Type Inference for C: Applications to the Static Analysis of Incomplete Programs - Melo, Leandro T. C.; Ribeiro, Rodrigo G.; Guimarães, Breno C. F.; Pereira, Fernando Magno Quintão (ACM transactions on programming languages and systems, 2020)
Type inference is a feature that is common to a variety of programming languages. While, in the past, it has been prominently present in functional ones (e.g., ML and Haskell), today, many object-oriented/multi-paradigm languages such as C# and C++ offer, to a certain extent, such a feature. Nevertheless, type inference still is an unexplored subject in the realm of C. In particular, it remains open whether it is possible to devise a technique that encompasses the idiosyncrasies of this language. The first difficulty encountered when tackling this problem is that parsing C requires, not only syntactic, but also semantic information. Yet, greater challenges emerge due to C’s intricate type system. In this work, we present a unification-based framework that lets us infer the missing struct, union, enum, and typedef declarations in a program. As an application of our technique, we investigate the reconstruction of partial programs. Incomplete source code naturally appears in software development: during design and while evolving, testing, and analyzing programs; therefore, understanding it is a valuable asset. With a reconstructed well-typed program, one can: (i) enable static analysis tools in scenarios where components are absent; (ii) improve precision of “zero setup” static analysis tools; (iii) apply stub generators, symbolic executors, and testing tools on code snippets; and (iv) provide engineers with an assortment of compilable benchmarks for performance and correctness validation. We evaluate our technique on code from a variety of C libraries, including GNU’s Coreutils and on snippets from popular projects such as CPython, FreeBSD, and Git.