Adaptive Logging System: A System Using Reinforcement Learning For Log Placement

The efficient management of software logs plays a key role in software development, as it allows for the examination of runtime information for post-execution analysis. Given the significance of logs and the possibility that developers may not possess the necessary knowledge to make informed logging decisions, it is important to have a robust log-placement framework that supports developers. Prior attempts to address this challenge have proposed various frameworks, however, these frameworks are either limited to a single logging objective or rely on methods that exhibit poor cross-project consistency. This study introduces a novel performance logging objective to capture and reveal performance bugs, and presents an adaptive software logging approach based on reinforcement learning, which can adapt to multiple logging objectives. This framework is not limited to a specific project and shows superior cross-project accuracy.

Name thesis:degree_name

M.Sc. Computer Science

Level thesis:degree_level

Masters

Discipline thesis:degree_discipline

Faculty of Mathematics and Science

Department dc:contributor.department

Department of Computer Science

Grantor

Brock University

Year dc:date.issued

2023

Author dc:creator

• Khosravi Tabrizi, Amirmahdi

• Software Logging
• Performance Bugs
• Reinforcement Learning
• Software Engineering

Statement dc:rights

• CC0 1.0 Universal

Licence dc:rights.uri

• http://creativecommons.org/publicdomain/zero/1.0/

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/10464/18173

OAI identifier oai:identifier

oai:brocku.scholaris.ca:10464/18173