Data-Driven Approach for Log Instruction Quality Assessment
This addresses the lack of guidelines for writing high-quality log messages, which can reduce confusion and maintenance costs for developers and system operators, though it is incremental as it builds on existing deep learning methods.
The paper tackles the problem of assessing log instruction quality in software development by automating the evaluation of correct log level assignment and sufficient linguistic structure, achieving an accuracy of 0.88 and an F1 score of 0.99 respectively.
In the current IT world, developers write code while system operators run the code mostly as a black box. The connection between both worlds is typically established with log messages: the developer provides hints to the (unknown) operator, where the cause of an occurred issue is, and vice versa, the operator can report bugs during operation. To fulfil this purpose, developers write log instructions that are structured text commonly composed of a log level (e.g., "info", "error"), static text ("IP {} cannot be reached"), and dynamic variables (e.g. IP {}). However, as opposed to well-adopted coding practices, there are no widely adopted guidelines on how to write log instructions with good quality properties. For example, a developer may assign a high log level (e.g., "error") for a trivial event that can confuse the operator and increase maintenance costs. Or the static text can be insufficient to hint at a specific issue. In this paper, we address the problem of log quality assessment and provide the first step towards its automation. We start with an in-depth analysis of quality log instruction properties in nine software systems and identify two quality properties: 1) correct log level assignment assessing the correctness of the log level, and 2) sufficient linguistic structure assessing the minimal richness of the static text necessary for verbose event description. Based on these findings, we developed a data-driven approach that adapts deep learning methods for each of the two properties. An extensive evaluation on large-scale open-source systems shows that our approach correctly assesses log level assignments with an accuracy of 0.88, and the sufficient linguistic structure with an F1 score of 0.99, outperforming the baselines. Our study shows the potential of the data-driven methods in assessing instructions quality and aid developers in comprehending and writing better code.