NDLI: Replicating parser behavior using neural machine translation

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Replicating parser behavior using neural machine translation

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Replicating parser behavior using neural machine translation

Content Provider	ACM Digital Library
Author	Panichella, Sebastiano Gall, Harald C. Alexandru, Carol V.
Abstract	More than other machine learning techniques, neural networks have been shown to excel at tasks where humans traditionally outperform computers: recognizing objects in images, distinguishing spoken words from background noise or playing "Go". These are hard problems, where hand-crafting solutions is rarely feasible due to their inherent complexity. Higher level program comprehension is not dissimilar in nature: while a compiler or program analysis tool can extract certain facts from (correctly written) code, it has no intrinsic 'understanding' of the data and for the majority of real-world problems, a human developer is needed - for example to find and fix a bug or to summarize the bahavior of a method. We perform a pilot study to determine the suitability of neural machine translation (NMT) for processing plain-text source code. We find that, on one hand, NMT is too fragile to accurately tokenize code, while on the other hand, it can precisely recognize different types of tokens and make accurate guesses regarding their relative position in the local syntax tree. Our results suggest that NMT may be exploited for annotating and enriching out-of-context code snippets to support automated tooling for code comprehension problems. We also identify several challenges in applying neural networks to learning from source code and determine key differences between the application of existing neural network models to source code instead of natural language.
Starting Page	316
Ending Page	319
Page Count	4
File Format	PDF
ISBN	9781538605356
DOI	10.1109/ICPC.2017.11
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2017-05-20
Access Restriction	Subscribed
Content Type	Text
Resource Type	Article

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