Predicting Program Properties from 'Big Code'

Communications of the ACM

We present a new approach for predicting program properties from large codebases (aka "Big Code"). Our approach learns a probabilistic model from "Big Code" and uses this model to predict properties of new, unseen programs. The key idea of our work is to transform the program into a representation that allows us to formulate the problem of inferring program properties as structured prediction in machine learning. This enables us to leverage powerful probabilistic models such as Conditional Random Fields (CRFs) and 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 tasks in the context of JavaScript: predicting (syntactic) names of identifiers and predicting (semantic) type annotations of variables. Experimentally, JSNICE predicts correct names for 63% of name identifiers and its type annotation predictions are correct in 81% of cases. Since its public release at, JSNice has become a popular system with hundreds of thousands of uses. By formulating the problem of inferring program properties as structured prediction, our work opens up the possibility for a range of new "Big Code" applications such as de-obfuscators, decompilers, invariant generators, and others. Recent years have seen significant progress in the area of programming languages driven by advances in type systems, constraint solving, program analysis, and synthesis techniques. Fundamentally, these methods reason about each program in isolation and while powerful, the effectiveness of programming tools based on these techniques is approaching its inherent limits. Thus, a more disruptive change is needed if a significant improvement is to take place. At the same time, creating probabilistic models from large datasets (also called "Big Data") has transformed a number of areas such as natural language processing, computer vision, recommendation systems, and many others. However, despite the overwhelming success of "Big Data" in a variety of application domains, learning from large datasets of programs has previously not had tangible impact on programming tools.

Statistical Machine Translation Is a Natural Fit for Automatic Identifier Renaming in Software Source Code

AAAI Conferences

Advances in natural language processing have led to a variety of successful tools and techniques for solving problems such as understanding, generating, and translating natural languages. Given the success of these techniques, a natural question is whether they can also be applied to programming languages. However, the initial research has been mixed. Researchers attempting to translate between programming languages by employing statistical machine translation (SMT) found that a large percentage of the translated programs were not syntactically valid. On the other hand, SMT has been successfully employed to recover identifiers in obfuscated JavaScript code. In this paper, we discuss several differences between natural languages and programming languages that can thwart successful application of NLP techniques to program transformation. We also discuss several strategies to cope with these differences in practice, using our own experiences with using SMT to assign meaningful identifier names to variables in decompiled C programs as an example.

Context2Name: A Deep Learning-Based Approach to Infer Natural Variable Names from Usage Contexts Machine Learning

Most of the JavaScript code deployed in the wild has been minified, a process in which identifier names are replaced with short, arbitrary and meaningless names. Minified code occupies less space, but also makes the code extremely difficult to manually inspect and understand. This paper presents Context2Name, a deep learningbased technique that partially reverses the effect of minification by predicting natural identifier names for minified names. The core idea is to predict from the usage context of a variable a name that captures the meaning of the variable. The approach combines a lightweight, token-based static analysis with an auto-encoder neural network that summarizes usage contexts and a recurrent neural network that predict natural names for a given usage context. We evaluate Context2Name with a large corpus of real-world JavaScript code and show that it successfully predicts 47.5% of all minified identifiers while taking only 2.9 milliseconds on average to predict a name. A comparison with the state-of-the-art tools JSNice and JSNaughty shows that our approach performs comparably in terms of accuracy while improving in terms of efficiency. Moreover, Context2Name complements the state-of-the-art by predicting 5.3% additional identifiers that are missed by both existing tools.

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Tree-to-tree Neural Networks for Program Translation

Neural Information Processing Systems

Program translation is an important tool to migrate legacy code in one language into an ecosystem built in a different language. In this work, we are the first to employ deep neural networks toward tackling this problem. We observe that program translation is a modular procedure, in which a sub-tree of the source tree is translated into the corresponding target sub-tree at each step. To capture this intuition, we design a tree-to-tree neural network to translate a source tree into a target one. Meanwhile, we develop an attention mechanism for the tree-to-tree model, so that when the decoder expands one non-terminal in the target tree, the attention mechanism locates the corresponding sub-tree in the source tree to guide the expansion of the decoder. We evaluate the program translation capability of our tree-to-tree model against several state-of-the-art approaches. Compared against other neural translation models, we observe that our approach is consistently better than the baselines with a margin of up to 15 points. Further, our approach can improve the previous state-of-the-art program translation approaches by a margin of 20 points on the translation of real-world projects.