Identifying Concurrency Bug Reports via Linguistic Patterns

• URL: http://arxiv.org/abs/2601.16338v1
• Date: Thu, 22 Jan 2026 21:54:14 GMT
• Title: Identifying Concurrency Bug Reports via Linguistic Patterns
• Authors: Shuai Shao, Lu Xiao, Tingting Yu,
• Abstract summary: This paper presents a linguistic-pattern-based framework for automatically identifying bug reports.<n>We derive 58 distinct linguistic patterns from 730 manually labeled bug reports.<n>We evaluate four complementary approaches-matching, learning, prompt-based, and fine-tuning-spanning traditional machine learning, large language models, and pre-trained language models.
• Score: 5.794959117360381
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the growing ubiquity of multi-core architectures, concurrent systems have become essential but increasingly prone to complex issues such as data races and deadlocks. While modern issue-tracking systems facilitate the reporting of such problems, labeling concurrency-related bug reports remains a labor-intensive and error-prone task. This paper presents a linguistic-pattern-based framework for automatically identifying concurrency bug reports. We derive 58 distinct linguistic patterns from 730 manually labeled concurrency bug reports, organized across four levels: word-level (keywords), phrase-level (n-grams), sentence-level (semantic), and bug report-level (contextual). To assess their effectiveness, we evaluate four complementary approaches-matching, learning, prompt-based, and fine-tuning-spanning traditional machine learning, large language models (LLMs), and pre-trained language models (PLMs). Our comprehensive evaluation on 12 large-scale open-source projects (10,920 issue reports from GitHub and Jira) demonstrates that fine-tuning PLMs with linguistic-pattern-enriched inputs achieves the best performance, reaching a precision of 91% on GitHub and 93% on Jira, and maintaining strong precision on post cut-off data (91%). The contributions of this work include: (1) a comprehensive taxonomy of linguistic patterns for concurrency bugs, (2) a novel fine-tuning strategy that integrates domain-specific linguistic knowledge into PLMs, and (3) a curated, labeled dataset to support reproducible research. Together, these advances provide a foundation for improving the automation, precision, and interpretability of concurrency bug classification.

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