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For an ML model to learn and generalize, there is a need for a large and high-quality dataset. Therefore, we created the ManyTypes4Py dataset which contains 5.2K Python projects and 4.2M type annotations. To avoid data leakage from the training set to the test set, we used our CD4Py tool to perform code de-duplication in the dataset. To know more about how we created the dataset, check out its paper .
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As with most ML tasks, we need to find a set of relevant features in order to predict type annotations. Here, we consider features as type hints. Specifically, we extract three kinds of type hints, namely, identifiers, code context, and visible type hints (VTHs).
To learn from the extracted...
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We have two different environments for development and production as it is common in software development. In the development env., we test, debug, and profile Type4Py’s server-side components before releasing new features/fixes into the production code.
Also, whenever we train a new Type4...
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To extract type hints, we first extract Abstract Syntax Trees (ASTs) and perform light-weight static analysis using our LibSA4Py package. NLP tasks are applied using NLTK. To train the Word2Vec model, w...
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Dynamic programming languages like Python and TypeScript allows developers to optionally define type annotations and benefit from the advantages of static typing such as better code completion, early bug detection.
However, retrofitting types is a cumbersome and error-prone process. To addr...
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Before going into the detail of the Type4Py’s model and its implementation, it would be helpful to see the overview of Type4Py. In general, there is a VSCode extension at the client-side (developers) and the Type4Py model and its pipeline are deployed on our servers. Simply, the extension sends a...
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To deploy the Type4Py model for the production environment, we convert the pre-trained PyTorch model to an ONNX model which allows us to query the model on both GPUs and CPUs with very fast inference speed and low...
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The basic idea is that two RNN captures different aspects of input sequences from both identifiers and code context.
Next, the output of two RNNs is concatenated into a single vector, which is passed through a fully-connected linear layer.
The final linear layer maps the learned typ...
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The Type4Py’s VSCode extension is small and simple.
Type slots are functions parameters, return types, and variables, which are located based on the line and column numbers. Currently, type prediction can be triggered via Command Pallete or by enabling the AutoInfer setting, which predicts ...
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Pandas is a Python language package, which is used for data processing. This is a very common basic programming library when we use Python language for machine learning programming. This article is an introductory tutorial to it. Pandas provide fast, flexible and expressive data structures with t...
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