Targeted Synthesis for Programming with Data Invariants.

Programmers frequently maintain implicit data invariants, which are relations between different data structures in a program. Traditionally, such invariants are manually enforced and checked by programmers. This ad-hoc practice is difficult because the programmer must manually account for all the locations and configurations that break an invariant. Moreover, implicit invariants are brittle under code-evolution: when the invariants and data structures change, the programmer must repeat the process of manually repairing all of the code locations where invariants are violated. A much better approach is to introduce data invariants as a language feature and rely on language support to maintain invariants. To handle this challenge, we introduce Targeted Synthesis, a technique for integrating data invariants with invariant-agnostic imperative code at compile-time. This technique is nontrivial due to the complex structure of both invariant specifications, as well as general imperative code. The key insight is to take a language co-design approach involving both the language of data invariants, as well as the imperative language. We leverage this insight to produce two high-level results: first, we support a language with iterators without requiring general quantified reasoning, and second, we infer complicated invariant-preserving patches. We evaluate these claims through a language termed Spyder, a core calculus of data invariants over imperative iterator programs. We evaluate the expressiveness and performance of Spyder on a variety of programs inspired by web applications, and we find that Spyder efficiently compiles and maintains data invariants.