Contracts are a popular tool for specifying the functional behavior of software. This paper characterizes the contracts that developers write, the contracts that developers could write, and how a developer reacts when shown the difference.

Formally verifying a program requires significant skill not only because of complex interactions between program subcomponents, but also because of deficiencies in current verification interfaces. These skill barriers make verification economically unattractive by preventing the use of less-skilled (less-expensive) workers and distributed workflows (i.e., crowdsourcing). This paper presents VeriWeb, a web-based IDE for verification that decomposes the task of writing verifiable specifications into manageable subproblems. To overcome the information loss caused by task decomposition, and to reduce the skill required to verify a program, VeriWeb incorporates several innovative user interface features: drag and drop condition construction, concrete counterexamples, and specification inlining.

This paper describes practical experience building and using pluggable type-checkers. A pluggable type-checker refines (strengthens) the built-in type system of a programming language. This permits programmers to detect and prevent, at compile time, defects that would otherwise have been manifested as run-time errors. The prevented defects may be generally applicable to all programs, such as null pointer dereferences. Or, an application-specific pluggable type system may be designed for a single application.

Patients undergoing thoracic radiation therapy can develop radiation pneumonitis (RP), a potentially fatal inflammation of the lungs. Support vector machines (SVMs), a statistical machine learning method, have recently been used to build binary-outcome RP prediction models with promising results. In this work, we (1) introduce a feature-ranking selection step to limit complexity in ensemble SVM models (2) show that ensembles of SVMs provide a statistically significant performance improvement in the area under the cross-validated receiver operating curve and (3) apply Platt's tuning to generate probability estimates from the component SVMs in order to augment clinical relevance.