Proceedings of the 46th IEEE/ACM International Conference on Software Engineering, ICSE 2024, Lisbon, Portugal, April 14-20, 2024.
第46届IEEE/ACM软件工程国际会议论文集，2024年4月14日至20日，葡萄牙里斯本。

1 Domain Knowledge Matters: Improving Prompts with Fix Templates for Repairing Python Type Errors.
领域知识的重要性：使用修复模板改进修复Python类型错误的提示。

2 Practical Program Repair via Preference-based Ensemble Strategy.
基于偏好的集成策略的实用程序修复。

3 Learning and Repair of Deep Reinforcement Learning Policies from Fuzz-Testing Data.
从模糊测试数据学习和修复深度强化学习策略。

4 BinAug: Enhancing Binary Similarity Analysis with Low-Cost Input Repairing.
BinAug：通过低成本输入修复增强二进制相似性分析。

5 VeRe: Verification Guided Synthesis for Repairing Deep Neural Networks.
VeRe：用于修复深度神经网络的验证引导合成。

6 RUNNER: Responsible UNfair NEuron Repair for Enhancing Deep Neural Network Fairness.
RUNNER：负责任的不公平神经元修复以提高深度神经网络的公平性。

7 ITER: Iterative Neural Repair for Multi-Location Patches.
ITER：多位置修补的迭代神经修复。

8 EGFE: End-to-end Grouping of Fragmented Elements in UI Designs with Multimodal Learning.
EGFE：使用多模态学习的UI设计中碎片化元素的端到端分组。

9 A Comprehensive Study of Learning-based Android Malware Detectors under Challenging Environments.
在挑战性环境下基于学习的Android恶意软件检测器的综合研究。

10 Toward Automatically Completing GitHub Workflows.
自动完成GitHub工作流的研究。

11 UniLog: Automatic Logging via LLM and In-Context Learning.
UniLog：通过LLM和上下文学习自动记录日志。

12 Predicting Performance and Accuracy of Mixed-Precision Programs for Precision Tuning.
预测混合精度程序的性能和准确性以进行精度调整。

13 Dataflow Analysis-Inspired Deep Learning for Efficient Vulnerability Detection.
受数据流分析启发的深度学习，用于高效的漏洞检测。

14 Large Language Models for Test-Free Fault Localization.
大型语言模型用于无需测试的故障定位。

15 CrashTranslator: Automatically Reproducing Mobile Application Crashes Directly from Stack Trace.
CrashTranslator：直接从堆栈跟踪自动复现移动应用程序崩溃。

16 Reorder Pointer Flow in Sound Concurrency Bug Prediction.
在声音并发错误预测中重新排序指针流。

17 Object Graph Programming.
对象图编程。

18 Semantic Analysis of Macro Usage for Portability.
宏使用的语义分析以实现可移植性。

19 NuzzleBug: Debugging Block-Based Programs in Scratch.
NuzzleBug：在Scratch中调试基于块的程序。

20 LogShrink: Effective Log Compression by Leveraging Commonality and Variability of Log Data.
LogShrink：通过利用日志数据的共性和可变性实现有效的日志压缩。

21 Demystifying Compiler Unstable Feature Usage and Impacts in the Rust Ecosystem.
揭秘Rust生态系统中编译器不稳定特性的使用和影响。

22 Resource Usage and Optimization Opportunities in Workflows of GitHub Actions.
GitHub Actions工作流中的资源使用和优化机会。

23 Revealing Hidden Threats: An Empirical Study of Library Misuse in Smart Contracts.
揭示隐藏威胁：智能合约中库滥用的实证研究。

24 Fine-SE: Integrating Semantic Features and Expert Features for Software Effort Estimation.
Fine-SE：整合语义特征和专家特征进行软件工作量估计。

25 Kind Controllers and Fast Heuristics for Non-Well-Separated GR(1) Specifications.
用于非良好分离GR(1)规范的友好控制器和快速启发式方法。

26 It's Not a Feature, It's a Bug: Fault-Tolerant Model Mining from Noisy Data.
这不是一个特性，这是一个错误：从噪声数据中挖掘容错模型。

27 Enabling Runtime Verification of Causal Discovery Algorithms with Automated Conditional Independence Reasoning.
通过自动条件独立推理启用因果发现算法的运行时验证。

28 Modularizing while Training: A New Paradigm for Modularizing DNN Models.
训练时模块化：模块化DNN模型的新范式。

29 KnowLog: Knowledge Enhanced Pre-trained Language Model for Log Understanding.
KnowLog：知识增强的预训练语言模型，用于日志理解。

30 FAIR: Flow Type-Aware Pre-Training of Compiler Intermediate Representations.
FAIR：流类型感知的编译器中间表示预训练。

31 Exploring the Potential of ChatGPT in Automated Code Refinement: An Empirical Study.
探索ChatGPT在自动代码优化中的潜力：一项实证研究。

32 Deep Learning or Classical Machine Learning? An Empirical Study on Log-Based Anomaly Detection.
深度学习还是经典机器学习？基于日志的异常检测的实证研究。

33 TRACED: Execution-aware Pre-training for Source Code.
TRACED：执行感知的源代码预训练。

34 CoderEval: A Benchmark of Pragmatic Code Generation with Generative Pre-trained Models.
CoderEval：使用生成式预训练模型的实用代码生成基准。

35 Inferring Data Preconditions from Deep Learning Models for Trustworthy Prediction in Deployment.
从深度学习模型中推断数据前提条件，以便在部署中进行可信预测。

36 Large Language Models are Few-Shot Summarizers: Multi-Intent Comment Generation via In-Context Learning.
大型语言模型是少数镜头的总结器：通过上下文学习生成多意图评论。

37 On Using GUI Interaction Data to Improve Text Retrieval-based Bug Localization.
使用GUI交互数据改进基于文本检索的错误定位。

38 DEMISTIFY: Identifying On-device Machine Learning Models Stealing and Reuse Vulnerabilities in Mobile Apps.
DEMISTIFY：识别移动应用中设备上机器学习模型的窃取和重用漏洞。

39 How do Developers Talk about GitHub Actions? Evidence from Online Software Development Community.
开发人员如何讨论GitHub Actions？来自在线软件开发社区的证据。

40 Block-based Programming for Two-Armed Robots: A Comparative Study.
双臂机器人的基于块的编程：一项比较研究。

41 BOMs Away! Inside the Minds of Stakeholders: A Comprehensive Study of Bills of Materials for Software Systems.
BOMs Away！了解利益相关者的想法：软件系统材料清单的综合研究。

42 EDEFuzz: A Web API Fuzzer for Excessive Data Exposures.
EDEFuzz：一个用于过度数据暴露的Web API模糊测试工具。

43 Detecting Logic Bugs in Graph Database Management Systems via Injective and Surjective Graph Query Transformation.
通过注入和满射图查询转换检测图数据库管理系统中的逻辑错误。

44 Do Automatic Test Generation Tools Generate Flaky Tests?
自动测试生成工具是否生成不稳定的测试？

45 ECFuzz: Effective Configuration Fuzzing for Large-Scale Systems.
ECFuzz：大规模系统的有效配置模糊测试。

46 Improving Testing Behavior by Gamifying IntelliJ.
通过游戏化IntelliJ改善测试行为。

47 SCTrans: Constructing a Large Public Scenario Dataset for Simulation Testing of Autonomous Driving Systems.
SCTrans：构建用于自动驾驶系统模拟测试的大型公共场景数据集。

48 Co-Creation in Fully Remote Software Teams.
完全远程软件团队的共创。

49 A Large-Scale Survey on the Usability of AI Programming Assistants: Successes and Challenges.
关于AI编程助手可用性的大规模调查：成功和挑战。

50 How to Support ML End-User Programmers through a Conversational Agent.
如何通过会话代理支持ML终端用户程序员。

51 Unveiling the Life Cycle of User Feedback: Best Practices from Software Practitioners.
揭示用户反馈的生命周期：软件实践者的最佳实践。

52 Novelty Begets Popularity, But Curbs Participation - A Macroscopic View of the Python Open-Source Ecosystem.
新奇产生了流行，但限制了参与 - Python开源生态系统的宏观视图。

53 Characterizing Software Maintenance Meetings: Information Shared, Discussion Outcomes, and Information Captured.
特征化软件维护会议：共享的信息、讨论结果和捕获的信息。

54 Predicting open source contributor turnover from value-related discussions: An analysis of GitHub issues.
从价值相关讨论预测开源贡献者流失：GitHub问题的分析。

55 On the Helpfulness of Answering Developer Questions on Discord with Similar Conversations and Posts from the Past.
回答Discord上开发人员问题的有用性，以及与过去相似的对话和帖子。

56 Marco: A Stochastic Asynchronous Concolic Explorer.
Marco：一个随机异步的混合测试探索器。

57 Smart Contract and DeFi Security Tools: Do They Meet the Needs of Practitioners?
智能合约和DeFi安全工具：它们是否满足实践者的需求？

58 DocFlow: Extracting Taint Specifications from Software Documentation.
DocFlow：从软件文档中提取污点规范。

59 Toward Improved Deep Learning-based Vulnerability Detection.
朝着改进的基于深度学习的漏洞检测前进。

60 Attention! Your Copied Data is Under Monitoring: A Systematic Study of Clipboard Usage in Android Apps.
注意！您复制的数据正在被监控：Android应用中剪贴板使用的系统研究。

61 PonziGuard: Detecting Ponzi Schemes on Ethereum with Contract Runtime Behavior Graph (CRBG).
PonziGuard：使用合约运行时行为图(CRBG)检测以太坊上的庞氏骗局。

62 FuzzSlice: Pruning False Positives in Static Analysis Warnings through Function-Level Fuzzing.
FuzzSlice：通过函数级模糊测试修剪静态分析警告中的假阳性。

63 LibvDiff: Library Version Difference Guided OSS Version Identification in Binaries.
LibvDiff：在二进制文件中引导OSS版本识别的库版本差异。

64 Prompting Is All You Need: Automated Android Bug Replay with Large Language Models.
只需提示：使用大型语言模型自动重放Android错误。

65 Towards Reliable AI: Adequacy Metrics for Ensuring the Quality of System-level Testing of Autonomous Vehicles.
走向可靠的AI：确保自动驾驶车辆系统级测试质量的充分性指标。

66 Learning-based Widget Matching for Migrating GUI Test Cases.
基于学习的小部件匹配，用于迁移GUI测试用例。

67 Large Language Models are Edge-Case Generators: Crafting Unusual Programs for Fuzzing Deep Learning Libraries.
大型语言模型是边缘案例生成器：为模糊测试深度学习库制作不寻常的程序。

68 Deeply Reinforcing Android GUI Testing with Deep Reinforcement Learning.
使用深度强化学习深度加强Android GUI测试。