Simple closure analysis (SCA) distills the essence of the machinery underlying guaranteed (near-)linear time constraint-based program analyses that emerged in the early 1990s. It involves three key steps. First, reduction to a constraint problem that captures abstract value flow in higher-order programming languages. Second, constraint normalization by instrumented unification closure. Finally, interpretation of the normalized constraints in the program analysis problem at hand.

In the present paper we revisit SCA and explain its original rationale of combining excellent asymptotic and practical run-time efficiency with guaranteed semantic robustness properties. We point out that SCA models induced flows bidirectionally, but keeps directional surface flow information. This preserves valuable directional flow information while breaking through the inherent quasi-cubic complexity of full closure analysis (0CFA), in which also induced flows are directional. We review some of the subsequent applications, extensions, and results on semantic robustness of SCA. We suggest that its modularity and the efficiency of its core data structure, a contracted and closed flow graph on abstract closures and structured data, be kept in mind for algorithmically efficient and semantically robust program analyses on large code bases.

This paper contains the original technical report on Simple Closure Analysis, with a few typographical changes and added explanations for clarification. It was posted on an FTP server at DIKU in 1992 and influenced several subsequent works, but was never submitted for publication (until now). It is a (very) late response to Olivier Danvy's original invitation to submit it to Higher Order Symbolic Computation with my heartfelt thanks for his encouragement.