Program transformation

About: Program transformation is a research topic. Over the lifetime, 2468 publications have been published within this topic receiving 73415 citations.

Verification of Parameterized Systems Using Logic Program Transformations

Abstract: We show how the problem of verifying parameterized systems can be reduced to the problem of determining the equivalence of goals in a logic program. We further show how goal equivalences can be established using induction-based proofs. Such proofs rely on a powerful new theory of logic program transformations (encompassing unfold, fold and goal replacement over multiple recursive clauses), can be highly automated, and are applicable to a variety of network topologies, including uni- and bi-directional chains, rings, and trees of processes. Unfold transformations in our system correspond to algorithmic model-checking steps, fold and goal replacement correspond to deductive steps, and all three types of transformations can be arbitrarily interleaved within a proof. Our framework thus provides a seamless integration of algorithmic and deductive verification at fine levels of granularity.

Semantics-based code obfuscation by abstract interpretation

Abstract: In recent years code obfuscation has attracted research interest as a promising technique for protecting secret properties of programs. The basic idea of code obfuscation is to transform programs in order to hide their sensitive information while preserving their functionality. One of the major drawbacks of code obfuscation is the lack of a rigorous theoretical framework that makes it difficult to formally analyze and certify the effectiveness of obfuscating techniques. We face this problem by providing a formal framework for code obfuscation based on abstract interpretation and program semantics. In particular, we show that what is hidden and what is preserved by an obfuscating transformation can be expressed as abstract interpretations of program semantics. Being able to specify what is masked and what is preserved by an obfuscation allows us to understand its potency, namely the amount of obscurity that the transformation adds to programs. In the proposed framework, obfuscation and attackers are modeled as approximations of program semantics and the lattice of abstract interpretations provides a formal tool for comparing obfuscations with respect to their potency. In particular, we prove that our framework provides an adequate setting to measure not only the potency of an obfuscation but also its resilience, i.e., the difficulty of undoing the obfuscation. We consider code obfuscation by opaque predicate insertion and we show how the degree of abstraction needed to disclose different opaque predicates allows us to compare their potency and resilience.

An Automatic Program Generator for Multi-Level Specialization

Abstract: Program specialization can divide a computation into several computation stages. This paper investigates the theoretical limitations and practical problems of standard specialization tools, presents multi-level specialization, and demonstrates that, in combination with the cogen approach, it is far more practical than previously supposed. The program generator which we designed and implemented for a higher-order functional language converts programs into very compact multi-level generating extensions that guarantee fast successive specialization. Experimental results show a remarkable reduction of generation time and generator size compared to previous attempts of multi-level specialization by self-application. Our approach to multi-level specialization seems well-suited for applications where generation time and program size are critical.

Dynamic slicing on Java bytecode traces

Abstract: Dynamic slicing is a well-known technique for program analysis, debugging and understanding. Given a program P and input I, it finds all program statements which directly/indirectly affect the values of some variables' occurrences when P is executed with I. In this article, we develop a dynamic slicing method for Java programs. Our technique proceeds by backwards traversal of the bytecode trace produced by an input I in a given program P. Since such traces can be huge, we use results from data compression to compactly represent bytecode traces. The major space savings in our method come from the optimized representation of (a) data addresses used as operands by memory reference bytecodes, and (b) instruction addresses used as operands by control transfer bytecodes. We show how dynamic slicing algorithms can directly traverse our compact bytecode traces without resorting to costly decompression. We also extend our dynamic slicing algorithm to perform “relevant slicing”. The resultant slices can be used to explain omission errors that is, why some events did not happen during program execution. Detailed experimental results on space/time overheads of tracing and slicing are reported in the article. The slices computed at the bytecode level are translated back by our tool to the source code level with the help of information available in Java class files. Our JSlice dynamic slicing tool has been integrated with the Eclipse platform and is available for usage in research and development.

Compilation of functional languages by program transformation

Abstract: One of the most important issues concerning functional languages is the efficiency and the correctness of their implementation. We focus on sequential implementations for conventional von Neumann computers. The compilation process is described in terms of program transformations in the functional framework. The original functional expression is transformed into a functional term that can be seen as a traditional machine code. The two main steps are the compilation of the computation rule by the introduction of continuation functions and the compilation of the environment management using combinators. The advantage of this approach is that we do not have to introduce an abstract machine, which makes correctness proofs much simpler. As far as efficiency is concerned, this approach is promising since many optimizations can be described and formally justified in the functional framework.