JSNice swMATH ID: 37814 Software Authors: Raychev, Veselin; Vechev, Martin; Krause, Andreas Description: Predicting program properties from “big code”. We present a new approach for predicting program properties from massive codebases (aka “big code”). Our approach first learns a probabilistic model from existing data and then uses this model to predict properties of new, unseen programs. The key idea of our work is to transform the input program into a representation which allows us to phrase the problem of inferring program properties as structured prediction in machine learning. This formulation enables us to leverage powerful probabilistic graphical models such as conditional random fields (CRFs) in order to perform joint prediction of program properties. As an example of our approach, we built a scalable prediction engine called JSNice for solving two kinds of problems in the context of JavaScript: predicting (syntactic) names of identifiers and predicting (semantic) type annotations of variables. Experimentally, JSNice predicts correct names for 63 Homepage: https://dl.acm.org/doi/10.1145/2676726.2677009 Keywords: big code; closure compiler; conditional random fields; JavaScript; names; program properties; structured prediction; types Related Software: Merlin; TypeScript; JavaScript Cited in: 1 Publication Standard Articles 1 Publication describing the Software, including 1 Publication in zbMATH Year Predicting program properties from “big code”. Zbl 1346.68063Raychev, Veselin; Vechev, Martin; Krause, Andreas 2015 Cited by 3 Authors 1 Krause, Andreas 1 Raychev, Veselin 1 Vechev, Martin T. Cited in 0 Serials Cited in 1 Field 1 Computer science (68-XX) Citations by Year