A Survey on String Constraint Solving

They are a fundamental datatype in all the modern programming languages, and operations on strings frequently occur in disparate fields such as software analysis, model checking, database applications, web security, bioinformatics and so on[3, 11, 19, 21, 27, 28, 49, 60, 67]. Reasoning over strings requires solving arbitrarily complex string constraints, i.e., relations defined on a number of string variables. Typical examples of string constraints are string length, (dis-)equality, concatenation, substring, regular expression matching. With the term "string constraint solving" (in short, string solving or SCS) we refer to the process of modelling, processing, and solving combinatorial problems involving string constraints. We may see SCS as a declarative paradigm which falls at the intersection between constraint solving and combinatorics on words: the user states a problem with string variables and constraints, and a suitable string solver seeks a solution for that problem. Although works on the combinatorics of words were already published in the 1940s [110], the dawn of SCS dates back to the late 1980s in correspondence with the rise of Constraint Programming (CP) [114] and Constraint Logic Programming(CLP)[73] paradigms. Pioneers in this field were for example Trilogy[142], a language providing strings, integer and real constraints, and CLP(Σ) [144], an instance of the CLP scheme representing strings as regular sets. The latter in particular was the first known attempt to use string constraints like regular membership to denote regular sets.