From description to prescription: Unraveling log severity adjustments in open-source software

Context: Logs are vital to understanding a software system's behavior, often being the only evidence available to investigate failures. Problem: Selecting a Log Severity Level (LSL) can be challenging for the following reasons: (i) the absence of knowledge about how logs are used in production, (ii) the lack of understanding of how critical an event is, and (iii) the lack of practical guidelines. This leads to frequent LSL adjustments during software development and evolution. Objective: Our goal is to investigate the LSL adjustments between system releases and explore methods to improve LSL classification. Methods: We analyzed the log statements from different releases of open-source systems, focusing on their LSL adjustments and examining the commit comments to understand the reasons for the adjustments. Results: Our results show that most adjustments occur at the intersection of development and production environment logs. Furthermore, the main guiding factors for the adjustments are the experience and logging theory. Our contributions are (i) a description of trends and patterns in LSL adjustments and (ii) a set of 24 heuristics to guide the choice, review, and adjustments of LSL. We advise developers to adhere to the LSL purposes, routinely review LSL settings, and remain adaptable to their mutability.