Borrowing from Similar Code: A Deep Learning NLP-Based Approach for Log
Statement Automation
- URL: http://arxiv.org/abs/2112.01259v1
- Date: Thu, 2 Dec 2021 14:03:49 GMT
- Title: Borrowing from Similar Code: A Deep Learning NLP-Based Approach for Log
Statement Automation
- Authors: Sina Gholamian and Paul A. S. Ward
- Abstract summary: We introduce an updated and improved log-aware code-clone detection method to predict the location of logging statements.
We incorporate natural language processing (NLP) and deep learning methods to automate the log statements' description prediction.
Our analysis shows that our hybrid NLP and code-clone detection approach (NLP CC'd) outperforms conventional clone detectors in finding log statement locations.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: 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).
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