FastLog: An End-to-End Method to Efficiently Generate and Insert Logging Statements

• URL: http://arxiv.org/abs/2311.02862v2
• Date: Fri, 29 Mar 2024 11:56:27 GMT
• Title: FastLog: An End-to-End Method to Efficiently Generate and Insert Logging Statements
• Authors: Xiaoyuan Xie, Zhipeng Cai, Songqiang Chen, Jifeng Xuan,
• Abstract summary: We propose FastLog, which can support the complete logging statement generation and insertion activity. FastLog first predicts the insertion position in the finest token level, and then generates a complete logging statement to insert. A comprehensive empirical analysis shows that our method outperforms the state-of-the-art approach in both efficiency and output quality.
• Score: 5.80502312468937
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Logs play a crucial role in modern software systems, serving as a means for developers to record essential information for future software maintenance. As the performance of these log-based maintenance tasks heavily relies on the quality of logging statements, various works have been proposed to assist developers in writing appropriate logging statements. However, these works either only support developers in partial sub-tasks of this whole activity; or perform with a relatively high time cost and may introduce unwanted modifications. To address their limitations, we propose FastLog, which can support the complete logging statement generation and insertion activity, in a very speedy manner. Specifically, given a program method, FastLog first predicts the insertion position in the finest token level, and then generates a complete logging statement to insert. We further use text splitting for long input texts to improve the accuracy of predicting where to insert logging statements. A comprehensive empirical analysis shows that our method outperforms the state-of-the-art approach in both efficiency and output quality, which reveals its great potential and practicality in current real-time intelligent development environments.

Related papers

• Studying and Benchmarking Large Language Models For Log Level Suggestion [49.176736212364496]
  Large Language Models (LLMs) have become a focal point of research across various domains. This paper investigates the impact of characteristics and learning paradigms on the performance of 12 open-source LLMs in log level suggestion.
  arXiv  Detail & Related papers  (2024-10-11T03:52:17Z)
• Log Statements Generation via Deep Learning: Widening the Support Provided to Developers [16.079459379684554]
  LANCE is an approach rooted in deep learning (DL) that has demonstrated the ability to correctly inject a log statement into Java methods. We present LEONID, a DL-based technique that can distinguish between methods that do and do not require the inclusion of log statements.
  arXiv  Detail & Related papers  (2023-11-08T10:31:18Z)
• 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)
• Are They All Good? Studying Practitioners' Expectations on the Readability of Log Messages [18.823475517909884]
  Despite the importance of log messages, there is still a lack of standards on what constitutes good readability in log messages. We conduct a series of interviews with 17 industrial practitioners to investigate their expectations on the readability of log messages. We find that both deep learning and machine learning models can effectively classify the readability of log messages with a balanced accuracy above 80.0% on average.
  arXiv  Detail & Related papers  (2023-08-17T07:53:24Z)
• LongCoder: A Long-Range Pre-trained Language Model for Code Completion [56.813974784131624]
  LongCoder employs a sliding window mechanism for self-attention and introduces two types of globally accessible tokens. Bridge tokens are inserted throughout the input sequence to aggregate local information and facilitate global interaction. memory tokens are included to highlight important statements that may be invoked later and need to be memorized.
  arXiv  Detail & Related papers  (2023-06-26T17:59:24Z)
• Unified Pretraining Framework for Document Understanding [52.224359498792836]
  We present UDoc, a new unified pretraining framework for document understanding. UDoc is designed to support most document understanding tasks, extending the Transformer to take multimodal embeddings as input. An important feature of UDoc is that it learns a generic representation by making use of three self-supervised losses.
  arXiv  Detail & Related papers  (2022-04-22T21:47:04Z)
• 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)
• Borrowing from Similar Code: A Deep Learning NLP-Based Approach for Log Statement Automation [0.0]
  We introduce an updated and improved log-aware code-clone detection method to predict the location of logging statements. We incorporate natural language processing (NLP) and deep learning methods to automate the log statements' description prediction. Our analysis shows that our hybrid NLP and code-clone detection approach (NLP CC'd) outperforms conventional clone detectors in finding log statement locations.
  arXiv  Detail & Related papers  (2021-12-02T14:03:49Z)
• 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.