Open AccessCan you Trust your Data?
Author(s) -
Peter Ørbæk
Publication year - 1995
Publication title -
brics report series
Language(s) - English
Resource type - Journals
eISSN - 1601-5355
pISSN - 0909-0878
DOI - 10.7146/brics.v2i24.19926
Subject(s) - abstract interpretation , aliasing , computer science , programming language , semantics (computer science) , constraint (computer aided design) , interpretation (philosophy) , compiler , variable (mathematics) , operational semantics , code (set theory) , trustworthiness , theoretical computer science , algorithm , mathematics , artificial intelligence , computer security , mathematical analysis , geometry , set (abstract data type) , undersampling
A new program analysis is presented, and two compile time methods for this analysis are given. The analysis attempts to answer the question: “Given some trustworthy and some untrustworthy input, can we trust the value of a given variable after execution of some code”. The analyses are based on an abstract interpretation framework and a constraint generation framework, respectively. The analyses are proved safe with respect to an instrumented semantics. We explicitly deal with a language with pointers and possible aliasing problems. The constraint based analysis is related directly to the abstract interpretation and therefore indirectly to the instrumented semantics.