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An Interactive Debugger for Rust Trait Errors

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arXiv 2025年

作者： Gray, Gavin Crichton, Will Krishnamurthi, Shriram Brown University Providence United States

Compiler diagnostics for type inference failures are notoriously bad, and type classes only make the problem worse. By introducing a complex search process during inference, type classes can lead to wholly inscrutable or useless errors. We describe a system, Argus, for interactively visualizing type class inferences to help programmers debug inference failures, applied specifically to Rust’s trait system. The core insight of Argus is to avoid the traditional model of compiler diagnostics as one-size-fits-all, instead providing the programmer with different views on the search tree corresponding to different debugging goals. Argus carefully uses defaults to improve debugging productivity, including interface design (e.g., not showing full paths of types by default) and heuristics (e.g., sorting obligations based on the expected complexity of fixing them). We evaluated Argus in a user study where N = 25 participants debugged type inference failures in realistic Rust programs, finding that participants using Argus correctly localized 2.2× as many faults and localized 3.3× faster compared to not using Argus. © 2025, CC BY.

关键词： program debugging

AI2Agent: An End-to-End Framework for Deploying AI Projects as Autonomous Agents

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arXiv 2025年

作者： Chen, Jiaxiang Shi, Jingwei Gan, Lei Zhang, Jiale Zhang, Qingyu Zhang, Dongqian Pang, Xin Li, Zhucong Xu, Yinghui Continue-AI United States Artificial Intelligence Innovation and Incubation Institute Fudan University Shanghai China

As AI technology advances, it is driving innovation across industries, increasing the demand for scalable AI project deployment. However, deployment remains a critical challenge due to complex environment configurations, dependency conflicts, cross-platform adaptation, and debugging difficulties, which hinder automation and adoption. This paper introduces AI2Agent, an end-to-end framework that automates AI project deployment through guideline-driven execution, self-adaptive debugging, and case & solution accumulation. AI2Agent dynamically analyzes deployment challenges, learns from past cases, and iteratively refines its approach, significantly reducing human intervention. To evaluate its effectiveness, we conducted experiments on 30 AI deployment cases, covering TTS, text-to-image generation, image editing, and other AI applications. Results show that AI2Agent significantly reduces deployment time and improves success rates. The code1 and demo video2 are now publicly accessible. Copyright © 2025, The Authors. All rights reserved.

关键词： program debugging

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Chinese Journal of Electronics 2021年第1期30卷 55-63页

作者： BO Lili ZHU Xuanrui SUN Xiaobing NI Zhen LI Bin School of Information Engineering Yangzhou University Jiangsu Engineering Research Center of Knowledge Management and Intelligent Service State Key Laboratory for Novel Software Technology Nanjing University

Fine-grained change operations can help software developers fix software bugs more accurately and efficiently. However, the current fine-grained change operations are only used in specific fixing process, such as fixing of If statement. In this paper, we conducted an empirical study to explore the fine-grained change operations for bug fixing. Based on the Mozilla bug data,we examined whether similar bugs are fixed with similar change operations. The results show that: First, for bug reports with similar descriptions or bug-fix commits with similar descriptions, their corresponding fine-grained change operations are not related; Second, in the case where the descriptions of both bug reports and bug-fix commits are similar, the fine-grained change operations in patch code are not related; Third, by classifying bug reports, we find that the change operations in the same bug report category are similar; Finally, by analyzing the fine-grained change operations for each bug, we present some combined patterns that are often used together.

关键词： bug reports bug fixing If statement Mozilla bug data bug-fix commits similar descriptions program debugging fine-grained change operations

Agent That Debugs: Dynamic State-Guided Vulnerability Repair

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arXiv 2025年

作者： Liu, Zhengyao Ma, Yunlong Xu, Jingxuan Ai, Junchen Gao, Xiang Sun, Hailong Roychoudhury, Abhik Beihang University China National University of Singapore Singapore

In recent years, more vulnerabilities have been discovered every day, while manual vulnerability repair requires specialized knowledge and is time-consuming. As a result, many detected or even published vulnerabilities remain unpatched, thereby increasing the exposure of software systems to attacks. Recent advancements in agents based on Large Language Models have demonstrated their increasing capabilities in code understanding and generation, which can be promising to achieve automated vulnerability repair. However, the effectiveness of agents based on static information retrieval is still not sufficient for patch generation. To address the challenge, we propose a program repair agent called VulDebugger that fully utilizes both static and dynamic context, and it debugs programs in a manner akin to humans. The agent inspects the actual state of the program via the debugger and infers expected states via constraints that need to be satisfied. By continuously comparing the actual state with the expected state, it deeply understands the root causes of the vulnerabilities and ultimately accomplishes repairs. We experimentally evaluated VulDebugger on 50 real-life projects. With 60.00% successfully fixed, VulDebugger significantly outperforms state-of-the-art approaches for vulnerability repair. © 2025, CC BY.

关键词： program debugging

OpenCodeInstruct: A Large-scale Instruction Tuning Dataset for Code LLMs

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arXiv 2025年

作者： Ahmad, Wasi Uddin Ficek, Aleksander Samadi, Mehrzad Huang, Jocelyn Noroozi, Vahid Majumdar, Somshubra Ginsburg, Boris NVIDIA Santa ClaraCA15213 United States

Large Language Models (LLMs) have transformed software development by enabling code generation, automated debugging, and complex reasoning. However, their continued advancement is constrained by the scarcity of high-quality, publicly available supervised fine-tuning (SFT) datasets tailored for coding tasks. To bridge this gap, we introduce OPENCODEINSTRUCT, the largest open-access instruction tuning dataset, comprising 5 million diverse samples. Each sample includes a programming question, solution, test cases, execution feedback, and LLM-generated quality assessments. We fine-tune various base models, including LLaMA and Qwen, across multiple scales (1B+, 3B+, and 7B+) using our dataset. Comprehensive evaluations on popular benchmarks (HumanEval, MBPP, LiveCodeBench, and BigCodeBench) demonstrate substantial performance improvements achieved by SFT with OPENCODEINSTRUCT. We also present a detailed methodology encompassing seed data curation, synthetic instruction and solution generation, and filtering. © 2025, CC BY.

关键词： program debugging

Automated planning for finding alternative bug traces

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IET COMPUTERS AND DIGITAL TECHNIQUES 2020年第6期14卷 322-335页

作者： Jana, Rajib Lochan Dey, Soumyajit Mondal, Arijit Dasgupta, Pallab Indian Inst Technol Kharagpur 721302 W Bengal India

Bug traces serve as references for patching a microprocessor design after a bug has been found. Unless the root cause of a bug has been detected and patched, variants of the bug may return through alternative bug traces, following a different sequence of micro-architectural events. To avoid such a situation, the verification engineer must think of every possible way in which the bug may return, which is a complex problem for a modern microprocessor. This study proposes a methodology which gleans high-level descriptions of the micro-architectural steps and uses them in an artificial Intelligence planning framework to find alternative pathways through which a bug may return. The plans are then translated to simulation test cases which explore these potential bug scenarios. The planning tool essentially automates the task of the verification engineer towards exploring possible alternative sequences of micro-architectural steps that may allow a bug to return. The proposed methodology is demonstrated in three case studies.

关键词： program debugging planning (artificial intelligence) microcomputers program verification microarchitectural events bug traces automated planning microprocessor design verification engineer artificial Intelligence planning planning tool

Cost-effective testing based fault localization with distance based test-suite reduction

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Science China(Information Sciences) 2017年第9期60卷 163-177页

作者： Xingya WANG Shujuan JIANG Pengfei GAO Xiaolin JU Rongcun WANG Yanmei ZHANG School of Computer Science and Technology China University of Mining and Technology School of Computer Science and Technology Nantong University Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology

The aim of testing based fault localization(TBFL) involves improving the efficiency of program debugging by providing developers with a guide of ranked list of suspicious statements. However, collection of testing information of the whole original test-suite is excessively expensive or even infeasible for developers to conduct TBFL. Traditional test-suite reduction(TSR) techniques are utilized to reduce the size of testsuite. However, they entail a time-consuming process of whole testing information collection. In this study, the distance based test-suite reduction(DTSR) technique is proposed. As opposed to the whole testing information,the distances among the test cases are used to guide the process of test-suite reduction in DTSR. Hence, it is only necessary to collect the testing information for a portion of the test cases for TSR and TBFL. The investigation on the Siemens and SIR benchmarks reveals that DTSR can effectively reduce the size of the given test-suite as well as the time cost of TBFL. Additionally, the fault locating effectiveness of DTSR results is close to that when the whole test-suite is used.

关键词： program debugging fault localization test-suite reduction distance estimation category partition

Towards Adaptive Software Agents for debugging

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arXiv 2025年

作者： Majdoub, Yacine Ben Charrada, Eya Touati, Haifa University of Gabes Tunisia

Using multiple agents was found to improve the debugging capabilities of Large Language Models. However, increasing the number of LLM-agents has several drawbacks such as increasing the running costs and rising the risk for the agents to lose focus. In this work, we propose an adaptive agentic design, where the number of agents and their roles are determined dynamically based on the characteristics of the task to be achieved. In this design, the agents roles are not predefined, but are generated after analyzing the problem to be solved. Our initial evaluation shows that, with the adaptive design, the number of agents that are generated depends on the complexity of the buggy code. In fact, for simple code with mere syntax issues, the problem was usually fixed using one agent only. However, for more complex problems, we noticed the creation of a higher number of agents. Regarding the effectiveness of the fix, we noticed an average improvement of 11% compared to the one-shot prompting. Given these promising results, we outline future research directions to improve our design for adaptive software agents that can autonomously plan and conduct their software goals. Copyright © 2025, The Authors. All rights reserved.

关键词： program debugging

SamEn-SVR: using sample entropy and support vector regression for bug number prediction

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IET SOFTWARE 2018年第3期12卷 183-189页

作者： Zhang, Wen Du, Yuhang Yoshida, Taketoshi Wang, Qing Li, Xiangjun Beijing Univ Technol Sch Econ & Management Beijing 100124 Peoples R China Beijing Univ Chem Technol Res Ctr Big Data Sci Beijing 100029 Peoples R China Japan Adv Inst Sci & Technol Sch Knowledge Sci 1-1 Ashahidai Nomi City Ishikawa 9231292 Japan Chinese Acad Sci Inst Software State Key Lab Comp Sci Beijing 100190 Peoples R China Xian Univ Sch Informat Engn Xian 710065 Shaanxi Peoples R China

Monitoring and predicting the trend of bug number time series of a software system is crucial for both software project managers and software end-users. For software managers, accurate prediction of bug number of a software system will assist them in making timely decisions, such as effort investment and resource allocation. For software end-users, knowing possible bug number of their systems ahead will enable them to adopt timely actions in coping with the loss caused by possible system failures. This study proposes an approach called SamEn-SVR to combine sample entropy and support vector regression (SVR) to predict software bug number using time series analysis. The basic idea is to use template vectors with the smallest complexity as input vectors for SVR classifiers to ensure predictability of time series. By using Mozilla Firefox bug data, we conduct extensive experiments to compare the proposed approach and state-of-the-art techniques including auto-regressive integrated moving average (ARIMA), X12 enhanced ARIMA and polynomial regression to predict bug number time series. Experimental results demonstrate that the proposed SamEn-SVR approach outperforms state-of-the-art techniques in bug number prediction.

关键词： autoregressive moving average processes program debugging support vector machines regression analysis entropy time series pattern classification vectors software management sample entropy support vector regression bug number prediction bug number time series software system software project managers software end-users software bug number time series analysis template vectors input vectors SVR classifiers Mozilla Firefox bug data SamEn-SVR approach