This article presents a framework, Frog, for Context-Based File Systems (CBFSs) that aim at simplifying the development of context-based file systems and applications. Unlike existing informed-based context-aware systems, Frog is a unifying informed-based framework that abstracts context-specific solutions as views, allowing applications to make view selections according to application behaviors. The framework can not only eliminate overheads induced by traditional context analysis, but also simplify the interactions between the context-based file systems and applications. Rather than propagating data through solution-specific interfaces, views in Frog can be selected by inserting their names in file path strings. With Frog in place, programmers can migrate an application from one solution to another by switching among views rather than changing programming interfaces. Since the data consistency issues are automatically enforced by the framework, file-system developers can focus their attention on context-specific solutions. We implement two prototypes to demonstrate the strengths and overheads of our design. Inspired by an observation that there are more than 50% of small files (<4KB) in a file system, we create a Bi-context Archiving Virtual File System (BAVFS) that utilizes conservative and aggressive prefetching for the contexts of random and sequential reads. To improve the performance of random read-and-write operations, the Bi-context Hybrid Virtual File System (BHVFS) combines the update-in-place and update-out-of-place solutions for read-intensive and write-intensive contexts. Our experimental results show that the benefits of Frog-based CBFSs outweigh the overheads introduced by integrating multiple context-specific solutions.