We present 1) a novel linguistic engine made of configurable linguistic components for understanding natural language use case specification; and 2) results of the first of a kind large scale experiment of application of linguistic techniques to industrial use cases. Requirement defects are well known to have adverse effects on dependability of software systems. While formal techniques are often cited as a remedy for specification errors, natural language remains the predominant mode for specifying requirements. Therefore, for dependable system development, a natural language processing technique is required that can translate natural language textual requirements into validation ready computer models. In this paper, we present the implementation details of such a technique and the results of applying a prototype implementation of our technique to 80 industrial and academic use case descriptions. We report on the accuracy and effectiveness of our technique. The results of our experiment are very encouraging.

Applications often have large runtime memory requirements. In some cases, large memory footprint helps accomplish an important functional, performance, or engineering requirement. A large cache, for example, may ameliorate a pernicious performance problem. In general, however, finding a good balance between memory consumption and other requirements is quite challenging. To do so, the development team must distinguish effective from excessive use of memory: when is a data structure too big for its own good?We introduce health signatures to facilitate this balance. Using data from dozens of applications and benchmarks, we show that they provide concise and application-neutral summaries of footprint. We show how to use them to form value judgments about whether a design or implementation choice is good or bad. We demonstrate how to use health signatures to evaluate the asymptotic behavior of these choices, as input data size scales up. Finally, we show how being independent of any application eases comparison across disparate implementations.