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Many programming tasks require using both domain-specific code andwell-established patterns (such as routines concerned with file IO). Together,several small patterns combine to create complex interactions. This compoundingeffect, mixed with domain-specific idiosyncrasies, creates a challengingenvironment for fully automatic specification inference. Mining specificationsin this environment, without the aid of rule templates, user-directed feedback,or predefined API surfaces, is a major challenge. We call this challengeOpen-World Specification Mining.  In this paper, we present a framework for mining specifications and usagepatterns in an Open-World setting. We design this framework to beminer-agnostic and instead focus on disentangling complex and noisy APIinteractions. To evaluate our framework, we introduce a benchmark of 71clusters extracted from five open-source projects. Using this dataset, we showthat interesting clusters can be recovered, in a fully automatic way, byleveraging unsupervised learning in the form of word embeddings. Once clustershave been recovered, the challenge of Open-World Specification Mining issimplified and any trace-based mining technique can be applied. In addition, weprovide a comprehensive evaluation of three word-vector learners to showcasethe value of sub-word information for embeddings learned in thesoftware-engineering domain.

Enabling Open-World Specification Mining via Unsupervised Learning