Taxonomy of Software Log Smells

• URL: http://arxiv.org/abs/2412.09284v1
• Date: Thu, 12 Dec 2024 13:54:17 GMT
• Title: Taxonomy of Software Log Smells
• Abstract summary: The goal of this study is to create a taxonomy of log smells that can help developers write better logging code.<n>We identified logging issues and tools by conducting a survey of the scientific literature.<n>The paper presents a taxonomy of ten log smells, describing several facets for each of them.
• Score: 5.810137118676013
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Background: Logging is an important part of modern software projects; logs are used in several tasks such as debugging and testing. Due to the complex nature of logging, it remains a difficult task with several pitfalls that could have serious consequences. Several other domains of software engineering have mitigated such threats by identifying the early signs of more serious issues, i.e., "smells". However, this concept is not yet properly defined for logging. Objective: The goal of this study is to create a taxonomy of log smells that can help developers write better logging code. To further help the developers and to identify issues that need more attention from the research community, we also map the identified smells to existing tools addressing them. Methods: We identified logging issues and tools by conducting a survey of the scientific literature. After extracting relevant data from 45 articles, we used them to define logging issues using open coding technique and classified the defined issues using card sorting. We classify the tools based on their reported output. Results: The paper presents a taxonomy of ten log smells, describing several facets for each of them. We also review existing tools addressing some of these facets, highlighting the lack of tools addressing some log smells and identifying future research opportunities to close this gap.

Related papers

• Empirical Characterization of Logging Smells in Machine Learning Code [7.520925824033758]
  We present an empirical study of logging smells in machine learning (ML) projects.<n>We identify 12 categories of logging smells spanning security, metric management, configuration, verbosity, and context-related issues.<n>Our results show that logging smells are widespread in ML systems and vary in frequency and manifestation across projects.
  arXiv  Detail & Related papers  (2026-03-24T23:01:40Z)
• Empirical Characterization of Logging Smells in Machine Learning Code [7.520925824033758]
  This study aims to empirically identify and characterize logging smells in machine learning systems.<n>We propose to conduct a large-scale mining of open-source ML repositories hosted on GitHub to catalogue recurring logging smells.
  arXiv  Detail & Related papers  (2026-01-09T05:48:42Z)
• Query Logs Analytics: A Aystematic Literature Review [0.0]
  This paper presents a systematic survey of log usage, focusing on Database (DB), Data Warehouse (DW), Web, and KG logs.<n>More than 300 publications were analyzed to address three central questions: do different types of logs share common structural and functional characteristics?<n>The survey reveals a limited number of end-to-end approaches, the absence of standardization across log usage pipelines, and the existence of shared structural elements among different types of logs.
  arXiv  Detail & Related papers  (2025-08-19T15:38:13Z)
• Log2graphs: An Unsupervised Framework for Log Anomaly Detection with Efficient Feature Extraction [1.474723404975345]
  High cost of manual annotation and dynamic nature of usage scenarios present major challenges to effective log analysis. This study proposes a novel log feature extraction model called DualGCN-LogAE, designed to adapt to various scenarios. We also introduce Log2graphs, an unsupervised log anomaly detection method based on the feature extractor.
  arXiv  Detail & Related papers  (2024-09-18T11:35:58Z)
• RAGLog: Log Anomaly Detection using Retrieval Augmented Generation [0.0]
  We explore the use of a Retrieval Augmented Large Language Model that leverages a vector database to detect anomalies from logs. To the best of our knowledge, our experiment which we called RAGLog is a novel one and the experimental results show much promise.
  arXiv  Detail & Related papers  (2023-11-09T10:40:04Z)
• GLAD: Content-aware Dynamic Graphs For Log Anomaly Detection [49.9884374409624]
  GLAD is a Graph-based Log Anomaly Detection framework designed to detect anomalies in system logs. We introduce GLAD, a Graph-based Log Anomaly Detection framework designed to detect anomalies in system logs.
  arXiv  Detail & Related papers  (2023-09-12T04:21:30Z)
• A Large-Scale Evaluation for Log Parsing Techniques: How Far Are We? [42.56249610409624]
  We provide a new collection of annotated log datasets, denoted Loghub-2.0, which can better reflect the characteristics of log data in real-world software systems. We conduct a thorough re-evaluation of 15 state-of-the-art logs in a more rigorous and practical setting. Particularly, we introduce a new evaluation metric to mitigate the sensitivity of existing metrics to imbalanced data distributions.
  arXiv  Detail & Related papers  (2023-08-21T16:24:15Z)
• Log Parsing Evaluation in the Era of Modern Software Systems [47.370291246632114]
  We focus on one integral part of automated log analysis, log parsing, which is the prerequisite to deriving any insights from logs. Our investigation reveals problematic aspects within the log parsing field, particularly its inefficiency in handling heterogeneous real-world logs. We propose a tool, Logchimera, that enables estimating log parsing performance in industry contexts.
  arXiv  Detail & Related papers  (2023-08-17T14:19:22Z)
• EvLog: Identifying Anomalous Logs over Software Evolution [31.46106509190191]
  We propose a novel unsupervised approach named Evolving Log extractor (EvLog) to process logs without parsing. EvLog implements an anomaly discriminator with an attention mechanism to identify the anomalous logs and avoid the issue brought by the unstable sequence. EvLog has shown effectiveness in two real-world system evolution log datasets with an average F1 score of 0.955 and 0.847 in the intra-version setting and inter-version setting, respectively.
  arXiv  Detail & Related papers  (2023-06-02T12:58:00Z)
• LogLG: Weakly Supervised Log Anomaly Detection via Log-Event Graph Construction [31.31712326361932]
  We propose a novel weakly supervised log anomaly detection framework, named LogLG, to explore the semantic connections among keywords from sequences. Specifically, we design an end-to-end iterative process, where the keywords of unlabeled logs are first extracted to construct a log-event graph. Then, we build a subgraph annotator to generate pseudo labels for unlabeled log sequences.
  arXiv  Detail & Related papers  (2022-08-23T09:32:19Z)
• Data-Driven Approach for Log Instruction Quality Assessment [59.04636530383049]
  There are no widely adopted guidelines on how to write log instructions with good quality properties. We 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. 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.
  arXiv  Detail & Related papers  (2022-04-06T07:02:23Z)
• AI based Log Analyser: A Practical Approach [0.0]
  The analysis of logs is a vital activity undertaken for fault or cyber forensics incident detection, investigation and technical analysis for system and cyber resilience. The potential application of AI algorithms for Log analysis could augment such complex and laborious tasks. This research seeks to address these challenges with the use of Transformer construct to train a new model with only normal log entries.
  arXiv  Detail & Related papers  (2022-03-18T07:57:48Z)
• Robust and Transferable Anomaly Detection in Log Data using Pre-Trained Language Models [59.04636530383049]
  Anomalies or failures in large computer systems, such as the cloud, have an impact on a large number of users. We propose a framework for anomaly detection in log data, as a major troubleshooting source of system information.
  arXiv  Detail & Related papers  (2021-02-23T09:17:05Z)
• Self-Supervised Log Parsing [59.04636530383049]
  Large-scale software systems generate massive volumes of semi-structured log records. Existing approaches rely on log-specifics or manual rule extraction. We propose NuLog that utilizes a self-supervised learning model and formulates the parsing task as masked language modeling.
  arXiv  Detail & Related papers  (2020-03-17T19:25:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.