Current approaches combining multiple static analyses deriving different, independent properties focus either on modularity or performance.
Whereas declarative approaches facilitate modularity and automated, analysis-independent optimizations, imperative approaches foster manual, analysis-specific optimizations.
In this paper, we present a novel approach to static analyses that leverages the modularity of blackboard systems and combines declarative and imperative techniques.
Our approach allows exchangeability, and pluggable extension of analyses in order to improve sound(i)ness, precision, and scalability and explicitly enables the combination of otherwise incompatible analyses.
With our approach integrated in the OPAL framework, we were able to implement various dissimilar analyses, including a points-to analysis that outperforms an equivalent analysis from Doop, the state-of-the-art points-to analysis framework.
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08:30 - 08:32 Talk | Domain-Independent Interprocedural Program Analysis using Block-Abstraction Memoization Research Papers DOI Pre-print Media Attached | ||
08:33 - 08:34 Talk | Inherent Vacuity for GR(1) Specifications Research Papers DOI | ||
08:35 - 08:36 Talk | Interval Counterexamples for Loop Invariant Learning Research Papers DOI | ||
08:37 - 08:38 Talk | Modular Collaborative Program Analysis in OPAL Research Papers Dominik HelmTU Darmstadt, Germany, Florian KüblerTU Darmstadt, Germany, Michael ReifTU Darmstadt, Germany, Michael EichbergTU Darmstadt, Germany, Mira MeziniTU Darmstadt, Germany DOI | ||
08:39 - 08:40 Talk | Past-Sensitive Pointer Analysis for Symbolic Execution Research Papers David TrabishTel Aviv University, Israel, Timotej KapusImperial College London, UK, Noam RinetzkyTel Aviv University, Cristian CadarImperial College London, UK DOI Pre-print Media Attached | ||
08:41 - 09:00 Talk | Conversations on Analysis 4 Paper Presentations David TrabishTel Aviv University, Israel, Dominik HelmTU Darmstadt, Germany, Rafi ShalomTel Aviv University, Israel, Rongchen XuTsinghua University, China, Shahar MaozTel Aviv University, Israel, M: Rachel Tzoref-BrillIBM Research |