Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning to Extend Program Graphs to Work-in-Progress Code   Source code spends most of its time in a broken or incomplete state during
software development. This presents a challenge to machine learning for code,
since high-performing models typically rely on graph structured representations
of programs derived from traditional program analyses. Such analyses may be
undefined for broken or incomplete code. We extend the notion of program graphs
to work-in-progress code by learning to predict edge relations between tokens,
training on well-formed code before transferring to work-in-progress code. We
consider the tasks of code completion and localizing and repairing variable
misuse in a work-in-process scenario. We demonstrate that training
relation-aware models with fine-tuned edges consistently leads to improved
performance on both tasks.

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The categories of this paper are: chao-dyn
3
The categories of this paper are: physics
2
The categories of this paper are: math
10
The categories of this paper are: q-bio
19
The categories of this paper are: eess
36
The categories of this paper are: cmp-lg
34
The categories of this paper are: chem-ph
25
The categories of this paper are: patt-sol
35
The categories of this paper are: mtrl-th
7
The categories of this paper are: hep-th
13
The categories of this paper are: quant-ph
11
The categories of this paper are: hep-lat
14
The categories of this paper are: math-ph
29
The categories of this paper are: funct-an
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The categories of this paper are: alg-geom
30
The categories of this paper are: q-alg