In this paper we generalise the notion of extensional (functional) equivalence of programs to abstract equivalences induced by abstract interpretations . The standard notion of extensional equivalence is recovered as the special case, induced by the concrete interpretation. Some properties of the extensional equivalence, such as the one spelled out in Rice’s theorem, lift to the abstract equivalences in suitably generalised forms. On the other hand, the generalised framework gives rise to interesting and important new properties, and allows refined, non-extensional analyses. In particular, since programs turn out to be extensionally equivalent if and only if they are equivalent just for the concrete interpretation, it follows that any non-trivial abstract interpretation uncovers some intensional aspect of programs. This striking result is also effective, in the sense that it allows constructing, for any non-trivial abstraction, a pair of programs that are extensionally equivalent, but have different abstract semantics. The construction is based on the fact that abstract interpretations are always sound, but that they can be made incomplete through suitable code transformations. To construct these transformations, we introduce a novel technique for building incompleteness cliques of extensionally equivalent yet abstractly distinguishable programs: They are built together with abstract interpretations that produce false alarms. While programs are forced into incompleteness cliques using both control-flow and data-flow transformations, the main result follows from limitations of data-flow transformations with respect to control-flow ones. A further consequence is that the class of incomplete programs for a non-trivial abstraction is Turing complete. The obtained results also shed a new light on the relation between the techniques of code obfuscation and the precision in program analysis.
Context-sensitive global analysis of large code bases can be expensive, which can make its use impractical during software development. However, there are many situations in which modifications are small and isolated within a few components, and it is desirable to reuse as much as possible previous analysis results. This has been achieved to date through incremental global analysis fixpoint algorithms that achieve cost reductions at fine levels of granularity, such as changes in program lines. However, these fine-grained techniques are neither directly applicable to modular programs nor are they designed to take advantage of modular structures. This paper describes, implements, and evaluates an algorithm that performs efficient context-sensitive analysis incrementally on modular partitions of programs. The experimental results show that the proposed modular algorithm shows significant improvements, in both time and memory consumption, when compared to existing non-modular, fine-grain incremental analysis techniques. Furthermore, thanks to the proposed intermodular propagation of analysis information, our algorithm also outperforms traditional modular analysis even when analyzing from scratch.
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