Toward a Dependability Case Language and Workflow for a Radiation Therapy System

We present a near-future research agenda for bringing a suite of modern programming-languages verification tools - specifically interactive theorem proving, solver-aided languages, and formally defined domain-specific languages - to the development of a specific safety-critical system, a radiotherapy medical device. We sketch how we believe recent programming-languages research advances can merge with existing best practices for safety-critical systems to increase system assurance and developer productivity. We motivate hypotheses central to our agenda: That we should start with a single specific system and that we need to integrate a variety of complementary verification and synthesis tools into system development

Data Structure Synthesis

Thesis (Ph.D.)--University of Washington, 2018Data structures are integral to software. Many programs require custom application-specific data structures more complicated than those found in standard libraries. Implementing and maintaining application-specific data structures is tedious and error-prone. This work presents Cozy, a novel tool that synthesizes efficient implementations of application-specific data structures from high-level specifications. Cozy handles a wider range of data structures than previous work, including structures that track multiple related collections and have complex retrieval methods involving sums, counts, minimums, and maximums. Cozy iteratively discovers good data structures using alternating steps of query synthesis and state maintenance. The query synthesis step implements pure operations over the data structure state by leveraging existing enumerative synthesis techniques, specialized to the data structures domain. The state maintenance step implements imperative state modifications by re-framing them as fresh queries that determine what to change, coupled with a small amount of code to apply the change. As an added benefit of this approach over previous work, the synthesized data structure is optimized for not only the queries in the specification but also the required update operations. Cozy has three goals: to reduce programmer effort, to produce bug-free code, and to match the performance of handwritten code. We have evaluated Cozy in four large case studies, demonstrating that it meets the goals. Using Cozy requires an order of magnitude fewer lines of code than manual implementation, results in fewer bugs, and matches the performance of handwritten code. Finally, we have used Cozy as an automatic incrementalizer. An incremental algorithm can update its output efficiently in response to small changes to its input. Replacing batch-style algorithms with incremental versions yields incredible speedups when the input data changes frequently. Cozy is well-suited to the problem since incrementalization is simply the task of finding the right data structure to track state between changes to the input. By re-framing the incremental computation task as a data structure specification we can produce efficient incremental versions in more situations than previous work