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Approximations during program analysis are a necessary evil, as they ensureessential properties, such as soundness and termination of the analysis, butthey also imply not always producing useful results. Automatic techniques havebeen studied to prevent precision loss, typically at the expense of largerresource consumption. In both cases (i.e., when analysis produces inaccurateresults and when resource consumption is too high), it is necessary to havesome means for users to provide information to guide analysis and thus improveprecision and/or performance. We present techniques for supporting within anabstract interpretation framework a rich set of assertions that can deal withmultivariance/context-sensitivity, and can handle different run-time semanticsfor those assertions that cannot be discharged at compile time. We show how theproposed approach can be applied to both improving precision and acceleratinganalysis. We also provide some formal results on the effects of such assertionson the analysis results.

Multivariant Assertion-based Guidance in Abstract Interpretation