Type-level computations for Ruby libraries

Abstract

Many researchers have explored ways to bring static typing to dynamic languages. However, to date, such systems are not precise enough when types depend on values, which often arises when using certain Ruby libraries. For example, the type safety of a database query in Ruby on Rails depends on the table and column names used in the query. To address this issue, we introduce CompRDL, a type system for Ruby that allows library method type signatures to include typelevel computations (or comp types for short). Combined with singleton types for table and column names, comp types let us give database query methods type signatures that compute a table's schema to yield very precise type information. Comp types for hash, array, and string libraries can also increase precision and thereby reduce the need for type casts. We formalize CompRDL and prove its type system sound. Rather than type check the bodies of library methods with comp types-those methods may include native code or be complex-CompRDL inserts run-time checks to ensure library methods abide by their computed types. We evaluated CompRDL by writing annotations with type-level computations for several Ruby core libraries and database query APIs. We then used those annotations to type check two popular Ruby libraries and four Ruby on Rails web apps. We found the annotations were relatively compact and could successfully type check 132 methods across our subject programs. Moreover, the use of type-level computations allowed us to check more expressive properties, with fewer manually inserted casts, than was possible without type-level computations. In the process, we found two type errors and a documentation error that were confirmed by the developers. Thus, we believe CompRDL is an important step forward in bringing precise static type checking to dynamic languages.

Refinement kinds: type-safe programming with practical type-level computation

“…Following the approach of Kazerounian et al [2019], it would be interesting to study a version of our theory of refinement kinds that is applied to a real-world dynamically typed language by inserting run-time checks to ensure methods follow their specified types.…”

“…The example above contrasts with [Kazerounian et al 2019], where a related example is formulated such that the equivalent of our ColRecPreds type-level function actually queries a database for the appropriate table schema, whereas ColRecPreds can only inspect the "shape" of its type arguments to obtain the table schema (i.e. the types of records contained in the collection).…”

SimTyper: sound type inference for Ruby using type equality prediction

“…This is a core idea behind RDL [Foster et al 2018], a Ruby type checker on which InferDL and SimTyper are built. Finally, Kazerounian et al [2019] introduced the idea of type-level computations, which are also incorporated into SimTyper and greatly reduce the need for type casts in the presence of arrays, hashes, and Rails database queries.…”

“…Many researchers have explored ways to add static types to dynamic languages, aiming to provide the benefits of static typing while preserving the flexibility of the language [Aiken et al 1994;Cartwright and Fagan 1991;Flanagan and Felleisen 1997;Kazerounian et al 2019;Lerner et al 2013;Ren and Foster 2016;Siek and Taha 2006;Tobin-Hochstadt and Felleisen 2008;Vitousek et al 2014].…”

Coming to Terms with Your Choices: An Existential Take on Dependent Types

“…For brevity, our presentation only covers a small part of Spark's dataset interface, but the approach can be scaled to cover that interface in its entirety. The type safety of database queries is a canonical example and has been studied in many different settings [Chlipala 2010;Kazerounian et al 2019;Leijen and Meijer 1999;Meijer et al 2006].…”