Leveraging Code Clones and Natural Language Processing for Log Statement Prediction

• URL: http://arxiv.org/abs/2109.03859v1
• Date: Wed, 8 Sep 2021 18:17:45 GMT
• Title: Leveraging Code Clones and Natural Language Processing for Log Statement Prediction
• Authors: Sina Gholamian
• Abstract summary: This research aims to predict the log statements by utilizing source code clones and natural language processing (NLP) Our work demonstrates the effectiveness of log-aware clone detection for automated log location and description prediction.
• 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. Prior research has emphasized the importance of logging statements in the operation and debugging of software systems. However, the current logging process is mostly manual and ad hoc, and thus, proper placement and content of logging statements remain as challenges. To overcome these challenges, methods that aim to automate log placement and log content, i.e., 'where, what, and how to log', are of high interest. Thus, we propose to accomplish the goal of this research, that is "to predict the log statements by utilizing source code clones and natural language processing (NLP)", as these approaches provide additional context and advantage for log prediction. We pursue the following four research objectives: (RO1) investigate whether source code clones can be leveraged for log statement location prediction, (RO2) propose a clone-based approach for log statement prediction, (RO3) predict log statement's description with code-clone and NLP models, and (RO4) examine approaches to automatically predict additional details of the log statement, such as its verbosity level and variables. For this purpose, we perform an experimental analysis on seven open-source java projects, extract their method-level code clones, investigate their attributes, and utilize them for log location and description prediction. Our work demonstrates the effectiveness of log-aware clone detection for automated log location and description prediction and outperforms the prior work.

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