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In this article we present a general method for achieving global static analyzers that are precise, sound, yet also scalable. Our method generalizes the sparse analysis techniques on top of the abstract interpretation framework to support relational as well as non-relational semantics properties for C-like languages. We first use the abstract interpretation(More)
We present a sound method for clustering alarms from static analyzers. Our method clusters alarms by discovering sound dependencies between them such that if the dominant alarms of a cluster turns out to be false, all the other alarms in the same cluster are guaranteed to be false. We have implemented our clustering algorithm on top of a realistic(More)
In this article, we present a general method for achieving global static analyzers that are precise and sound, yet also scalable. Our method, on top of the abstract interpretation framework, is a general sparse analysis technique that supports relational as well as nonrelational semantics properties for various programming languages. Analysis designers(More)
We present a method for selectively applying context-sensitivity during interprocedural program analysis. Our method applies context-sensitivity only when and where doing so is likely to improve the precision that matters for resolving given queries. The idea is to use a pre-analysis to estimate the impact of context-sensitivity on the main analysis's(More)
We present a method for automatically learning an effective strategy for clustering variables for the Octagon analysis from a given codebase. This learned strategy works as a preprocessor of Octagon. Given a program to be analyzed, the strategy is first applied to the program and clusters variables in it. We then run a partial variant of the Octagon(More)
We present a method for selectively applying context-sensitivity during interprocedural program analysis. Our method applies context-sensitivity only when and where doing so is likely to improve the precision that matters for resolving given queries. The idea is to use a pre-analysis to estimate the impact of context-sensitivity on the main analysis’s(More)
We present a technique for automatically generating features for data-driven program analyses. Recently data-driven approaches for building a program analysis have been proposed, which mine existing codebases and automatically learn heuristics for finding a cost-effective abstraction for a given analysis task. Such approaches reduce the burden of the(More)
We present a machine-learning-based technique for selectively applying unsoundness in static analysis. Existing bug-finding static analyzers are unsound in order to be precise and scalable in practice. However, they are uniformly unsound and hence at the risk of missing a large amount of real bugs. By being sound, we can improve the detectability of the(More)
In this paper, we present a useful technique for implementing practical static program analyzers that use widening. Our technique aims to improve the efficiency of the conventional widening-with-thresholds technique at a small precision compromise. In static analysis, widening is used to accelerate (or converge) fixed point iterations. Unfortunately, this(More)
We present a sparse evaluation technique that is effectively applicable to a set of elaborate semantic-based static analyses. Existing sparse evaluation techniques are effective only when the underlying analyses have comparably low precision. For example, if a pointer analysis precision is not affected by numeric statements like x≔1 then existing sparse(More)