IDE^al: Efficient and precise alias-Aware dataflow analysis

Program analyses frequently track objects throughout a program, which requires reasoning about aliases. Most dataflow analysis frameworks, however, delegate the task of handling aliases to the analysis clients, which causes a number of problems. For instance, custom-made extensions for alias analysis are complex and cannot easily be reused. On the other hand, due to the complex interfaces involved, off-the-shelf alias analyses are hard to integrate precisely into clients. Lastly, for precision many clients require strong updates, and alias abstractions supporting strong updates are often relatively inefficient. In this paper, we present IDE^al , an alias-aware extension to the framework for Interprocedural Distributive Environment (IDE) problems. IDE^al relieves static-analysis authors completely of the burden of handling aliases by automatically resolving alias queries on-demand, both efficiently and precisely. IDE^al supports a highly precise analysis using strong updates by resorting to an on-demand, flow-sensitive, and context-sensitive all-alias analysis. Yet, it achieves previously unseen efficiency by propagating aliases individually, creating highly reusable per-pointer summaries. We empirically evaluate IDE^al by comparing TS^f , a state-of-the-art typestate analysis, to TS^al , an IDE^al - based typestate analysis. Our experiments show that the individual propagation of aliases within IDE^al enables TS^al to propagate 10.4× fewer dataflow facts and analyze 10.3× fewer methods when compared to TS^f . On the DaCapo benchmark suite, TS^al is able to efficiently compute precise results.