From Owicki-Gries' Resource Invariants and Jones' Rely/Guarantee to modern variants based on Separation Logic, axiomatic logics for concurrency require auxiliary state to explicitly relate the effect of all threads to the global invariant on the shared resource. Unfortunately, auxiliary state gives the proof of an individual thread access to the auxiliaries of all other threads. This makes proofs sensitive to the global context, which prevents local reasoning and compositionality.

Self-adjusting computation provides an evaluation model where computations can respond automatically to modifications to their data by using a mechanism for propagating modifications through the computation. Current approaches to self-adjusting computation guarantee correctness by recording dependencies in a trace at the granularity of individual memory operations. Tracing at the granularity of memory operations, however, has some limitations: it can be asymptotically inefficient (\eg, compared to optimal solutions) because it cannot take advantage of problem-specific structure, it requires keeping a large computation trace (often proportional to the runtime of the program on the current input), and it introduces moderately large constant factors in practice.

Self-adjusting computation is an evaluation model in which programs can respond efficiently to small changes to their input data by using a change-propagation mechanism that updates computation by re-building only the parts affected by changes. Previous work has proposed language techniques for self-adjusting computation and showed the approach to be effective in a number of application areas. However, due to the complex semantics of change propagation and the indirect nature of previously proposed language techniques, it remains difficult to reason about the efficiency of self-adjusting programs and change propagation.

Self-adjusting programs respond automatically and efficiently to input changes by tracking the dynamic data dependences of the computation and incrementally updating the output as needed. In order to identify data dependences, previously proposed approaches require the user to make use of a set of monadic primitives. Rewriting an ordinary program into a self-adjusting program with these primitives, however, can be difficult and error-prone due to various monadic and proper-usage restrictions, some of which cannot be enforced statically. Previous work therefore suggests that self-adjusting computation would benefit from direct language and compiler support.

As XML-based messages have become common in many client-server protocols, there is a need to protect application servers from invalid or dangerous messages. This leads to the XML stream firewalling problem; that of applying integrity constraints against a large number of simultaneous streams. We conduct the first investigation of a constraint engine optimized for the generation of XML stream firewalls. We isolate a class of DTDs and XPath constraints which support the generation of low-space filters, and provide algorithms for generating firewalls with low per-input-character time and per-stream space. We give experimental results which show that we have achieved these goals in practice.