Um estudo preliminar sobre o uso de uma arquitetura deep learning para seleção de respostas no problema de recuperação de código-fonte

• Marcelo de Rezende Martins IPT
• Marco Aurélio Gerosa NAU

DOI: https://doi.org/10.5753/vem.2019.7589

Abstract

Code retrieval techniques aim to select a code snippet to solve a question given a set of possible solutions. This article presents a preliminary study about a new approach for code retrieval, applying an answer selection deep learning architecture. We present the preliminary results of a bidirectional LSTM with convolutional network model adapted for code retrieval. After 20 runs on a evaluation dataset, our model could achieve a mean MRR of 0.60 ± 0.02 and a top-1 accuracy up to 51%.

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