In a dependently typed language, we can guarantee correctness of our programmes by providing formal proofs. To check them, the typechecker elaborates these programs and proofs into a low-level core language. However, this core language is by nature hard to understand by mere humans, so how can we know we proved the right thing? This question occurs in particular for dependent copattern matching, a powerful language construct for writing programmes and proofs by dependent case analysis and mixed induction/coinduction. A definition by copattern matching consists of a list of clauses that are elaborated to a case tree, which can be further translated to primitive eliminators. In previous work this second step has received a lot of attention, but the first step has been mostly ignored so far. We present an algorithm elaborating definitions by dependent copattern matching to a core language with inductive data types, coinductive record types, an identity type, and constants defined by well-typed case trees. To ensure correctness, we prove that elaboration preserves the first-match semantics of the user clauses. Based on this theoretical work, we reimplement the algorithm used by Agda to check left-hand sides of definitions by pattern matching. The new implementation is at the same time more general and less complex, and fixes a number of bugs and usability issues with the old version. Thus, we take another step towards the formally verified implementation of a practical dependently typed language.

Good tools can bring mechanical verification to programs written in mainstream functional languages. We use hs-to-coq to translate significant portions of Haskell’s containers library into Coq, and verify it against specifications that we derive from a variety of sources including type class laws, the library’s test suite, and interfaces from Coq’s standard library. Our work shows that it is feasible to verify mature, widely used, highly optimized, and unmodified Haskell code. We also learn more about the theory of weight-balanced trees, extend hs-to-coq to handle partiality, and – since we found no bugs – attest to the superb quality of well-tested functional code.

Highly critical application domains, like medicine and aerospace, require the use of strict design, implementation, and validation techniques. Functional languages have been used in these domains to develop synchronous dataflow programming languages for reactive systems. Causal stream functions and functional reactive programming (FRP) capture the essence of those languages in a way that is both elegant and robust. To guarantee that critical systems can operate under high stress over long periods of time, these applications require clear specifications of possible faults and hazards, and how they are being handled. Modeling failure is straightforward in functional languages, and many functional reactive abstractions incorporate support for failure or termination. However, handling unknown types of faults, and incorporating fault tolerance into FRP, requires a different construction and remains an open problem. This work demonstrates how to extend an existing functional reactive framework with fault tolerance features. At value level, we tag faulty signals with reliability and probability information and use random testing to inject faults and validate system properties encoded in temporal logic. At type level, we tag components with the kinds of faults they may exhibit and use type-level programming to obtain compile-time guarantees of key aspects of fault tolerance. Our approach is powerful enough to be used in systems with realistic complexity, and flexible enough to be used to guide system analysis and design, validate system properties in the presence of faults, perform runtime monitoring, and study the effects of different fault tolerance mechanisms.

Multi types – aka non-idempotent intersection types – have been used. to obtain quantitative bounds on higher-order programs, as pioneered by de Carvalho. Notably, they bound at the same time the number of evaluation steps and the size of the result. Recent results show that the number of steps can be taken as a reasonable time complexity measure. At the same time, however, these results suggest that multi types provide quite lax complexity bounds, because the size of the result can be exponentially bigger than the number of steps. Starting from this observation, we refine and generalise a technique introduced by Bernadet and Graham-Lengrand to provide exact bounds. Our typing judgements carry counters, one measuring evaluation lengths and the other measuring result sizes. In order to emphasise the modularity of the approach, we provide exact bounds for four evaluation strategies, both in the λ-calculus (head, leftmost-outermost, and maximal evaluation) and in the linear substitution calculus (linear head evaluation). Our work aims at both capturing the results in the literature and extending them with new outcomes. Concerning the literature, it unifies de Carvalho and Bernadet & Graham-Lengrand via a uniform technique and a complexity-based perspective. The two main novelties are exact split bounds for the leftmost strategy – the only known strategy that evaluates terms to full normal forms and provides a reasonable complexity measure – and the observation that the computing device hidden behind multi types is the notion of substitution at a distance, as implemented by the linear substitution calculus.

The syntax of almost every programming language includes a notion of binder and corresponding bound occurrences, along with the accompanying notions of α-equivalence, capture-avoiding substitution, typing contexts, runtime environments, and so on. In the past, implementing and reasoning about programming languages required careful handling to maintain the correct behaviour of bound variables. Modern programming languages include features that enable constraints like scope safety to be expressed in types. Nevertheless, the programmer is still forced to write the same boilerplate over again for each new implementation of a scope-safe operation (e.g., renaming, substitution, desugaring, printing), and then again for correctness proofs. We present an expressive universe of syntaxes with binding and demonstrate how to (1) implement scope-safe traversals once and for all by generic programming; and (2) how to derive properties of these traversals by generic proving. Our universe description, generic traversals and proofs, and our examples have all been formalised in Agda and are available in the accompanying material available online at https://github.com/gallais/generic-syntax.