Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Using Categorical Features in Mining Bug Tracking Systems to Assign Bug
  Reports   Most bug assignment approaches utilize text classification and information
retrieval techniques. These approaches use the textual contents of bug reports
to build recommendation models. The textual contents of bug reports are usually
of high dimension and noisy source of information. These approaches suffer from
low accuracy and high computational needs. In this paper, we investigate
whether using categorical fields of bug reports, such as component to which the
bug belongs, are appropriate to represent bug reports instead of textual
description. We build a classification model by utilizing the categorical
features, as a representation, for the bug report. The experimental evaluation
is conducted using three projects namely NetBeans, Freedesktop, and Firefox. We
compared this approach with two machine learning based bug assignment
approaches. The evaluation shows that using the textual contents of bug reports
is important. In addition, it shows that the categorical features can improve
the classification accuracy.

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The categories of this paper are: chao-dyn
36
The categories of this paper are: cmp-lg
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The categories of this paper are: q-bio
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The categories of this paper are: hep-th
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The categories of this paper are: dg-ga
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The categories of this paper are: adap-org
3
The categories of this paper are: physics
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The categories of this paper are: atom-ph
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The categories of this paper are: supr-con
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The categories of this paper are: acc-phys
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The categories of this paper are: quant-ph
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The categories of this paper are: nucl-ex
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The categories of this paper are: cond-mat
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The categories of this paper are: patt-sol
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The categories of this paper are: comp-gas
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The categories of this paper are: chem-ph