Static analysis of JavaScript remains notoriously difficult due to the language’s dynamically typed nature, unconventional scoping rules, and pervasive side effects. Unlike mature infrastructures such as LLVM for C/C++ or Soot for Java, comparable frameworks for JavaScript are fragmented and limited in scope. In this paper, we introduce IRIDIUM, a first-of-its-kind framework to statically optimize JavaScript programs. IRIDIUM systematically lowers JavaScript into a structured intermediate representation (called IRI) that models bindings, environments, and control flow explicitly. The resultant expressiveness enables more predictable analyses and transformations, ranging from dataflow tracking to optimization passes to executable code generation for existing runtimes, that are otherwise hindered by the language’s complexity. By bridging the gap between JavaScript’s surface syntax and the requirements of static analysis, IRIDIUM, thus, lays the foundation for a new generation of tools that can reason effectively about modern JavaScript applications.

Most code is executed more than once. If not entire programs then libraries remain unchanged from one run to the next. Just-in-time compilers expend considerable effort gathering insights about code they compiled many times, and often end up generating the same binary over and over again. We explore how to reuse compiled code across runs of different programs to reduce warm-up costs of dynamic languages. We propose to use speculative contextual dispatch to select versions of functions from an off-line curated code repository . That repository is a persistent database of previously compiled functions indexed by the context under which they were compiled. The repository is curated to remove redundant code and to optimize dispatch. We assess practicality by extending Ř, a compiler for the R language, and evaluating its performance. Our results suggest that the approach improves warmup times while preserving peak performance.