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We present an approach to learn contracts for object-oriented programs where guarantees of correctness of the contracts are made with respect to a test generator. Our contract synthesis approach is based on a novel notion of tight contracts and an online learning algorithm that works in tandem with a test generator to synthesize tight contracts. We implement our approach in a tool called Precis and evaluate it on a suite of programs written in C#, studying the safety and strength of the synthesized contracts, and compare them to those synthesized by Daikon.

Nonspecific shoulder pain is common presentation among elderly patients and patients with a history of cancer. The major concern of shoulder pain in cancer patients is the development of metastases. MRI of the shoulder is the most commonly used imaging modality for assessment of suspected soft tissue pathologies associated with the shoulder pain. In this study, we try to examine the role of shoulder MRI in elderly patients with a history of cancer presenting with nonspecific shoulder complaints for assessment of metastases.