and Artificial Intelligence Laboratory technical report MIT-CSAIL-TR-2007-020, (Cambridge, MA), March 23, 2007.
A previous version appeared as “Combined static and dynamic mutability analysis” by Shay Artzi, Michael D. Ernst, David Glasser, and Adam Kieżun. MIT Computer Science and Artificial Intelligence Laboratory technical report MIT-CSAIL-TR-2006-065, (Cambridge, MA), September 18, 2006.

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 reference immutability, in which several lightweight, scalable analyses are combined in stages, with each stage refining 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.

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BibTeX entry:

@inproceedings{ArtziKGE2007,
   author = {Shay Artzi and Adam Kie{\.z}un and David Glasser and Michael
	D. Ernst},
   title = {Combined static and dynamic mutability analysis},
   booktitle = {ASE 2007: Proceedings of the 22nd Annual International
	Conference on Automated Software Engineering},
   pages = {104--113},
   address = {Atlanta, GA, USA},
   month = {November~7--9,},
   year = {2007}
}

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