Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Borrowing from Similar Code: A Deep Learning NLP-Based Approach for Log
  Statement Automation   Software developers embed logging statements inside the source code as an
imperative duty in modern software development as log files are necessary for
tracking down runtime system issues and troubleshooting system management
tasks. However, the current logging process is mostly manual, and thus, proper
placement and content of logging statements remain as challenges. To overcome
these challenges, methods that aim to automate log placement and predict its
content, i.e., 'where and what to log', are of high interest. Thus, we focus on
predicting the location (i.e., where) and description (i.e., what) for log
statements by utilizing source code clones and natural language processing
(NLP), as these approaches provide additional context and advantage for log
prediction. Specifically, we guide our research with three research questions
(RQs): (RQ1) how similar code snippets, i.e., code clones, can be leveraged for
log statements prediction? (RQ2) how the approach can be extended to automate
log statements' descriptions? and (RQ3) how effective the proposed methods are
for log location and description prediction? To pursue our RQs, we perform an
experimental study on seven open-source Java projects. We introduce an updated
and improved log-aware code-clone detection method to predict the location of
logging statements (RQ1). Then, we incorporate natural language processing
(NLP) and deep learning methods to automate the log statements' description
prediction (RQ2). Our analysis shows that our hybrid NLP and code-clone
detection approach (NLP CC'd) outperforms conventional clone detectors in
finding log statement locations on average by 15.60% and achieves 40.86% higher
performance on BLEU and ROUGE scores for predicting the description of logging
statements when compared to prior research (RQ3).

34
The categories of this paper are: chem-ph
10
The categories of this paper are: q-bio
23
The categories of this paper are: solv-int
12
The categories of this paper are: nucl-th
35
The categories of this paper are: mtrl-th
18
The categories of this paper are: econ
27
The categories of this paper are: comp-gas
31
The categories of this paper are: ao-sci
30
The categories of this paper are: q-alg
20
The categories of this paper are: acc-phys
3
The categories of this paper are: physics
26
The categories of this paper are: bayes-an
0
The categories of this paper are: hep-ph
19
The categories of this paper are: eess
36
The categories of this paper are: cmp-lg
7
The categories of this paper are: hep-th