跳到正文
研踪PaperTrail

YOUR RESEARCH, IN SIGHT

让下一篇重要论文，不再错过。

覆盖计算机全学科，以 AI 为快捷入口。关注方向、作者和关键词，把发现变成持续的研究积累。

部分采集异常
正在读取账号与研究记录…

全站更新概况

+2新增论文
+2其中 AI 相关
+1已有论文修订
+100新增 AI 导读

arXiv 最近完成扫描：2026-11-12 18:21 · 全站最近入库：2026-11-12 06:37各信源进度 →

统计口径与数据新鲜度

统计 2026-11-11 19:06 — 2026-11-12 19:06（北京时间） · 点击数字查看对应简报；AI 相关包含在新增论文内。

最近完成检查：2026-11-12 18:21最近发现新增版本：2026-11-12 06:37库内论文最新更新时间：2026-11-11 08:00

每分钟刷新已保存的数据。空检查不代表采集异常；新收录可能包含历史论文。AI 导读按新增版本计数。

21,675 已收录论文18,323 AI 相关6,056 有修订112 HF 社区发现

浏览论文库

研究日报 / 周报 →按信源浏览 →

时间范围跟随排序：版本更新、本站收录或首次发表。最新收录可能包含历史论文。部分信源异常，查看状态 →

找到 3 篇论文 · 按版本更新时间排序上轮扫描起点 2026-11-12 18:18（北京时间）
新论文 · v1arXiv:2611.09162

Rethinking Requirement-to-Code Traceability Through Single-Agent and Multi-Agent Approaches

Sarthak Banerjee, Sallam Abualhaija, Domenico Bianculli, Lionel Briand

原文摘要

Requirements-to-code traceability supports essential software engineering activities, including change impact analysis, maintenance, and regulatory compliance. Existing approaches typically rely on a retrieval-and-classification pipeline, which suffers from an inherent retrieval bottleneck: relevant source files that are not retrieved cannot be recovered during subsequent analysis. In this paper, we revisit requirements-to-code traceability in light of the substantially larger context windows offered by modern large language models, which make repository-scale analysis feasible without a separate retrieval step. We propose three approaches: a single-agent approach that analyzes the entire code repository in one pass, and two multi-agent approaches based on self-verification and multi-agent debate. Our evaluation shows that the single-agent approach substantially improves recall, achieving 80.9%, a gain of 38.6 percentage points (pp) over the state-of-the-art retrieval-based baseline, albeit with a 25.8 pp reduction in precision. By leveraging collaborative reasoning, the multi-agent debate-based approach achieves the best overall precision-recall balance, with 24.5% precision and 71.0% recall, yielding an F2 score of 49.5%. This represents an improvement of 7.5 pp over the state of the art and 2.7 pp over the single-agent approach. While the quantitative evaluation establishes the overall effectiveness of the approaches, the aggregate metrics alone explain little due to the considerable variability across results. Importantly, qualitative analysis provides key insights into highly variable quantitative results: the retrieval approach suits requirements that echo the code's vocabulary; single-agent, full-context reasoning suits well-documented, consistently named code; and multi-agent debate suits indirect, conflicting, or distributed evidence.

cs.CE

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2607.03194

TATG: Tracking-Aware Testing Objective for LLM-based Test Generation

Guancheng Wang, Qinghua Xu, Lionel Briand

原文摘要

Complex Java methods challenge automated unit test generation, as achieving high coverage and fault detection requires satisfying intricate testing requirements involving program states, execution paths, and assertions. Existing LLM-based approaches leverage program context, static analysis, and dynamic feedback but lack explicit tracking of individual testing requirements across iterations, potentially leading to redundant generation and unresolved testing objectives. We present TATG, a tracking-aware LLM-based unit test generation framework that unifies structural coverage and mutation-based fault detection. TATG introduces a structured objective representation capturing testing requirements derived from static analysis and dynamic feedback, enabling fine-grained tracking of their satisfaction status. It employs a two-stage workflow that first improves structural coverage and subsequently strengthens assertions through mutation-guided hardening. We evaluate TATG on 141 complex Java methods against state-of-the-art baselines, including KTester and PANTA. Results show average improvements of 22.15, 20.14, and 37.66 percentage points in line coverage, branch coverage, and mutation score, respectively. TATG also outperforms general-purpose coding assistants, including Codex and Claude Code, under the same open-weight model configurations, achieving higher average coverage and mutation scores while generating fewer tests and consuming fewer tokens. These findings highlight the value of explicitly tracking testing objectives for effective LLM-based test generation.

cs.SE

查看论文
arXiv ↗PDF ↗
版本更新 · v3arXiv:2601.08024

A Highly Efficient Diversity-based Input Selection for DNN Improvement Using VLMs

Amin Abbasishahkoo, Mahboubeh Dadkhah, Lionel Briand

原文摘要

Maintaining or improving the performance of Deep Neural Networks (DNNs) through fine-tuning requires labeling newly collected inputs, a process that is often costly and time-consuming. To alleviate this problem, input selection approaches have been developed in recent years to identify small, yet highly informative subsets for labeling. Diversity-based selection is one of the most effective approaches for this purpose. However, they are often computationally intensive and lack scalability for large input sets, limiting their practical applicability. To address this challenge, we introduce Concept-Based Diversity (CBD), a novel and highly efficient diversity metric for image inputs that leverages Vision-Language Models (VLMs). Our results show that CBD exhibits a strong correlation with Geometric Diversity (GD), an established diversity metric, while requiring only a fraction of its computation time. Building on this finding, we propose a hybrid input selection approach that combines CBD with Margin, a simple uncertainty metric. We conduct a comprehensive evaluation across a diverse set of DNN models, input sets, selection budgets, and six most effective state-of-the-art selection baselines. The results demonstrate that the CBD-based selection consistently outperforms all baselines at guiding input selection to improve the DNN model. Furthermore, the CBD-based selection approach remains highly efficient, requiring selection times close to those of simple uncertainty-based methods such as Margin, even on larger input sets like ImageNet. These results confirm not only the effectiveness and computational advantage of the CBD-based approach, particularly compared to hybrid baselines, but also its scalability in repetitive and extensive input selection scenarios.

cs.CV · cs.SE

查看论文
arXiv ↗PDF ↗