Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Improving the Robustness to Data Inconsistency between Training and
  Testing for Code Completion by Hierarchical Language Model   In the field of software engineering, applying language models to the token
sequence of source code is the state-of-art approach to build a code
recommendation system. The syntax tree of source code has hierarchical
structures. Ignoring the characteristics of tree structures decreases the model
performance. Current LSTM model handles sequential data. The performance of
LSTM model will decrease sharply if the noise unseen data is distributed
everywhere in the test suite. As code has free naming conventions, it is common
for a model trained on one project to encounter many unknown words on another
project. If we set many unseen words as UNK just like the solution in natural
language processing, the number of UNK will be much greater than the sum of the
most frequently appeared words. In an extreme case, just predicting UNK at
everywhere may achieve very high prediction accuracy. Thus, such solution
cannot reflect the true performance of a model when encountering noise unseen
data. In this paper, we only mark a small number of rare words as UNK and show
the prediction performance of models under in-project and cross-project
evaluation. We propose a novel Hierarchical Language Model (HLM) to improve the
robustness of LSTM model to gain the capacity about dealing with the
inconsistency of data distribution between training and testing. The newly
proposed HLM takes the hierarchical structure of code tree into consideration
to predict code. HLM uses BiLSTM to generate embedding for sub-trees according
to hierarchies and collects the embedding of sub-trees in context to predict
next code. The experiments on inner-project and cross-project data sets
indicate that the newly proposed Hierarchical Language Model (HLM) performs
better than the state-of-art LSTM model in dealing with the data inconsistency
between training and testing and achieves averagely 11.2\% improvement in
prediction accuracy.

28
The categories of this paper are: alg-geom
0
The categories of this paper are: hep-ph
15
The categories of this paper are: nucl-ex
36
The categories of this paper are: cmp-lg
33
The categories of this paper are: plasm-ph
10
The categories of this paper are: q-bio
13
The categories of this paper are: quant-ph
26
The categories of this paper are: bayes-an
5
The categories of this paper are: gr-qc
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The categories of this paper are: eess
6
The categories of this paper are: astro-ph
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The categories of this paper are: q-fin
20
The categories of this paper are: acc-phys
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The categories of this paper are: math-ph
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The categories of this paper are: comp-gas
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The categories of this paper are: adap-org