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Static analysis tools typically address the problem of excessive falsepositives by requiring programmers to explicitly annotate their code. However,when faced with incomplete annotations, many analysis tools are either tooconservative, yielding false positives, or too optimistic, resulting in unsoundanalysis results. In order to flexibly and soundly deal withpartially-annotated programs, we propose to build upon and adapt the gradualtyping approach to abstract-interpretation-based program analyses.Specifically, we focus on null-pointer analysis and demonstrate that a gradualnull-pointer analysis hits a sweet spot, by gracefully applying static analysiswhere possible and relying on dynamic checks where necessary for soundness. Inaddition to formalizing a gradual null-pointer analysis for a core imperativelanguage, we build a prototype using the Infer static analysis framework, andpresent preliminary evidence that the gradual null-pointer analysis reducesfalse positives compared to two existing null-pointer checkers for Infer.Further, we discuss ways in which the gradualization approach used to derivethe gradual analysis from its static counterpart can be extended to supportmore domains. This work thus provides a basis for future analysis tools thatcan smoothly navigate the tradeoff between human effort and run-time overheadto reduce the number of reported false positives.

Gradual Program Analysis for Null Pointers