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The US Rejection of the Composite Protocol: A Huge Mistake Based on Illogical Assessments
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Differentially Testing Soundness and Precision of Program Analyzers
In the last decades, numerous program analyzers have been developed both by
academia and industry. Despite their abundance however, there is currently no
systematic way of comparing the effectiveness of different analyzers on
arbitrary code. In this paper, we present the first automated technique for
differentially testing soundness and precision of program analyzers. We used
our technique to compare six mature, state-of-the art analyzers on tens of
thousands of automatically generated benchmarks. Our technique detected
soundness and precision issues in most analyzers, and we evaluated the
implications of these issues to both designers and users of program analyzers
Proving Safety with Trace Automata and Bounded Model Checking
Loop under-approximation is a technique that enriches C programs with
additional branches that represent the effect of a (limited) range of loop
iterations. While this technique can speed up the detection of bugs
significantly, it introduces redundant execution traces which may complicate
the verification of the program. This holds particularly true for verification
tools based on Bounded Model Checking, which incorporate simplistic heuristics
to determine whether all feasible iterations of a loop have been considered.
We present a technique that uses \emph{trace automata} to eliminate redundant
executions after performing loop acceleration. The method reduces the diameter
of the program under analysis, which is in certain cases sufficient to allow a
safety proof using Bounded Model Checking. Our transformation is precise---it
does not introduce false positives, nor does it mask any errors. We have
implemented the analysis as a source-to-source transformation, and present
experimental results showing the applicability of the technique
Towards an Intelligent Tutor for Mathematical Proofs
Computer-supported learning is an increasingly important form of study since
it allows for independent learning and individualized instruction. In this
paper, we discuss a novel approach to developing an intelligent tutoring system
for teaching textbook-style mathematical proofs. We characterize the
particularities of the domain and discuss common ITS design models. Our
approach is motivated by phenomena found in a corpus of tutorial dialogs that
were collected in a Wizard-of-Oz experiment. We show how an intelligent tutor
for textbook-style mathematical proofs can be built on top of an adapted
assertion-level proof assistant by reusing representations and proof search
strategies originally developed for automated and interactive theorem proving.
The resulting prototype was successfully evaluated on a corpus of tutorial
dialogs and yields good results.Comment: In Proceedings THedu'11, arXiv:1202.453
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