Modular, Fully-abstract Compilation by Approximate Back-translation

A compiler is fully-abstract if the compilation from source language programs to target language programs reflects and preserves behavioural equivalence. Such compilers have important security benefits, as they limit the power of an attacker interacting with the program in the target language to that of an attacker interacting with the program in the source language. Proving compiler full-abstraction is, however, rather complicated. A common proof technique is based on the back-translation of target-level program contexts to behaviourally-equivalent source-level contexts. However, constructing such a back- translation is problematic when the source language is not strong enough to embed an encoding of the target language. For instance, when compiling from STLC to ULC, the lack of recursive types in the former prevents such a back-translation. We propose a general and elegant solution for this problem. The key insight is that it suffices to construct an approximate back-translation. The approximation is only accurate up to a certain number of steps and conservative beyond that, in the sense that the context generated by the back-translation may diverge when the original would not, but not vice versa. Based on this insight, we describe a general technique for proving compiler full-abstraction and demonstrate it on a compiler from STLC to ULC. The proof uses asymmetric cross-language logical relations and makes innovative use of step-indexing to express the relation between a context and its approximate back-translation. The proof extends easily to common compiler patterns such as modular compilation and it, to the best of our knowledge, it is the first compiler full abstraction proof to have been fully mechanised in Coq. We believe this proof technique can scale to challenging settings and enable simpler, more scalable proofs of compiler full-abstraction

Knowledge Compilation of Logic Programs Using Approximation Fixpoint Theory

To appear in Theory and Practice of Logic Programming (TPLP), Proceedings of ICLP 2015 Recent advances in knowledge compilation introduced techniques to compile \emph{positive} logic programs into propositional logic, essentially exploiting the constructive nature of the least fixpoint computation. This approach has several advantages over existing approaches: it maintains logical equivalence, does not require (expensive) loop-breaking preprocessing or the introduction of auxiliary variables, and significantly outperforms existing algorithms. Unfortunately, this technique is limited to \emph{negation-free} programs. In this paper, we show how to extend it to general logic programs under the well-founded semantics. We develop our work in approximation fixpoint theory, an algebraical framework that unifies semantics of different logics. As such, our algebraical results are also applicable to autoepistemic logic, default logic and abstract dialectical frameworks

Evaluating the Relationship Between Running Times and DNA Sequence Sizes using a Generic-Based Filtering Program.

Generic programming depends on the decomposition of programs into simpler components which may be developed separately and combined arbitrarily, subject only to well- defined interfaces. Bioinformatics deals with the application of computational techniques to data present in the Biological sciences. A genetic sequence is a succession of letters which represents the basic structure of a hypothetical DNA molecule, with the capacity to carry information. This research article studied the relationship between the running times of a generic-based filtering program and different samples of genetic sequences in an increasing order of magnitude. A graphical result was obtained to adequately depict this relationship. It was also discovered that the complexity of the generic tree program was O (log2 N). This research article provided one of the systematic approaches of generic programming to Bioinformatics, which could be instrumental in elucidating major discoveries in Bioinformatics, as regards efficient data management and analysis