Please use this identifier to cite or link to this item: http://www.repositorio.ufop.br/handle/123456789/10962
Title: Inference of static semantics for incomplete C programs.
Authors: Melo, Leandro T. C.
Ribeiro, Rodrigo Geraldo
Araújo, Marcos Roberto de
Pereira, Fernando Magno Quintao
Keywords: Partial programs
Parsing
Type inference
Issue Date: 2018
Citation: MELO, L. T. C. et al. Inference of static semantics for incomplete C programs. Proceedings of the ACM on Programming Languages, v. 2, p. 1-28, 2017. Disponível em: <https://dl.acm.org/citation.cfm?id=3158117>. Acesso em: 15 fev. 2019.
Abstract: Incomplete source code naturally emerges in software development: during the design phase, while evolving, testing and analyzing programs. Therefore, the ability to understand partial programs is a valuable asset. However, this problem is still unsolved in the C programming language. Difficulties stem from the fact that parsing C requires, not only syntax, but also semantic information. Furthermore, inferring types so that they respect C’s type system is a challenging task. In this paper we present a technique that lets us solve these problems. We provide a unification-based type inference capable of dealing with C intricacies. The ideas we present let us reconstruct partial C programs into complete well-typed ones. Such program reconstruction has several applications: enabling static analysis tools in scenarios where software components may be absent; improving static analysis tools that do not rely on build-specifications; allowing stub-generation and testing tools to work on snippets; and assisting programmers on the extraction of reusable data-structures out of the program parts that use them. Our evaluation is performed on source code from a variety of C libraries such as GNU’s Coreutils, GNULib, GNOME’s GLib, and GDSL; on implementations from Sedgewick’s books; and on snippets from popular open-source projects like CPython, FreeBSD, and Git.
URI: http://www.repositorio.ufop.br/handle/123456789/10962
metadata.dc.identifier.uri2: https://dl.acm.org/citation.cfm?id=3158117
ISSN: 24751421
Appears in Collections:DECSI - Artigos publicados em periódicos

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