Increasing demand on computer networks continuously pushes manufacturers to incorporate novel features and capabilities into their switches at an ever-accelerating pace. However, the traditional approach to switch development relies on informal specifications and handcrafted tests to ensure reliability, which are tedious and slow to maintain and update, effectively putting feature velocity at odds with reliability.

File systems are often used to store persistent application data, but manipulating file systems using standard APIs can be difficult for programmers. Forest is a domain-specific language that bridges the gap between the on-disk and in-memory representations of file system data. Given a high-level specification of the structure, contents, and properties of a collection of directories, files, and symbolic links, the Forest compiler generates tools for loading, storing, and validating that data. Unfortunately, the initial implementation of Forest offered few mechanisms for controlling cost-e.g., the run-time system could load gigabytes of data, even if only a few bytes were needed. This paper introduces Incremental Forest (iForest), an extension to Forest with an explicit delay construct that programmers can use to precisely control costs. We describe the design of iForest using a series of running examples, present a formal semantics in a core calculus, and define a simple cost model that accurately characterizes the resources needed to use a given specification. We propose skins, which allow programmers to modify the delay structure of a specification in a compositional way, and develop a static type system for ensuring compatibility between specifications and skins. We prove the soundness and completeness of the type system and a variety of algebraic properties of skins. We describe an OCaml implementation and evaluate its performance on applications developed in collaboration with watershed hydrologists.

We present a method for automatically repairing arbitrary software defects in embedded systems, which have limited memory, disk and CPU capacities, but exist in great numbers. We extend evolutionary computation (EC) algorithms that search for valid repairs at the source code level to assembly and ELF format binaries, compensating for limited system resources with several algorithmic innovations. Our method does not require access to the source code or build toolchain of the software under repair, does not require program instrumentation, specialized execution environments, or virtual machines, or prior knowledge of the bug type.