Go Static: Contextualized Logging Statement Generation

• URL: http://arxiv.org/abs/2402.12958v1
• Date: Tue, 20 Feb 2024 12:22:59 GMT
• Title: Go Static: Contextualized Logging Statement Generation
• Abstract summary: SCLogger is a contextualized logging statement generation approach with inter-method static contexts. SCLogger surpasses the state-of-the-art approach by 8.7% in logging position accuracy, 32.1% in level accuracy, 19.6% in variable precision, and 138.4% in text BLEU-4 score.
• Score: 38.15795803230719
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Logging practices have been extensively investigated to assist developers in writing appropriate logging statements for documenting software behaviors. Although numerous automatic logging approaches have been proposed, their performance remains unsatisfactory due to the constraint of the single-method input, without informative programming context outside the method. Specifically, we identify three inherent limitations with single-method context: limited static scope of logging statements, inconsistent logging styles, and missing type information of logging variables. To tackle these limitations, we propose SCLogger, the first contextualized logging statement generation approach with inter-method static contexts. First, SCLogger extracts inter-method contexts with static analysis to construct the contextualized prompt for language models to generate a tentative logging statement. The contextualized prompt consists of an extended static scope and sampled similar methods, ordered by the chain-of-thought (COT) strategy. Second, SCLogger refines the access of logging variables by formulating a new refinement prompt for language models, which incorporates detailed type information of variables in the tentative logging statement. The evaluation results show that SCLogger surpasses the state-of-the-art approach by 8.7% in logging position accuracy, 32.1% in level accuracy, 19.6% in variable precision, and 138.4% in text BLEU-4 score. Furthermore, SCLogger consistently boosts the performance of logging statement generation across a range of large language models, thereby showcasing the generalizability of this approach.

Related papers

• LLMEval-Logic: A Solver-Verified Chinese Benchmark for Logical Reasoning of LLMs with Adversarial Hardening [69.1037790901185]
  We present LLMEval-Logic, a Chinese logical reasoning benchmark built from realistic situational scenarios.<n>Its pipeline forward-authors and expert-audits natural-language items together with their reference formalizations, verifies annotated answers with Z3, constructs expert rubrics for natural-to-formal grading, and hardens selected items through a closed-loop adversarial workflow.
  arXiv  Detail & Related papers  (2026-05-19T09:40:29Z)
• A Comparative Study of Semantic Log Representations for Software Log-based Anomaly Detection [20.286101279636025]
  We benchmark widely used semantic log representation methods.<n>We propose QTyBERT, a novel semantic log representation method.<n>Our results show that QTyBERT-generated log embeddings achieves detection effectiveness comparable to or better than BERT-generated log embeddings.
  arXiv  Detail & Related papers  (2026-04-09T09:30:04Z)
• PDLogger: Automated Logging Framework for Practical Software Development [7.860311994179783]
  Existing automated logging techniques focus on isolated sub-tasks.<n>PDLogger is the first end-to-end log generation technique expressly designed for practical, multi-log scenarios.<n>It improves log-position precision by 139.0 percent, F1 by 69.2 percent, level accuracy by 82.3 percent, variable precision by 131.8 percent, and message quality (BERTScore) by 65.7 percent.
  arXiv  Detail & Related papers  (2025-07-26T13:35:57Z)
• AL-Bench: A Benchmark for Automatic Logging [3.8293110324859505]
  We introduce AL-Bench, a benchmark designed specifically for automatic logging tools. AL-Bench includes a large-scale, high-quality, diverse dataset collected from 10 widely recognized projects. It provides a run-time perspective of logging quality in addition to the traditional static evaluation at source code level.
  arXiv  Detail & Related papers  (2025-02-05T13:32:39Z)
• LogLLM: Log-based Anomaly Detection Using Large Language Models [8.03646578793411]
  We propose LogLLM, a log-based anomaly detection framework that leverages large language models (LLMs) LogLLM employs BERT for extracting semantic vectors from log messages, while utilizing Llama, a transformer decoder-based model, for classifying log sequences. Our framework is trained through a novel three-stage procedure designed to enhance performance and adaptability.
  arXiv  Detail & Related papers  (2024-11-13T12:18:00Z)
• LibreLog: Accurate and Efficient Unsupervised Log Parsing Using Open-Source Large Language Models [3.7960472831772774]
  This paper introduces LibreLog, an unsupervised log parsing approach that enhances privacy and reduces operational costs while achieving state-of-the-art parsing accuracy. Our evaluation on LogHub-2.0 shows that LibreLog achieves 25% higher parsing accuracy and processes 2.7 times faster compared to state-of-the-art LLMs.
  arXiv  Detail & Related papers  (2024-08-02T21:54:13Z)
• Log Probabilities Are a Reliable Estimate of Semantic Plausibility in Base and Instruction-Tuned Language Models [50.15455336684986]
  We evaluate the effectiveness of LogProbs and basic prompting to measure semantic plausibility. We find that LogProbs offers a more reliable measure of semantic plausibility than direct zero-shot prompting. We conclude that, even in the era of prompt-based evaluations, LogProbs constitute a useful metric of semantic plausibility.
  arXiv  Detail & Related papers  (2024-03-21T22:08:44Z)
• LogFormer: A Pre-train and Tuning Pipeline for Log Anomaly Detection [73.69399219776315]
  We propose a unified Transformer-based framework for Log anomaly detection (LogFormer) to improve the generalization ability across different domains. Specifically, our model is first pre-trained on the source domain to obtain shared semantic knowledge of log data. Then, we transfer such knowledge to the target domain via shared parameters.
  arXiv  Detail & Related papers  (2024-01-09T12:55:21Z)
• 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)
• 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)
• In-Context Learning for Few-Shot Dialogue State Tracking [55.91832381893181]
  We propose an in-context (IC) learning framework for few-shot dialogue state tracking (DST) A large pre-trained language model (LM) takes a test instance and a few annotated examples as input, and directly decodes the dialogue states without any parameter updates. This makes the LM more flexible and scalable compared to prior few-shot DST work when adapting to new domains and scenarios.
  arXiv  Detail & Related papers  (2022-03-16T11:58:24Z)
• 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)
• Leveraging Code Clones and Natural Language Processing for Log Statement Prediction [0.0]
  This research aims to predict the log statements by utilizing source code clones and natural language processing (NLP) Our work demonstrates the effectiveness of log-aware clone detection for automated log location and description prediction.
  arXiv  Detail & Related papers  (2021-09-08T18:17:45Z)
• 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.