日志气味检测的系统文献综述
A systematic literature review on logging smell detection
AI总结：
本文通过对21项相关研究的系统文献综述，梳理日志气味检测的技术、数据集与评估方法，指出现有研究存在分散不一致的问题，并提出未来改进方向。
AI中文摘要：
背景：日志是软件开发的重要组成部分，可帮助开发者监控系统、理解行为并修复问题。但日志记录不当会产生日志气味，这类缺陷会降低日志的可用性，甚至引发问题。目的：本研究调研当前日志气味的检测方式，旨在深入理解现有自动检测技术、数据集及评估方法的研究。方法：针对21项聚焦日志气味检测的研究开展系统文献综述（SLR），在综述中定义关键日志相关术语，将气味类型映射至现有分类体系，并分析研究中所用的检测技术、数据集与评估策略。结果：研究现状仍呈分散且不一致的状态，例如不存在通用基准或标准化评估方法，导致研究间难以比较；此外，日志气味的处理方式也存在不一致，不同研究针对的气味类型与数量存在差异。结论：日志气味的研究与检测仍有改进空间，本文指出若干挑战并提出未来方向，如开发更优质的工具、使用大语言模型（LLMs），以及构建更标准化的评估数据集。
英文摘要：
Context:Logging is an important part of software development that helps developers monitor systems, understand behavior, and fix problems. But when logging is done poorly, it can introduce logging smells, which are defects that reduce the usefulness of logs or even make them problematic. Objective:This study looks at how logging smells are currently detected. The goal is to better understand the existing research on automatic detection techniques, datasets, and evaluation methods. Method:We conducted a systematic literature review (SLR) of 21 studies focused on detecting logging smells. In this review, we define key logging-related terms, identify and map the types of smells to an existing taxonomy, and examine the detection techniques, datasets, and evaluation strategies used across the studies. Results:We found that the research is still scattered and inconsistent. For example, there is no common benchmark or standardized approach for evaluating results, making it difficult to compare studies. In addition, we observe inconsistencies in the way log smells are addressed, as studies differ in the types and number of smells they target. Conclusion:There is still room for improvement in how logging smells are studied and detected. We point out several challenges and suggest future directions, such as developing better tools, using large language models (LLMs), and building more standardized datasets for evaluation.
