paper_name stringlengths 4 421 | paper_url stringlengths 21 200 | paper_authors listlengths 0 125 | paper_abstract stringlengths 0 43.4k | paper_code stringlengths 1 149 | conf stringlengths 6 18 |
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47th IEEE/ACM International Conference on Software Engineering: New Ideas and Emerging Results, ICSE 2025 - NIER, Ottawa, ON, Canada, April 27 - May 3, 2025 | https://doi.org/10.1109/ICSE-NIER66352.2025 | [] | # | ICSE2025 | |
AI-Assisted Autoformalization of Combinatorics Problems in Proof Assistants | https://doi.org/10.1109/ICSE-NIER66352.2025.00006 | [
"Long Doan",
"ThanhVu Nguyen"
] | Proof assistants such as Coq and LEAN have been increasingly used by renowned mathematicians to formalize and prove mathematical theorems. Despite their growing use, writing formal proofs is challenging, and even the first step of stating the problem formally is difficult as it requires a deep understanding of these sy... | # | ICSE2025 |
SolSearch: An LLM-Driven Framework for Efficient SAT-Solving Code Generation | https://doi.org/10.1109/ICSE-NIER66352.2025.00007 | [
"Junjie Sheng",
"Yanqiu Lin",
"Jiehao Wu",
"Yanhong Huang",
"Jianqi Shi",
"Min Zhang",
"Xiangfeng Wang"
] | The Satisfiability (SAT) problem is a core challenge with significant applications in software engineering, including automated testing, configuration management, and program verification. This paper presents SolSearch, a novel framework that harnesses large language models (LLMs) to discover and optimize SAT-solving s... | # | ICSE2025 |
Listening to the Firehose: Sonifying Z3's Behavior | https://doi.org/10.1109/ICSE-NIER66352.2025.00008 | [
"A. Finn Hackett",
"Ivan Beschastnikh"
] | Modern formal methods rely heavily on Satisfiability Modulo Theory (SMT) solvers like Z3. Unfortunately, these solvers are complex, have unpredictable runtime behavior, and are highly sensitive to the structure of the input query. As a result, when a Z3 query runs for tens of minutes and/or times out inconclusively, th... | # | ICSE2025 |
Digital Twins for Software Engineering Processes | https://doi.org/10.1109/ICSE-NIER66352.2025.00009 | [
"Robin Kimmel",
"Judith Michael",
"Andreas Wortmann",
"Jingxi Zhang"
] | Digital twins promise a better understanding and use of complex systems. To this end, they represent these systems at their runtime and may interact with them to control their processes. Software engineering is a wicked challenge in which stakeholders from many domains collaborate to produce software artifacts together... | # | ICSE2025 |
Discovering Ideologies of the Open Source Software Movement | https://doi.org/10.1109/ICSE-NIER66352.2025.00010 | [
"Yang Yue",
"Yi Wang",
"David F. Redmiles"
] | Encompassing a diverse population of developers, non-technical users, and other stakeholders, open source software (OSS) development has expanded to broader social movements from the initial product development aims. Ideology, as a coherent system of ideas, offers value commitments and normative implications for any so... | # | ICSE2025 |
When in Doubt Throw It Out: Building on Confident Learning for Vulnerability Detection | https://doi.org/10.1109/ICSE-NIER66352.2025.00011 | [
"Yuanjun Gong",
"Fabio Massacci"
] | [Context:] Confident learning’s intuition is that a good model can be used to identify mislabelled data. By swapping mislabeled samples that are not confidently predicted, the performance of model can be further improved. [Problem:] Unfortunately, vulnerability detectors are generally under-performing models and confid... | # | ICSE2025 |
Model Assisted Refinement of Metamorphic Relations for Scientific Software | https://doi.org/10.1109/ICSE-NIER66352.2025.00012 | [
"Clay Stevens",
"Katherine Kjeer",
"Ryan M. Richard",
"Edward F. Valeev",
"Myra B. Cohen"
] | Ensuring the correctness of scientific software is challenging due to the need to represent and model complex phenomenon in a discrete form. Many dynamic approaches for correctness have been developed for numerical overflow or imprecision, which may manifest as program crashes or hangs. Less effort has been spent on fu... | # | ICSE2025 |
A Unit Proofing Framework for Code-level Verification: A Research Agenda | https://doi.org/10.1109/ICSE-NIER66352.2025.00013 | [
"Paschal C. Amusuo",
"Parth V. Patil",
"Owen Cochell",
"Taylor Le Lievre",
"James C. Davis"
] | Formal verification provides mathematical guarantees that a software is correct. Design-level verification tools ensure software specifications are correct, but they do not expose defects in actual implementations. For this purpose, engineers use code-level tools. However, such tools struggle to scale to large software... | # | ICSE2025 |
What is a Feature, Really? Toward a Unified Understanding Across SE Disciplines | https://doi.org/10.1109/ICSE-NIER66352.2025.00014 | [
"Nitish Patkar",
"Aimen Fahmi",
"Timo Kehrer",
"Norbert Seyff"
] | In software engineering, the concept of a "feature" is widely used but inconsistently defined across disciplines such as requirements engineering (RE) and software product lines (SPL). This lack of consistency often results in communication gaps, rework, and inefficiencies in projects. To address these challenges, this... | # | ICSE2025 |
Optimizing Experiment Configurations for LLM Applications Through Exploratory Analysis | https://doi.org/10.1109/ICSE-NIER66352.2025.00015 | [
"Nimrod Busany",
"Hananel Hadad",
"Zofia Maszlanka",
"Rohit Shelke",
"Gregory Price",
"Okhaide Akhigbe",
"Daniel Amyot"
] | The integration of Large Language Models (LLMs) into software applications necessitates informed design choices across various configurations, including LLM selection, prompting techniques, and their parameters, and prompt templates. Many of these choices are arbitrary, and developers often lack guidance on optimizing ... | # | ICSE2025 |
On the Impact of Requirements Smells in Prompts: The Case of Automated Traceability | https://doi.org/10.1109/ICSE-NIER66352.2025.00016 | [
"Andreas Vogelsang",
"Alexander Korn",
"Giovanna Broccia",
"Alessio Ferrari",
"Jannik Fischbach",
"Chetan Arora"
] | Large language models (LLMs) are increasingly used to generate software artifacts, such as source code, tests, and trace links. Requirements play a central role in shaping the input prompts that guide LLMs, as they are often used as part of the prompts to synthesize the artifacts. However, the impact of requirements fo... | # | ICSE2025 |
Towards Trustworthy LLMs for Code: A Data-Centric Synergistic Auditing Framework | https://doi.org/10.1109/ICSE-NIER66352.2025.00017 | [
"Chong Wang",
"Zhenpeng Chen",
"Tianlin Li",
"Yilun Zhang",
"Yang Liu"
] | LLM-powered coding and development assistants have become prevalent to programmers’ workflows. However, concerns about the trustworthiness of LLMs for code persist despite their widespread use. Much of the existing research focused on either training or evaluation, raising questions about whether stakeholders in traini... | # | ICSE2025 |
A Unified Browser-Based Consent Management Framework | https://doi.org/10.1109/ICSE-NIER66352.2025.00018 | [
"Gayatri Priyadarsini Kancherla",
"Abhishek Bichhawat"
] | Today’s users are concerned about the privacy of their personal or sensitive information on the Web because of the different techniques employed to track their online activities and behavior. Privacy laws like the GDPR, CCPA, etc., allow users to decide whether they would like to share their personal data online and wh... | # | ICSE2025 |
Using ML filters to help automated vulnerability repairs: when it helps and when it doesn't | https://doi.org/10.1109/ICSE-NIER66352.2025.00019 | [
"Maria Camporese",
"Fabio Massacci"
] | [Context:] The acceptance of candidate patches in automated program repair has been typically based on testing oracles. Testing requires typically a costly process of building the application while ML models can be used to quickly classify patches, thus allowing more candidate patches to be generated in a positive feed... | # | ICSE2025 |
UniGenCoder: Merging SEQ2SEQ and SEQ2TREE Paradigms for Unified Code Generation | https://doi.org/10.1109/ICSE-NIER66352.2025.00020 | [
"Liangying Shao",
"Yanfu Yan",
"Denys Poshyvanyk",
"Jinsong Su"
] | Deep learning-based code generation has completely transformed the way developers write programs today. Existing approaches to code generation have focused either on the Sequence-to-Sequence paradigm, which generates target code as a sequence of tokens, or the Sequence-to-Tree paradigm, which outputs code as a sequence... | https://github.com/DeepLearnXMU/UniGenCoder | ICSE2025 |
Automatically Generating Content for Testing Autonomous Vehicles from User Descriptions | https://doi.org/10.1109/ICSE-NIER66352.2025.00021 | [
"Benedikt Steininger",
"Chrysanthi Papamichail",
"David Stark",
"Dejan Nickovic",
"Alessio Gambi"
] | Testing autonomous vehicles (AV) software, which is currently done using simulations, requires the availability of various content, such as terrains and maps, to instantiate relevant scenarios. Manually generating such content is time-consuming, and current approaches for procedural content generation struggle to handl... | # | ICSE2025 |
Takuan: Using Dynamic Invariants to Debug Order-Dependent Flaky Tests | https://doi.org/10.1109/ICSE-NIER66352.2025.00022 | [
"Nate Levin",
"Chengpeng Li",
"Yule Zhang",
"August Shi",
"Wing Lam"
] | Automated regression testing is critical to effective software development, but it suffers from flaky tests, i.e., tests that can nondeterministically pass or fail when run on the same version of code. Conceptually, a flaky test depends on a component not controlled by the test, and the test’s outcome depends on the st... | # | ICSE2025 |
Beyond Syntax: How Do LLMs Understand Code? | https://doi.org/10.1109/ICSE-NIER66352.2025.00023 | [
"Marc North",
"Amir Atapour-Abarghouei",
"Nelly Bencomo"
] | Within software engineering research, Large Language Models (LLMs) are often treated as ‘black boxes’, with only their inputs and outputs being considered. In this paper, we take a machine interpretability approach to examine how LLMs internally represent and process code.We focus on variable declaration and function s... | # | ICSE2025 |
Evaluating the Generalizability of LLMs in Automated Program Repair | https://doi.org/10.1109/ICSE-NIER66352.2025.00024 | [
"Fengjie Li",
"Jiajun Jiang",
"Jiajun Sun",
"Hongyu Zhang"
] | LLM-based automated program repair methods have attracted significant attention for their state-of-the-art performance. However, they were primarily evaluated on a few well-known datasets like Defects4J, raising questions about their effectiveness on new datasets. In this study, we evaluate 11 top-performing LLMs on DE... | # | ICSE2025 |
How Propense Are Large Language Models at Producing Code Smells? A Benchmarking Study | https://doi.org/10.1109/ICSE-NIER66352.2025.00025 | [
"Alejandro Velasco",
"Daniel Rodríguez-Cárdenas",
"Luftar Rahman Alif",
"David N. Palacio",
"Denys Poshyvanyk"
] | Large Language Models (LLMs) have shown significant potential in automating software engineering tasks, particularly in code generation. However, current evaluation benchmarks, which primarily focus on accuracy, fall short in assessing the quality of the code generated by these models, specifically their tendency to pr... | # | ICSE2025 |
Energy-Aware Software Testing | https://doi.org/10.1109/ICSE-NIER66352.2025.00026 | [
"Roberto Verdecchia",
"Emilio Cruciani",
"Antonia Bertolino",
"Breno Miranda"
] | Our planet urges for a more responsible use of its resources, and since information technology contributes substantially to the global energy consumption, software engineering research has promptly embraced this request and is actively working towards more sustainable processes. An indispensable activity in software de... | # | ICSE2025 |
SusDevOps: Promoting Sustainability to a First Principle in Software Delivery | https://doi.org/10.1109/ICSE-NIER66352.2025.00027 | [
"Istvan David"
] | Sustainability is becoming a key property of modern software systems. While there is a substantial and growing body of knowledge on engineering sustainable software, end-to-end frameworks that situate sustainability-related activities within the software delivery lifecycle are missing. In this article, we propose the S... | # | ICSE2025 |
LLMs as Evaluators: A Novel Approach to Commit Message Quality Assessment | https://doi.org/10.1109/ICSE-NIER66352.2025.00028 | [
"Abhishek Kumar",
"Sandhya Sankar",
"Sonia Haiduc",
"Partha Pratim Das",
"Partha Pratim Chakrabarti"
] | Evaluating the quality of commit messages is a challenging task in software engineering. Existing evaluation approaches, such as automatic metrics like BLEU, ROUGE and METEOR, as well as manual human assessments have notable limitations. Automatic metrics often overlook semantic relevance and context, while human evalu... | # | ICSE2025 |
Using Reinforcement Learning to Sustain the Performance of Version Control Repositories | https://doi.org/10.1109/ICSE-NIER66352.2025.00029 | [
"Shane McIntosh",
"Luca Milanesio",
"Antonio Barone",
"Jacek Centkowski",
"Marcin Czech",
"Fabio Ponciroli"
] | Although decentralized Version Control Systems (VCSs) like Git support several organizational structures, a central copy of the repository is typically where development activity is coalesced and where official software releases are cut. Popular practices like trunk-based development and monolithic repositories (a.k.a.... | # | ICSE2025 |
Towards Early Warning and Migration of High-Risk Dormant Open-Source Software Dependencies | https://doi.org/10.1109/ICSE-NIER66352.2025.00030 | [
"Zijie Huang",
"Lizhi Cai",
"Xuan Mao",
"Kang Yang"
] | Dormant open-source software (OSS) dependencies are no longer maintained or actively developed, their related code components are more vulnerable and error-prone since they can hardly keep up with evolving software dependents. Presently, their migration remains costly and challenging for practitioners. To tackle such a... | # | ICSE2025 |
Recommending Pre-Trained Models for IoT Devices | https://doi.org/10.1109/ICSE-NIER66352.2025.00031 | [
"Parth V. Patil",
"Wenxin Jiang",
"Huiyun Peng",
"Daniel Lugo",
"Kelechi G. Kalu",
"Josh LeBlanc",
"Lawrence Smith",
"Hyeonwoo Heo",
"Nathanael Aou",
"James C. Davis"
] | The availability of pre-trained models (PTMs) has enabled faster deployment of machine learning across applications by reducing the need for extensive training. Techniques like quantization and distillation have further expanded PTM applicability to resource-constrained IoT hardware. Given the many PTM options for any ... | # | ICSE2025 |
47th IEEE/ACM International Conference on Software Engineering, ICSE 2025, Ottawa, ON, Canada, April 26 - May 6, 2025 | https://doi.org/10.1109/ICSE55347.2025 | [] | # | ICSE2025 | |
AssetHarvester: A Static Analysis Tool for Detecting Secret-Asset Pairs in Software Artifacts | https://doi.org/10.1109/ICSE55347.2025.00067 | [
"Setu Kumar Basak",
"K. Virgil English",
"Ken Ogura",
"Vitesh Kambara",
"Bradley Reaves",
"Laurie A. Williams"
] | GitGuardian monitored secrets exposure in public GitHub repositories and reported that developers leaked over 12 million secrets (database and other credentials) in 2023, indicating a 113% surge from 2021. Despite the availability of secret detection tools, developers ignore the tools' reported warnings because of fals... | # | ICSE2025 |
Repository-Level Graph Representation Learning for Enhanced Security Patch Detection | https://doi.org/10.1109/ICSE55347.2025.00121 | [
"Xin-Cheng Wen",
"Zirui Lin",
"Cuiyun Gao",
"Hongyu Zhang",
"Yong Wang",
"Qing Liao"
] | Software vendors often silently release security patches without providing sufficient advisories (e.g., Common Vulnerabilities and Exposures) or delayed updates via resources (e.g., National Vulnerability Database). Therefore, it has become crucial to detect these security patches to ensure secure software maintenance.... | https://github.com/Xin-Cheng-Wen/RepoSPD | ICSE2025 |
Decictor: Towards Evaluating the Robustness of Decision-Making in Autonomous Driving Systems | https://doi.org/10.1109/ICSE55347.2025.00114 | [
"Mingfei Cheng",
"Xiaofei Xie",
"Yuan Zhou",
"Junjie Wang",
"Guozhu Meng",
"Kairui Yang"
] | Autonomous Driving System (ADS) testing is crucial in ADS development, with the current primary focus being on safety. However, the evaluation of non-safety-critical performance, particularly the ADS's ability to make optimal decisions and produce optimal paths for autonomous vehicles (AVs), is also vital to ensure the... | # | ICSE2025 |
Closing the Gap: A User Study on the Real-world Usefulness of AI-powered Vulnerability Detection & Repair in the IDE | https://doi.org/10.1109/ICSE55347.2025.00126 | [
"Benjamin Steenhoek",
"Kalpathy Sivaraman",
"Renata Saldivar Gonzalez",
"Yevhen Mohylevskyy",
"Roshanak Zilouchian Moghaddam",
"Wei Le"
] | Security vulnerabilities impose significant costs on users and organizations. Detecting and addressing these vulnerabilities early is crucial to avoid exploits and reduce development costs. Recent studies have shown that deep learning models can effectively detect security vulnerabilities. Yet, little research explores... | # | ICSE2025 |
SpecGen: Automated Generation of Formal Program Specifications via Large Language Models | https://doi.org/10.1109/ICSE55347.2025.00129 | [
"Lezhi Ma",
"Shangqing Liu",
"Yi Li",
"Xiaofei Xie",
"Lei Bu"
] | In the software development process, formal program specifications play a crucial role in various stages, including requirement analysis, software testing, and verification. However, manually crafting formal program specifications is rather difficult, making the job time-consuming and labor-intensive. Moreover, it is e... | # | ICSE2025 |
Gpass: A Goal-Adaptive Neural Theorem Prover Based on Coq for Automated Formal Verification | https://doi.org/10.1109/ICSE55347.2025.00116 | [
"Yizhou Chen",
"Zeyu Sun",
"Guoqing Wang",
"Dan Hao"
] | Formal verification is a crucial means to assure software quality. Regrettably, the manual composition of verification scripts proves to be both laborious and time-consuming. In response, researchers have put forth automated theorem prover approaches; however, these approaches still grapple with several limitations. Th... | # | ICSE2025 |
Formally Verified Binary-Level Pointer Analysis | https://doi.org/10.1109/ICSE55347.2025.00231 | [
"Freek Verbeek",
"Ali Shokri",
"Daniel Engel",
"Binoy Ravindran"
] | Binary-level pointer analysis can be of use in symbolic execution, testing, verification, and decompilation of software binaries. In various such contexts, it is crucial that the result is trustworthy, i.e., it can be formally established that the pointer designations are overapproximative. This paper presents an appro... | # | ICSE2025 |
EffBT: An Efficient Behavior Tree Reactive Synthesis and Execution Framework | https://doi.org/10.1109/ICSE55347.2025.00225 | [
"Ziji Wu",
"Yu Huang",
"Peishan Huang",
"Shanghua Wen",
"Minglong Li",
"Ji Wang"
] | Behavior Trees (BTs), originated from the control of Non-Player-Characters (NPCs), have been widely embraced in robotics and software engineering communities due to their modularity, reactivity, and other beneficial characteristics. It is highly desirable to synthesize BTs automatically. The consequent challenges are t... | # | ICSE2025 |
An Empirical Study on Package-Level Deprecation in Python Ecosystem | https://doi.org/10.1109/ICSE55347.2025.00046 | [
"Zhiqing Zhong",
"Shilin He",
"Haoxuan Wang",
"Boxi Yu",
"Haowen Yang",
"Pinjia He"
] | Open-source software (OSS) plays a crucial role in modern software development. Utilizing OSS code can greatly accelerate software development, reduce redundancy, and enhance reliability. Python, a widely adopted programming language, is renowned for its extensive and diverse third-party package ecosystem. However, a s... | # | ICSE2025 |
Datalog-Based Language-Agnostic Change Impact Analysis for Microservices | https://doi.org/10.1109/ICSE55347.2025.00115 | [
"Qingkai Shi",
"Xiaoheng Xie",
"Xianjin Fu",
"Peng Di",
"Huawei Li",
"Ang Zhou",
"Gang Fan"
] | The shift-left principle in the industry requires us to test a software application as early as possible. In particular, when code changes in a microservice application are committed to the code repository, we have to efficiently identify all public microservice interfaces affected by the changes so that the impacted i... | # | ICSE2025 |
GenC2Rust: Towards Generating Generic Rust Code from C | https://doi.org/10.1109/ICSE55347.2025.00127 | [
"Xiafa Wu",
"Brian Demsky"
] | Rust provides an exciting combination of strong safety guarantees and high performance. Many new systems are being implemented in Rust. Nevertheless, there is a large body of existing C code that could greatly benefit from Rust's safety guarantees. Unfortunately, the manual effort required to rewrite C code into Rust i... | # | ICSE2025 |
Instrumentation-Driven Evolution-Aware Runtime Verification | https://doi.org/10.1109/ICSE55347.2025.00099 | [
"Kevin Guan",
"Owolabi Legunsen"
] | Runtime verification (RV) found hundreds of bugs by monitoring passing tests against formal specifications (specs). RV first instruments a program to obtain relevant events, e.g., method calls, to monitor. A hindrance to RV adoption, especially in continuous integration, is its high overhead. So, prior work proposed sp... | # | ICSE2025 |
Moye: A Wallbreaker for Monolithic Firmware | https://doi.org/10.1109/ICSE55347.2025.00053 | [
"Jintao Huang",
"Kai Yang",
"Gaosheng Wang",
"Zhiqiang Shi",
"Zhiwen Pan",
"Shichao Lv",
"Limin Sun"
] | As embedded devices become increasingly popular, monolithic firmware, known for its execution efficiency and simplicity, is widely used in resource-constrained devices. Different from ordinary firmware, the monolithic firmware image is packed without the file that indicates its format, which challenges the reverse engi... | # | ICSE2025 |
Understanding and Detecting Peer Dependency Resolving Loop in npm Ecosystem | https://doi.org/10.1109/ICSE55347.2025.00054 | [
"Xingyu Wang",
"Mingsen Wang",
"Wenbo Shen",
"Rui Chang"
] | As the default package manager for Node.js, npm has become one of the largest package management systems in the world. To facilitate dependency management for developers, npm supports a special type of dependency, Peer Dependency, whose installation and usage differ from regular dependencies. However, conflicts between... | https://github.com/zju-sec/peerchecker | ICSE2025 |
Critical Variable State-Aware Directed Greybox Fuzzing | https://doi.org/10.1109/ICSE55347.2025.00219 | [
"Xu Chen",
"Ningning Cui",
"Zhe Pan",
"Liwei Chen",
"Gang Shi",
"Dan Meng"
] | Directed fuzzing is an effective software testing method that guides the fuzzing campaign towards user-defined target sites of interest, enabling the discovery of vulnerabilities relevant to those sites. However, even though the generated test cases cover the code near the target sites, complex vulnerabilities remain u... | # | ICSE2025 |
LWDIFF: an LLM-Assisted Differential Testing Framework for Webassembly Runtimes | https://doi.org/10.1109/ICSE55347.2025.00233 | [
"Shiyao Zhou",
"Jincheng Wang",
"He Ye",
"Hao Zhou",
"Claire Le Goues",
"Xiapu Luo"
] | WebAssembly (Wasm) runtimes execute Wasm programs, a popular low-level language for efficiently executing high-level languages in browsers, with broad applications across diverse domains. The correctness of those runtimes is critical for both functionality and security of Wasm execution, motivating testing approaches t... | # | ICSE2025 |
No Harness, No Problem: Oracle-guided Harnessing for Auto-generating C API Fuzzing Harnesses | https://doi.org/10.1109/ICSE55347.2025.00239 | [
"Gabriel Sherman",
"Stefan Nagy"
] | Library APIs are used by virtually every modern application and system, making them among today's most security-critical software. In recent years, library bug-finding efforts have overwhelmingly adopted the powerful testing strategy of coverage-guided fuzzing. At its core, API fuzzing operates on harnesses: wrapper pr... | # | ICSE2025 |
Parametric Falsification of Many Probabilistic Requirements Under Flakiness | https://doi.org/10.1109/ICSE55347.2025.00237 | [
"Matteo Camilli",
"Raffaela Mirandola"
] | Falsification is a popular simulation-based testing method for Cyber-Physical Systems to find inputs that violate a formal requirement. It employs optimization algorithms to minimize a robustness metric that defines the satisfaction of a given property over an execution trace. Despite falsification representing an esta... | # | ICSE2025 |
RediI: Test Infrastructure to Enable Deterministic Reproduction of Failures for Distributed Systems | https://doi.org/10.1109/ICSE55347.2025.00244 | [
"Yang Feng",
"Zheyuan Lin",
"Dongchen Zhao",
"Mengbo Zhou",
"Jia Liu",
"James A. Jones"
] | Despite the fact that distributed systems have become a crucial aspect of modern technology and support many of the software systems that enable modern life, developers experience challenges in performing regression testing of these systems. Existing solutions for testing distributed systems are often either: (1) speci... | # | ICSE2025 |
Automated Generation of Accessibility Test Reports from Recorded User Transcripts | https://doi.org/10.1109/ICSE55347.2025.00043 | [
"Syed Fatiul Huq",
"Mahan Tafreshipour",
"Kate Kalcevich",
"Sam Malek"
] | Testing for accessibility is a significant step when developing software, as it ensures that all users, including those with disabilities, can effectively engage with web and mobile applications. While automated tools exist to detect accessibility issues in software, none are as comprehensive and effective as the proce... | # | ICSE2025 |
Fuzzing MLIR Compilers with Custom Mutation Synthesis | https://doi.org/10.1109/ICSE55347.2025.00037 | [
"Ben Limpanukorn",
"Jiyuan Wang",
"Hong Jin Kang",
"Eric Zitong Zhou",
"Miryung Kim"
] | Compiler technologies in deep learning and domain-specific hardware acceleration are increasingly adopting extensible compiler frameworks such as Multi-Level Intermediate Representation (MLIR) to facilitate more efficient development. With MLIR, compiler developers can easily define their own custom IRs in the form of ... | # | ICSE2025 |
Insvdf: Interface-State-Aware Virtual Device Fuzzing | https://doi.org/10.1109/ICSE55347.2025.00190 | [
"Zexiang Zhang",
"Gaoning Pan",
"Ruipeng Wang",
"Yiming Tao",
"Zulie Pan",
"Cheng Tu",
"Min Zhang",
"Yang Li",
"Yi Shen",
"Chunming Wu"
] | Hypervisor is the core technology of virtualization for emulating independent hardware resources for each virtual machine. Virtual devices serve as the main interface of the hypervisor, making the security of virtual devices crucial, as any vulnerabilities can impact the entire virtualization environment and pose a thr... | # | ICSE2025 |
Reduce Dependence for Sound Concurrency Bug Prediction | https://doi.org/10.1109/ICSE55347.2025.00149 | [
"Shihao Zhu",
"Yuqi Guo",
"Yan Cai",
"Bin Liang",
"Long Zhang",
"Rui Chen",
"Tingting Yu"
] | Recently, dynamic concurrency bug predictions have kept making notable progress in improving concurrency coverage while ensuring soundness. Most of them rely solely on dynamic information in traces and overlook the static semantics of the program when predicting bugs. To ensure soundness, they assume that any (memory) ... | # | ICSE2025 |
Sand: Decoupling Sanitization from Fuzzing for Low Overhead | https://doi.org/10.1109/ICSE55347.2025.00187 | [
"Ziqiao Kong",
"Shaohua Li",
"Heqing Huang",
"Zhendong Su"
] | Sanitizers provide robust test oracles for various vulnerabilities. Fuzzing on sanitizer-enabled programs has been the best practice to find software bugs. Since sanitizers require heavy program instrumentation to insert run-time checks, sanitizerenabled programs have much higher overhead compared to normally built pro... | # | ICSE2025 |
TransferFuzz: Fuzzing with Historical Trace for Verifying Propagated Vulnerability Code | https://doi.org/10.1109/ICSE55347.2025.00061 | [
"Siyuan Li",
"Yuekang Li",
"Zuxin Chen",
"Chaopeng Dong",
"Yongpan Wang",
"Hong Li",
"Yongle Chen",
"Hongsong Zhu"
] | Code reuse in software development frequently facilitates the spread of vulnerabilities, making the scope of affected software in CVE reports imprecise. Traditional methods primarily focus on identifying reused vulnerability code within target software, yet they cannot verify if these vulnerabilities can be triggered i... | https://github.com/Siyuan-Li201/TransferFuzz | ICSE2025 |
A Multiple Representation Transformer with Optimized Abstract Syntax Tree for Efficient Code Clone Detection | https://doi.org/10.1109/ICSE55347.2025.00050 | [
"Tianchen Yu",
"Li Yuan",
"Liannan Lin",
"Hongkui He"
] | Over the past decade, the application of deep learning in code clone detection has produced remarkable results. However, the current approaches have two limitations: (a) code representation approaches with low information utilization, such as vanilla Abstract Syntax Tree (AST), leading to information redundancy which r... | # | ICSE2025 |
Can an LLM Find Its Way Around a Spreadsheet? | https://doi.org/10.1109/ICSE55347.2025.00101 | [
"Cho-Ting Lee",
"Andrew Neeser",
"Shengzhe Xu",
"Jay Katyan",
"Patrick Cross",
"Sharanya Pathakota",
"Marigold Norman",
"John Simeone",
"Jaganmohan Chandrasekaran",
"Naren Ramakrishnan"
] | Spreadsheets are routinely used in business and scientific contexts, and one of the most vexing challenges is performing data cleaning prior to analysis and evaluation. The ad-hoc and arbitrary nature of data cleaning problems, such as typos, inconsistent formatting, missing values, and a lack of standardization, often... | # | ICSE2025 |
QEDCartographer: Automating Formal Verification Using Reward-Free Reinforcement Learning | https://doi.org/10.1109/ICSE55347.2025.00033 | [
"Alex Sanchez-Stern",
"Abhishek Varghese",
"Zhanna Kaufman",
"Shizhuo Dylan Zhang",
"Talia Ringer",
"Yuriy Brun"
] | Formal verification is a promising method for producing reliable software, but the difficulty of manually writing verification proofs severely limits its utility in practice. Recent methods have automated some proof synthesis by guiding a search through the proof space using a theorem prover. Unfortunately, the theorem... | # | ICSE2025 |
TIGER: A Generating-Then-Ranking Framework for Practical Python Type Inference | https://doi.org/10.1109/ICSE55347.2025.00019 | [
"Chong Wang",
"Jian Zhang",
"Yiling Lou",
"Mingwei Liu",
"Weisong Sun",
"Yang Liu",
"Xin Peng"
] | Python's dynamic typing system offers flexibility and expressiveness but can lead to type-related errors, prompting the need for automated type inference to enhance type hinting. While existing learning-based approaches show promising inference accuracy, they struggle with practical challenges in comprehensively handli... | # | ICSE2025 |
ROCODE: Integrating Backtracking Mechanism and Program Analysis in Large Language Models for Code Generation | https://doi.org/10.1109/ICSE55347.2025.00133 | [
"Xue Jiang",
"Yihong Dong",
"Yongding Tao",
"Huanyu Liu",
"Zhi Jin",
"Ge Li"
] | Large language models (LLMs) have achieved impressive performance in code generation recently, offering programmers revolutionary assistance in software development. However, due to the auto-regressive nature of LLMs, they are susceptible to error accumulation during code generation. Once an error is produced, LLMs can... | https://github.com/jiangxxxue/ROCODE | ICSE2025 |
Rango: Adaptive Retrieval-Augmented Proving for Automated Software Verification | https://doi.org/10.1109/ICSE55347.2025.00161 | [
"Kyle Thompson",
"Nuno Saavedra",
"Pedro Carrott",
"Kevin Fisher",
"Alex Sanchez-Stern",
"Yuriy Brun",
"João F. Ferreira",
"Sorin Lerner",
"Emily First"
] | Formal verification using proof assistants, such as Coq, enables the creation of high-quality software. However, the verification process requires significant expertise and manual effort to write proofs. Recent work has explored automating proof synthesis using machine learning and large language models (LLMs). This wo... | https://github.com/rkthomps/coq-modeling | ICSE2025 |
A Differential Testing Framework to Identify Critical AV Failures Leveraging Arbitrary Inputs | https://doi.org/10.1109/ICSE55347.2025.00163 | [
"Trey Woodlief",
"Carl Hildebrandt",
"Sebastian G. Elbaum"
] | The proliferation of autonomous vehicles (AVs) has made their failures increasingly evident. Testing efforts aimed at identifying the inputs leading to those failures are challenged by the input's long-tail distribution, whose area under the curve is dominated by rare scenarios. We hypothesize that leveraging emerging ... | # | ICSE2025 |
Automating a Complete Software Test Process Using LLMs: An Automotive Case Study | https://doi.org/10.1109/ICSE55347.2025.00211 | [
"Shuai Wang",
"Yinan Yu",
"Robert Feldt",
"Dhasarathy Parthasarathy"
] | Vehicle API testing verifies whether the interactions between a vehicle's internal systems and external applications meet expectations, ensuring that users can access and control various vehicle functions and data. However, this task is inherently complex, requiring the alignment and coordination of API systems, commun... | # | ICSE2025 |
LLM-Agents Driven Automated Simulation Testing and Analysis of small Uncrewed Aerial Systems | https://doi.org/10.1109/ICSE55347.2025.00223 | [
"Venkata Sai Aswath Duvvuru",
"Bohan Zhang",
"Michael Vierhauser",
"Ankit Agrawal"
] | Thorough simulation testing is crucial for validating the correct behavior of small Uncrewed Aerial Systems (sUAS) across multiple scenarios, including adverse weather conditions (such as wind, and fog), diverse settings (hilly terrain, or urban areas), and varying mission profiles (surveillance, tracking). While vario... | # | ICSE2025 |
Efficient Domain Augmentation for Autonomous Driving Testing Using Diffusion Models | https://doi.org/10.1109/ICSE55347.2025.00206 | [
"Luciano Baresi",
"Davide Yi Xian Hu",
"Andrea Stocco",
"Paolo Tonella"
] | Simulation-based testing is widely used to assess the reliability of Autonomous Driving Systems (ADS), but its effectiveness is limited by the operational design domain (ODD) conditions available in such simulators. To address this limitation, in this work, we explore the integration of generative artificial intelligen... | # | ICSE2025 |
GARL: Genetic Algorithm-Augmented Reinforcement Learning to Detect Violations in Marker-Based Autonomous Landing Systems | https://doi.org/10.1109/ICSE55347.2025.00076 | [
"Linfeng Liang",
"Yao Deng",
"Kye Morton",
"Valtteri Kallinen",
"Alice James",
"Avishkar Seth",
"Endrowednes Kuantama",
"Subhas Mukhopadhyay",
"Richard Han",
"Xi Zheng"
] | Automated Uncrewed Aerial Vehicle (UAV) landing is crucial for autonomous UAV services such as monitoring, surveying, and package delivery. It involves detecting landing targets, perceiving obstacles, planning collision-free paths, and controlling UAV movements for safe landing. Failures can lead to significant losses,... | https://github.com/lfeng0722/drone_testig/ | ICSE2025 |
Does GenAI Make Usability Testing Obsolete? | https://doi.org/10.1109/ICSE55347.2025.00138 | [
"Ali Ebrahimi Pourasad",
"Walid Maalej"
] | Ensuring usability is crucial for the success of mobile apps. Usability issues can compromise user experience and negatively impact the perceived app quality. This paper presents UX-LLM, a novel tool powered by a Large Vision-Language Model that predicts usability issues in iOS apps. To evaluate the performance of UX-L... | # | ICSE2025 |
Feature-Driven End-to-End Test Generation | https://doi.org/10.1109/ICSE55347.2025.00141 | [
"Parsa Alian",
"Noor Nashid",
"Mobina Shahbandeh",
"Taha Shabani",
"Ali Mesbah"
] | End-to-end (E2E) testing is essential for ensuring web application quality. However, manual test creation is time-consuming, and current test generation techniques produce incoherent tests. In this paper, we present Autoe2e,a novel approach that leverages Large Language Models (LLMs) to automate the generation of seman... | https://github.com/parsaalian/autoe2e | ICSE2025 |
SeeAction: Towards Reverse Engineering How-What-Where of HCI Actions from Screencasts for UI Automation | https://doi.org/10.1109/ICSE55347.2025.00144 | [
"Dehai Zhao",
"Zhenchang Xing",
"Qinghua Lu",
"Xiwei Xu",
"Liming Zhu"
] | UI automation is an useful technique for UI testing, bug reproduction and robotic process automation. Recording the user actions with an application assists rapid development of UI automation scripts, but existing recording techniques are intrusive, rely on OS or GUI framework accessibility support or assume specific a... | https://github.com/DehaiZhao/SeeAction | ICSE2025 |
Synthesizing Document Database Queries Using Collection Abstractions | https://doi.org/10.1109/ICSE55347.2025.00152 | [
"Qikang Liu",
"Yang He",
"Yanwen Cai",
"Byeongguk Kwak",
"Yuepeng Wang"
] | Document databases are increasingly popular in various applications, but their queries are challenging to write due to the flexible and complex data model underlying document databases. This paper presents a synthesis technique that aims to generate document database queries from input-output examples automatically. A ... | # | ICSE2025 |
The Power of Types: Exploring the Impact of Type Checking on Neural Bug Detection in Dynamically Typed Languages | https://doi.org/10.1109/ICSE55347.2025.00088 | [
"Boqi Chen",
"José Antonio Hernández López",
"Gunter Mussbacher",
"Dániel Varró"
] | [Motivation] Automated bug detection in dynamically typed languages such as Python is essential for maintaining code quality. The lack of mandatory type annotations in such languages can lead to errors that are challenging to identify early with traditional static analysis tools. Recent progress in deep neural networks... | https://github.com/20001lastorder/icse2025-type4py | ICSE2025 |
A Test Oracle for Reinforcement Learning Software Based on Lyapunov Stability Control Theory | https://doi.org/10.1109/ICSE55347.2025.00074 | [
"Shiyu Zhang",
"Haoyang Song",
"Qixin Wang",
"Henghua Shen",
"Yu Pei"
] | Reinforcement Learning (RL) has gained significant attention in recent years. As RL software becomes more complex and infiltrates critical application domains, ensuring its quality and correctness becomes increasingly important. An indispensable aspect of software quality/correctness insurance is testing. However, test... | # | ICSE2025 |
CodeImprove: Program Adaptation for Deep Code Models | https://doi.org/10.1109/ICSE55347.2025.00139 | [
"Ravishka Rathnasuriya",
"Zijie Zhao",
"Wei Yang"
] | Leveraging deep learning (DL)-based code analysis tools to solve software engineering tasks is becoming increasingly popular. Code models often suffer performance degradation due to various reasons (e.g., code data shifts). Retraining is often required to address these issues, but frequent model updates are costly in l... | # | ICSE2025 |
Fairquant: Certifying and Quantifying Fairness of Deep Neural Networks | https://doi.org/10.1109/ICSE55347.2025.00016 | [
"Brian Hyeongseok Kim",
"Jingbo Wang",
"Chao Wang"
] | We propose a method for formally certifying and quantifying individual fairness of deep neural networks (DNN). Individual fairness guarantees that any two individuals who are identical except for a legally protected attribute (e.g., gender or race) receive the same treatment. While there are existing techniques that pr... | # | ICSE2025 |
Calibration and Correctness of Language Models for Code | https://doi.org/10.1109/ICSE55347.2025.00040 | [
"Claudio Spiess",
"David Gros",
"Kunal Suresh Pai",
"Michael Pradel",
"Md. Rafiqul Islam Rabin",
"Amin Alipour",
"Susmit Jha",
"Prem Devanbu",
"Toufique Ahmed"
] | Machine learning models are widely used, but can also often be wrong. Users would benefit from a reliable indication of whether a given output from a given model should be trusted, so a rational decision can be made whether to use the output or not. For example, outputs can be associated with a confidence measure; if t... | # | ICSE2025 |
An Empirical Study on Commit Message Generation Using LLMs via In-Context Learning | https://doi.org/10.1109/ICSE55347.2025.00091 | [
"Yifan Wu",
"Yunpeng Wang",
"Ying Li",
"Wei Tao",
"Siyu Yu",
"Haowen Yang",
"Wei Jiang",
"Jianguo Li"
] | Commit messages concisely describe code changes in natural language and are important for software maintenance. Several approaches have been proposed to automatically generate commit messages, but they still suffer from critical limitations, such as time-consuming training and poor generalization ability. To tackle the... | https://github.com/wuyifan18/llm4cmg | ICSE2025 |
Instruct or Interact? Exploring and Eliciting LLMs' Capability in Code Snippet Adaptation Through Prompt Engineering | https://doi.org/10.1109/ICSE55347.2025.00104 | [
"Tanghaoran Zhang",
"Yue Yu",
"Xinjun Mao",
"Shangwen Wang",
"Kang Yang",
"Yao Lu",
"Zhang Zhang",
"Yuxin Zhao"
] | Code snippet adaptation is a fundamental activity in the software development process. Unlike code generation, code snippet adaptation is not a “free creation”, which requires developers to tailor a given code snippet in order to fit specific requirements and the code context. Recently, large language models (LLMs) hav... | https://github.com/ztwater/instruct-or-interact | ICSE2025 |
Search-Based LLMs for Code Optimization | https://doi.org/10.1109/ICSE55347.2025.00021 | [
"Shuzheng Gao",
"Cuiyun Gao",
"Wenchao Gu",
"Michael R. Lyu"
] | The code written by developers usually suffers from efficiency problems and contain various performance bugs. These inefficiencies necessitate the research of automated refactoring methods for code optimization. Early research in code optimization employs rule-based methods and focuses on specific inefficiency issues, ... | https://github.com/shuzhenggao/sbllm | ICSE2025 |
Towards Better Answers: Automated Stack Overflow Post Updating | https://doi.org/10.1109/ICSE55347.2025.00024 | [
"Yubo Mai",
"Zhipeng Gao",
"Haoye Wang",
"Tingting Bi",
"Xing Hu",
"Xin Xia",
"Jianling Sun"
] | Utilizing code snippets on Stack Overflow (SO) is a common practice among developers for problem-solving. Although SO code snippets serve as valuable resources, it is important to acknowledge their imperfections, reusing problematic code snippets can lead to the introduction of suboptimal or buggy code into software pr... | # | ICSE2025 |
Unseen Horizons: Unveiling the Real Capability of LLM Code Generation Beyond the Familiar | https://doi.org/10.1109/ICSE55347.2025.00082 | [
"Yuanliang Zhang",
"Yifan Xie",
"Shanshan Li",
"Ke Liu",
"Chong Wang",
"Zhouyang Jia",
"Xiangbing Huang",
"Jie Song",
"Chaopeng Luo",
"Zhizheng Zheng",
"Rulin Xu",
"Yitong Liu",
"Si Zheng",
"Xiangke Liao"
] | Recently, large language models (LLMs) have shown strong potential in code generation tasks. However, there are still gaps before they can be fully applied in actual software development processes. Accurately assessing the code generation capabilities of large language models has become an important basis for evaluatin... | # | ICSE2025 |
ConsCS: Effective and Efficient Verification of Circom Circuits | https://doi.org/10.1109/ICSE55347.2025.00200 | [
"Jinan Jiang",
"Xinghao Peng",
"Jinzhao Chu",
"Xiapu Luo"
] | Circom is a popular programming language for writing arithmetic circuits that can be used to generate zero-knowledge proofs (ZKPs) like zk-SNARKS. ZKPs have received tremendous attention in protocols like zkRollups. The Circom circuits are compiled to Rank-1 Constraint Systems (R1CS) circuits, based on which zk-SNARK p... | # | ICSE2025 |
Constrained LTL Specification Learning from Examples | https://doi.org/10.1109/ICSE55347.2025.00160 | [
"Changjian Zhang",
"Parv Kapoor",
"Ian Dardik",
"Leyi Cui",
"Rômulo Meira-Góes",
"David Garlan",
"Eunsuk Kang"
] | Temporal logic specifications play an important role in a wide range of software analysis tasks, such as model checking, automated synthesis, program comprehension, and runtime monitoring. Given a set of positive and negative examples, specified as traces, LTL learning is the problem of synthesizing a specification, in... | # | ICSE2025 |
LLM-Aided Automatic Modeling for Security Protocol Verification | https://doi.org/10.1109/ICSE55347.2025.00197 | [
"Ziyu Mao",
"Jingyi Wang",
"Jun Sun",
"Shengchao Qin",
"Jiawen Xiong"
] | Symbolic protocol analysis serves as a pivotal technique for protocol design, security analysis, and the safeguarding of information assets. Several modern tools such as Tamarin and ProVerif have been proven successful in modeling and verifying real-world protocols, including complex protocols like TLS 1.3 and 5G AKA. ... | # | ICSE2025 |
Thanos: DBMS Bug Detection via Storage Engine Rotation Based Differential Testing | https://doi.org/10.1109/ICSE55347.2025.00001 | [
"Ying Fu",
"Zhiyong Wu",
"Yuanliang Zhang",
"Jie Liang",
"Jingzhou Fu",
"Yu Jiang",
"Shanshan Li",
"Xiangke Liao"
] | Differential testing is a prevalent strategy for establishing test oracles in automated DBMS testing. However, meticulously selecting equivalent DBMSs with diverse implementations and compatible input syntax requires huge manual efforts. In this paper, we propose Thanos, a framework that finds DBMS bugs via storage eng... | # | ICSE2025 |
Coni: Detecting Database Connector Bugs via State-Aware Test Case Generation | https://doi.org/10.1109/ICSE55347.2025.00003 | [
"Wenqian Deng",
"Jie Liang",
"Zhiyong Wu",
"Jingzhou Fu",
"Mingzhe Wang",
"Yu Jiang"
] | Database connectors are widely used in many applications to facilitate flexible and convenient database interactions. Potential bugs in database connectors can lead to various abnormal behaviors within applications, such as returning incorrect results or experiencing unexpected connection interruption. However, existin... | # | ICSE2025 |
PUPPY: Finding Performance Degradation Bugs in DBMSs via Limited-Optimization Plan Construction | https://doi.org/10.1109/ICSE55347.2025.00045 | [
"Zhiyong Wu",
"Jie Liang",
"Jingzhou Fu",
"Mingzhe Wang",
"Yu Jiang"
] | Database management systems (DBMSs) consistently strive for enhanced performance. For a given query, the optimizer of a DBMS aims to construct an optimal execution plan that incorporates multiple optimization operations. However, the resulting plan may sometimes perform worse than even if no optimizations were applied.... | # | ICSE2025 |
Enhancing Fault Localization in Industrial Software Systems via Contrastive Learning | https://doi.org/10.1109/ICSE55347.2025.00009 | [
"Chun Li",
"Hui Li",
"Zhong Li",
"Minxue Pan",
"Xuandong Li"
] | Engineers utilize logs as a primary resource for fault localization in large-scale software and system testing, a process that is notoriously time-consuming, costly, and labor-intensive. Despite considerable progress in automated fault localization approaches, their applicability remains limited in such settings, due t... | # | ICSE2025 |
Who's Pushing the Code? An Exploration of GitHub Impersonation | https://doi.org/10.1109/ICSE55347.2025.00065 | [
"Yueke Zhang",
"Anda Liang",
"Xiaohan Wang",
"Pamela J. Wisniewski",
"Fengwei Zhang",
"Kevin Leach",
"Yu Huang"
] | GitHub is one of the largest open-source software (OSS) communities for software development and collaboration. Impersonation in the OSS communities refers to the malicious act of assuming another user's identity, often aiming to gain unauthorized access to code, manipulate project outcomes, or spread misinformation. W... | # | ICSE2025 |
Investigating the Impact of Interpersonal Challenges on Feeling Welcome in OSS | https://doi.org/10.1109/ICSE55347.2025.00056 | [
"Bianca Trinkenreich",
"Zixuan Feng",
"Rudrajit Choudhuri",
"Marco Aurélio Gerosa",
"Anita Sarma",
"Igor Steinmacher"
] | The sustainability of open source software (OSS) projects hinges on contributor retention. Interpersonal challenges can inhibit a feeling of welcomeness among contributors, particularly from underrepresented groups, which impacts their decision to continue with the project. How much this impact is, varies among individ... | # | ICSE2025 |
Understanding the Effectiveness of Coverage Criteria for Large Language Models: A Special Angle from Jailbreak Attacks | https://doi.org/10.1109/ICSE55347.2025.00209 | [
"Shide Zhou",
"Tianlin Li",
"Kailong Wang",
"Yihao Huang",
"Ling Shi",
"Yang Liu",
"Haoyu Wang"
] | Large language models (LLMs) have revolutionized artificial intelligence, but their increasing deployment across critical domains has raised concerns about their abnormal behaviors when faced with malicious attacks. Such vulnerability alerts the widespread inadequacy of pre-release testing. In this paper, we conduct a ... | # | ICSE2025 |
Diversity Drives Fairness: Ensemble of Higher Order Mutants for Intersectional Fairness of Machine Learning Software | https://doi.org/10.1109/ICSE55347.2025.00122 | [
"Zhenpeng Chen",
"Xinyue Li",
"Jie M. Zhang",
"Federica Sarro",
"Yang Liu"
] | Intersectional fairness is a critical requirement for Machine Learning (ML) software, demanding fairness across subgroups defined by multiple protected attributes. This paper introduces FairHOME, a novel ensemble approach using higher order mutation of inputs to enhance intersectional fairness of ML software during the... | # | ICSE2025 |
HIFI: Explaining and Mitigating Algorithmic Bias Through the Lens of Game-Theoretic Interactions | https://doi.org/10.1109/ICSE55347.2025.00221 | [
"Lingfeng Zhang",
"Zhaohui Wang",
"Yueling Zhang",
"Min Zhang",
"Jiangtao Wang"
] | Machine Learning (ML) algorithms are increasingly used in decision-making process across various social-critical domains, but they often somewhat inherit and amplify bias from their training data, leading to unfair and unethical outcomes. This issue highlights the urgent need for effective methods to detect, explain, a... | # | ICSE2025 |
Towards More Trustworthy Deep Code Models by Enabling Out-of-Distribution Detection | https://doi.org/10.1109/ICSE55347.2025.00177 | [
"Yanfu Yan",
"Viet Duong",
"Huajie Shao",
"Denys Poshyvanyk"
] | Numerous machine learning (ML) models have been developed, including those for software engineering (SE) tasks, under the assumption that training and testing data come from the same distribution. However, training and testing distributions often differ, as training datasets rarely encompass the entire distribution, wh... | https://github.com/yanyanfu/cood | ICSE2025 |
FairSense: Long-Term Fairness Analysis of ML-Enabled Systems | https://doi.org/10.1109/ICSE55347.2025.00159 | [
"Yining She",
"Sumon Biswas",
"Christian Kästner",
"Eunsuk Kang"
] | Algorithmic fairness of machine learning (ML) models has raised significant concern in the recent years. Many testing, verification, and bias mitigation techniques have been proposed to identify and reduce fairness issues in ML models. The existing methods are model-centric and designed to detect fairness issues under ... | https://github.com/cmu-soda/fairsense | ICSE2025 |
A Little Goes a Long Way: Tuning Configuration Selection for Continuous Kernel Fuzzing | https://doi.org/10.1109/ICSE55347.2025.00042 | [
"Sanan Hasanov",
"Stefan Nagy",
"Paul Gazzillo"
] | The Linux kernel is actively-developed and widely-used. It supports billions of devices of all classes, from high-performance computing to the Internet-of-Things, in part because of its sophisticated configuration system, which automatically tailors the source code according to thousands of user-provided configuration ... | # | ICSE2025 |
Exploring the Robustness of the Effect of EVO on Intention Valuation Through Replication | https://doi.org/10.1109/ICSE55347.2025.00143 | [
"Yesugen Baatartogtokh",
"Kaitlyn Cook",
"Alicia M. Grubb"
] | The development of high-quality software depends on precise and comprehensive requirements that meet the objectives of stakeholders. Goal modeling techniques have been developed to fill this gap by capturing and analyzing stakeholders' needs and allowing them to make trade-off decisions; yet, goal modeling analysis is ... | # | ICSE2025 |
Unavoidable Boundary Conditions: a Control Perspective on Goal Conflicts | https://doi.org/10.1109/ICSE55347.2025.00031 | [
"Francisco Cirelli",
"Dalal Alrajeh",
"Sebastián Uchitel"
] | Boundary conditions express situations under which requirements specifications conflict. They are used within a broader conflict management process to produce less idealized specifications. Several approaches have been proposed to identify boundary conditions automatically. Some introduce a prioritization criteria to r... | # | ICSE2025 |
User Personas Improve Social Sustainability by Encouraging Software Developers to Deprioritize Antisocial Features | https://doi.org/10.1109/ICSE55347.2025.00135 | [
"Bimpe Ayoola",
"Miikka Kuutila",
"Rina R. Wehbe",
"Paul Ralph"
] | Background: Sustainable software development involves creating software in a manner that meets present goals without undermining our ability to meet future goals. In a software engineering context, sustainability has at least four dimensions: ecological, economic, social, and technical. No interventions for improving s... | # | ICSE2025 |
Neurosymbolic Modular Refinement Type Inference | https://doi.org/10.1109/ICSE55347.2025.00090 | [
"Georgios Sakkas",
"Pratyush Sahu",
"Kyeling Ong",
"Ranjit Jhala"
] | Refinement types, a type-based generalization of Floyd-Hoare logics, are an expressive and modular means of statically ensuring a wide variety of correctness, safety, and security properties of software. However, their expressiveness and modularity means that to use them, a developer must laboriously annotate all the f... | # | ICSE2025 |
An Empirical Study on Automatically Detecting AI-Generated Source Code: How Far are We? | https://doi.org/10.1109/ICSE55347.2025.00064 | [
"Hyunjae Suh",
"Mahan Tafreshipour",
"Jiawei Li",
"Adithya Bhattiprolu",
"Iftekhar Ahmed"
] | Artificial Intelligence (AI) techniques, especially Large Language Models (LLMs), have started gaining popularity among researchers and software developers for generating source code. However, LLMs have been shown to generate code with quality issues and also incurred copyright/licensing infringements. Therefore, detec... | # | ICSE2025 |
Planning a Large Language Model for Static Detection of Runtime Errors in Code Snippets | https://doi.org/10.1109/ICSE55347.2025.00102 | [
"Smit Patel",
"Aashish Yadavally",
"Hridya Dhulipala",
"Tien N. Nguyen"
] | Large Language Models (LLMs) have been excellent in generating and reasoning about source code and natural-language texts. They can recognize patterns, syntax, and semantics in code, making them effective in several software engineering tasks. However, they exhibit weaknesses in reasoning about the program execution. T... | # | ICSE2025 |
LLMs Meet Library Evolution: Evaluating Deprecated API Usage in LLM-Based Code Completion | https://doi.org/10.1109/ICSE55347.2025.00245 | [
"Chong Wang",
"Kaifeng Huang",
"Jian Zhang",
"Yebo Feng",
"Lyuye Zhang",
"Yang Liu",
"Xin Peng"
] | Large language models (LLMs), pre-trained or fine-tuned on large code corpora, have shown effectiveness in generating code completions. However, in LLM-based code completion, LLMs may struggle to use correct and up-to-date Application Programming Interfaces (APIs) due to the rapid and continuous evolution of libraries.... | # | ICSE2025 |
End of preview. Expand in Data Studio
PaperVault Dataset · 论文元数据库
🔎 项目简介 · Overview
PaperVault 是一份持续自动更新的统一论文元数据库，覆盖自然语言处理、计算机视觉、机器学习、数据挖掘、数据库、语音、系统、网络、安全、理论计算机科学、人机交互、计算机图形学与多媒体等方向的顶级会议与期刊。PaperVault is a continuously-updated, unified metadata database of papers from top-tier conferences and journals across NLP, Computer Vision, Machine Learning, Data Mining, Databases, Speech, Systems, Networking, Security, Theory, HCI, Graphics, and Multimedia.
🌐 源仓库 / Source: github.com/youngfish42/PaperVault — Web UI、REST API、采集流水线、Issues / PRs 全部在那里 · Web UI, REST API, crawling pipelines and issues/PRs all live there.
🌐 在线搜索 / Live search: papervault.top — 直接使用 Web 检索界面，无需下载数据 · Use the web search UI directly without downloading the dataset.
🆕 最近更新 · Recent Update
- 📅 最近更新 · Last updated: 2026-10-17 (Asia/Shanghai)
- 📊 数据库规模 · Database size: 714,769 篇论文 / 129 个刊物系列 / 625,152 篇含摘要 / 112,086 篇含开源代码（714,769 papers / 129 venue series / 625,152 with abstract / 112,086 with code）
📈 数据看板 · Statistics at a Glance
下列 4 张统计图与 cache.jsonl.gz 同源同步，反映本数据集的最新状态。The four charts below are generated from the same cache.jsonl.gz and always reflect the latest state of this dataset.
📦 数据集内容 · What's in this dataset
| 路径 Path | 子集 Subset | 格式 Format | 说明 Description |
|---|---|---|---|
cache/cache.jsonl.gz |
papers（默认 / default） |
gzip-compressed JSON Lines (UTF-8) | 每行一篇论文 · One paper per line; one JSON object per line |
cache/abstract_backfill_progress.jsonl.gz |
abstract_backfill_progress |
gzip-compressed JSON Lines (UTF-8) | 摘要回填流水线的进度/断点记录，不是论文元数据；仅供工作流恢复使用 · Append-only progress log of the abstract-backfill pipeline (not paper records); used by the workflow to resume between runs |
Hugging Face 会自动为 cache.jsonl.gz 生成 Parquet 视图，也可直接用 datasets.load_dataset(...) 读取，无需手动解压。Hugging Face also exposes an auto-generated Parquet view, so datasets.load_dataset(...) works out of the box.
💡 Dataset Viewer 与
datasets.load_dataset("youngfish42/PaperVault")默认展示/加载的都是papers子集（即cache/cache.jsonl.gz）。如需查看回填进度，请在 Viewer 顶部下拉框切换到abstract_backfill_progress，或调用load_dataset("youngfish42/PaperVault", name="abstract_backfill_progress")。The Dataset Viewer anddatasets.load_dataset("youngfish42/PaperVault")both default to thepaperssubset (cache/cache.jsonl.gz). To inspect backfill progress, switch the Viewer's subset dropdown toabstract_backfill_progressor callload_dataset("youngfish42/PaperVault", name="abstract_backfill_progress").
📐 字段 Schema
| Field 字段 | Type 类型 | Notes 说明 |
|---|---|---|
paper_name |
string | 论文标题（已归一化）· Normalised paper title |
paper_authors |
list[string] | 作者列表，按原始顺序 · Author names in order |
paper_url |
string | 论文在原始平台的链接（PDF 或落地页）· Canonical URL on the venue's site (PDF or landing page) |
paper_abstract |
string | 摘要；未回填时为空字符串 · Abstract; may be empty when not yet backfilled |
paper_code |
string | 从摘要中抽取出的 GitHub 仓库 URL；"#" 是「未发现代码链接」的占位符 · GitHub repository URL extracted from the abstract; "#" is the sentinel for "no code link discovered" |
conf |
string | 会议+年份标识，如 ACL2024、NIPS2023、CVPR2025；去掉末尾四位数字即可得到会议系列。注意 NeurIPS Proceedings 沿用历史命名 NIPS{year}。Venue + year identifier (e.g. ACL2024, NIPS2023, CVPR2025). Strip the trailing 4-digit year to recover the venue series. Note that NeurIPS Proceedings entries use the historical name NIPS{year}. |
缺失字段请按空字符串处理。Treat missing fields as empty strings.
⬇️ 获取方式 · How to download
下面三种方式任选其一即可，无需克隆 GitHub 仓库。Pick any one of the three options below — no GitHub clone is required.
方式 A · Option A — huggingface_hub（推荐 / recommended）
from huggingface_hub import hf_hub_download
import gzip, json
path = hf_hub_download(
repo_id="youngfish42/PaperVault",
filename="cache/cache.jsonl.gz",
repo_type="dataset",
)
with gzip.open(path, "rt", encoding="utf-8") as f:
for line in f:
record = json.loads(line)
# 在这里处理一条记录 · do something with the record
方式 B · Option B — datasets
from datasets import load_dataset
ds = load_dataset("youngfish42/PaperVault")
print(ds[next(iter(ds))][0])
数据集只有一个默认 split（非 ML 训练集），不要传
split="train"。Single default split — do not passsplit="train".
方式 C · Option C — huggingface-cli / 直接 HTTPS · Plain HTTPS
huggingface-cli download youngfish42/PaperVault \
cache/cache.jsonl.gz --repo-type dataset --local-dir ./data
💡 文件压缩后约 120 MB（会随数据持续增长），解压后是 GB 级 JSONL 流，请按行流式读取，不要整体载入内存。The file is ~120 MB compressed (and growing) and decompresses to a multi-GB JSONL stream. Stream it line-by-line rather than loading the whole thing into memory.
🔁 更新节奏 · Update cadence
数据集由三个 GitHub Actions 工作流负责重建并推送到本 Hub 仓库 / The dataset is rebuilt and pushed to this Hub repo by three GitHub Actions workflows：
| 工作流 Workflow | 触发节奏 Schedule | 推送的内容 What it pushes |
|---|---|---|
collect_papers |
每月 15 号 16:00 UTC + 手动触发 · 15th of every month at 16:00 UTC + workflow_dispatch |
增量抓取新发现的会议/年份组合 · Incremental crawl of newly-discovered conference/year combinations |
backfill_abstracts |
每月 1 号 00:00 UTC + 手动触发 · 1st of every month at 00:00 UTC + workflow_dispatch |
为已有论文回填 paper_abstract · Adds paper_abstract for papers that were collected without one |
update_readme |
仅手动触发 (workflow_dispatch) · Manual only (workflow_dispatch) |
默认仅刷新 README 与统计；当输入参数 mode=force 时执行全量重建 · Refreshes the README and statistics by default; performs a full rebuild only when invoked with mode=force |
每次推送都使用 Hugging Face 的 parent_commit 乐观锁机制，避免并发覆盖。Each push uses Hugging Face's parent_commit optimistic-lock mechanism to avoid silently overwriting concurrent updates.
🌐 线上服务 · Live Service
无需下载数据集即可直接体验 Web 检索： No need to download the dataset — try the web search directly:
🔗 关联仓库 · Related repository
如果你需要完整的搜索 Web UI（智能搜索 + Web of Science 风格的高级查询 DSL）、REST API（/api/v1/*）、抓取 / 合并 / 摘要回填流水线源码、收录会议范围、统计仪表盘、项目截图或贡献指南，请前往 GitHub 项目仓库。If you are looking for the full search Web UI (smart search + Web-of-Science-style advanced query DSL), the REST API (/api/v1/*), the crawling / merging / abstract-backfill pipelines source code, conference coverage, statistics dashboards, screenshots or contribution guidelines, please visit the GitHub repository.
👉 github.com/youngfish42/PaperVault
📜 许可证 · License
代码以 GPL-3.0 发布；每条论文记录的著作权仍属于原作者 / 出版方，本数据集仅重新分发公开可获取的元数据与链接。Code is released under GPL-3.0; individual paper records remain the IP of their authors/publishers — this dataset only redistributes publicly available bibliographic metadata and links.
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