Machine learning and its promising branch deep learning have proven to be effective in a
wide range of application domains. Recently, several efforts have shown success in …

Context-free language reachability (CFL-reachability) is a fundamental framework for
program analysis. A large variety of static analyses can be formulated as CFL-reachability …

Context-free language (CFL) reachability is a fundamental framework for formulating
program analyses. CFL-reachability analysis works on top of an edge-labeled graph by …

Many existing static analysis algorithms suffer from cubic bottlenecks because of the need to
compute a dynamic transitive closure (DTC). For the first time, this article studies the …

Context-free language reachability (CFL-reachability) is a prominent model for formulating
program analysis problems. Almost all CFL-reachability algorithms are based on the Reps …

The IFDS algorithm is pivotal in solving field-sensitive data-flow problems. However, its
conventional use of access paths for field sensitivity leads to the generation of a large …

Context-free language reachability (CFL-reachability) is a fundamental framework for
implementing various static analyses. CFL-reachability utilizes context-free grammar (CFG) …

Context-free language reachability is an important program analysis framework, but the
exact analysis problems can be intractable or undecidable, where CFL-reachability …

Context-free language reachability (CFL-reachability) is a fundamental formulation for
program analysis with many applications. CFL-reachability analysis is computationally …

Context-free language (CFL) reachability is a fundamental framework for formulating
program analyses. CFL-reachability analysis works on top of an edge-labeled graph by …