We present the first mechanized, succinct, practical, complete, and proven-faithful semantics for a modern regular expression language with backtracking semantics. We ensure its faithfulness by proving it equivalent to a preexisting line-by-line embedding of the official ECMAScript specification of JavaScript regular expressions. We demonstrate its practicality by presenting two real-world applications. First, a new notion of contextual equivalence for modern regular expressions, which we use to prove or disprove rewrites drawn from previous work. Second, the first formal proof of the PikeVM algorithm used in many real-world engines. In contrast with the specification and other formalization work, our semantics captures not only the top-priority match, but a full backtracking tree recording all possible matches and their respective priority. All our definitions and results have been mechanized in the Rocq proof assistant.

Modern regex languages have strayed far from well-understood traditional regular expressions: they include features that fundamentally transform the matching problem. In exchange for these features, modern regex engines at times suffer from exponential complexity blowups, a frequent source of denial-of-service vulnerabilities in JavaScript applications. Worse, regex semantics differ across languages, and the impact of these divergences on algorithmic design and worst-case matching complexity has seldom been investigated. This paper provides a novel perspective on JavaScript’s regex semantics by identifying a larger-than-previously-understood subset of the language that can be matched with linear time guarantees. In the process, we discover several cases where state-of-the-art algorithms were either wrong (semantically incorrect), inefficient (suffering from superlinear complexity) or excessively restrictive (assuming certain features could not be matched linearly). We introduce novel algorithms to restore correctness and linear complexity. We further advance the state-of-the-art in linear regex matching by presenting the first nonbacktracking algorithms for matching lookarounds in linear time: one supporting captureless lookbehinds in any regex language, and another leveraging a JavaScript property to support unrestricted lookaheads and lookbehinds. Finally, we describe new time and space complexity tradeoffs for regex engines. All of our algorithms are practical: we validated them in a prototype implementation, and some have also been merged in the V8 JavaScript implementation used in Chrome and Node.js.

We present an executable, proven-safe, faithful, and future-proof Coq mechanization of JavaScript regular expression (regex) matching, as specified by the latest published edition of ECMA-262 section 22.2. This is, to our knowledge, the first time that an industrial-strength regex language has been faithfully mechanized in an interactive theorem prover. We highlight interesting challenges that arose in the process (including issues of encoding, corner cases, and executability), and we document the steps that we took to ensure that the result is straightforwardly auditable and that our understanding of the specification aligns with existing implementations. We demonstrate the usability and versatility of the mechanization through a broad collection of analyses, case studies, and experiments: we prove that JavaScript regex matching always terminates and is safe (no assertion failures); we identify subtle corner cases that led to mistakes in previous publications; we verify an optimization extracted from a state-of-the-art regex engine; we show that some classic properties described in automata textbooks and used in derivatives-based matchers do not hold in JavaScript regexes; and we demonstrate that the cost of updating the mechanization to account for changes in the original specification is reasonably low. Our mechanization can be extracted to OCaml and JavaScript and linked with Unicode libraries to produce an executable regex engine that passes the relevant parts of the official Test262 conformance test suite.

Modern Just-in-Time compilers (or JITs) typically interleave several mechanisms to execute a program. For faster startup times and to observe the initial behavior of an execution, interpretation can be initially used. But after a while, JITs dynamically produce native code for parts of the program they execute often. Although some time is spent compiling dynamically, this mechanism makes for much faster times for the remaining of the program execution. Such compilers are complex pieces of software with various components, and greatly rely on a precise interplay between the different languages being executed, including on-stack-replacement. Traditional static compilers like CompCert have been mechanized in proof assistants, but JITs have been scarcely formalized so far, partly due to their impure nature and their numerous components. This work presents a model JIT with dynamic generation of native code, implemented and formally verified in Coq. Although some parts of a JIT cannot be written in Coq, we propose a proof methodology to delimit, specify and reason on the impure effects of a JIT. We argue that the daunting task of formally verifying a complete JIT should draw on existing proofs of native code generation. To this end, our work successfully reuses CompCert and its correctness proofs during dynamic compilation. Finally, our prototype can be extracted and executed.

Just-in-time compilers for dynamic languages routinely generate code under assumptions that may be invalidated at run-time, this allows for specialization of program code to the common case in order to avoid unnecessary overheads due to uncommon cases. This form of software speculation requires support for deoptimization when some of the assumptions fail to hold. This paper presents a model just-in-time compiler with an intermediate representation that explicits the synchronization points used for deoptimization and the assumptions made by the compiler's speculation. We also present several common compiler optimizations that can leverage speculation to generate improved code. The optimizations are proved correct with the help of a proof assistant. While our work stops short of proving native code generation, we demonstrate how one could use the verified optimization to obtain significant speed ups in an end-to-end setting.