A systematic literature review on logging smell detection

Abstract

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.