ML Dependency Analysis for Assessors

Critical software needs to obtain an assessment before commissioning. This assessment is given after a long task of software analysis performed by assessors. They may be helped by tools, used interactively, to build models using information-flow analysis. Tools like SPARK-Ada exist for Ada subsets used for critical software. But some emergent languages such as those of the ML family lack such adapted tools. Providing similar tools for ML languages requires special attention on specific features such as higher-order functions and pattern-matching. This paper presents an information-flow analysis for such a language specifically designed according to the needs of assessors. This analysis can be parametrized to allow assessors getting a view of dependencies at several levels of abstraction and gives the basis for an efficient fault tolerance analysis.