Table of Contents

• Combining Static Analysis Techniques for Program Comprehension Using SlicitoRobert Husák, Jan Kofron, Filip Zavoral. 1-5 [doi]
• Log Parsing Using LLMs with Self-Generated In-Context Learning and Self-CorrectionYifan Wu, Siyu Yu, Ying Li. 1-12 [doi]
• Leveraging Multi-Task Learning to Improve the Detection of SATD and VulnerabilityBarbara Russo, Jorge Melegati, Moritz Mock. 1-12 [doi]
• CLCoSum: Curriculum Learning-Based Code Summarization for Code Language ModelsHongkui He, Jiexin Wang, Liuwen Cao, Yi Cai 0001. 1-13 [doi]
• Developing a Taxonomy for Advanced Log Parsing TechniquesIssam Sedki, Abdelwahab Hamou-Lhadj, Otmane Aït Mohamed, Naser Ezzati Jivan. 1-12 [doi]
• Automated Refactoring of Non-Idiomatic Python Code: A Differentiated Replication with LLMSAlessandro Midolo, Massimiliano Di Penta. 1-11 [doi]
• Combining Language and App Ui Analysis for the Automated Assessment of Bug Reproduction StepsJunayed Mahmud, Antu Saha, Oscar Chaparro, Kevin Moran, Andrian Marcus. 1-12 [doi]
• Personalized Code Readability Assessment: Are We There Yet?Antonio Vitale, Emanuela Guglielmi, Rocco Oliveto, Simone Scalabrino. 1-11 [doi]
• Extracting Formal Specifications From Documents Using LLMS for Test AutomationHui Li, Zhen Dong, Siao Wang, Hui Zhang, Liwei Shen, Xin Peng 0001, Dongdong She. 1-12 [doi]
• How Do Papers Make Into Machine Learning Frameworks: a Preliminary Study on TensorflowFederica Pepe, Claudia Farkas, Maleknaz Nayebi, Giuliano Antoniol, Massimiliano Di Penta. 1-6 [doi]
• JavaWiz: A Trace-Based Graphical Debugger for Software Development EducationMarkus Weninger, Simon Grünbacher, Herbert Prähofer. 1-12 [doi]
• Optimizing Code Runtime Performance Through Context-Aware Retrieval-Augmented GenerationManish Acharya, Yifan Zhang, Kevin Leach, Yu Huang. 1-5 [doi]
• A Study on Applying Large Language Models to Issue ClassificationJueun Heo, Seonah Lee 0001. 1-11 [doi]
• Advancing Large Language Models in Code Generation: Usaco Benchmark and Bug Mitigation InsightsJacob Trentini, Victor Liu, Yiming Peng, Ziliang Zong. 1-12 [doi]
• Leveraging Context Information for Self-Admitted Technical Debt DetectionMiki Yonekura, Yutaro Kashiwa, Bin Lin 0008, Kenji Fujiwara, Hajimu Iida. 1-12 [doi]
• On the Possibility of Breaking Copyleft Licenses When Reusing Code Generated by ChatGPTGaia Colombo, Leonardo Mariani, Daniela Micucci, Oliviero Riganelli. 1-12 [doi]
• Effectively Modeling UI Transition Graphs for Android Apps Via Reinforcement LearningWunan Guo, Zhen Dong, Liwei Shen, Daihong Zhou, Bin Hu, Chen Zhang, Hai Xue. 13-24 [doi]
• Characterizing Bugs in Login Processes of Android Applications: An Empirical StudyZixu Zhou, Rufeng Chen, Junfeng Chen, Yepang Liu 0001, Lili Wei 0001. 25-36 [doi]
• Calmdroid: Core-Set Based Active Learning for Multi-Label Android Malware DetectionMinhong Dong, Liyuan Liu, Mengting Zhang, Sen Chen, Wenying He, Ze Wang, Yude Bai. 37-48 [doi]
• Towards Task-Harmonious Vulnerability Assessment Based on LLMZaixing Zhang, Jianming Chang, Tianyuan Hu, Lulu Wang 0001, Bixin Li. 49-59 [doi]
• A Slicing-Based Approach for Detecting and Patching Vulnerable Code ClonesHakam W. Alomari, Christopher Vendome, Himal Gyawali. 60-72 [doi]
• Revisiting Security Practices for Github Actions WorkflowsJiangnan Huang 0001, Bin Lin. 73-77 [doi]
• Sembug: Detecting Logic Bugs in Dbms Through Generating Semantic-Aware Non-Optimizing QueryShiyang Ye, Chao Ni 0001, Jue Wang, Qianqian Pang, Xinrui Li, Xiaodan Xu. 124-135 [doi]
• Pinpointing the Learning Obstacles of an Interactive Theorem ProverSára Juhosová, Andy Zaidman, Jesper Cockx. 159-170 [doi]
• Ai-Based Automated Grading of Source Code of Introductory Programming AssignmentsJayant Havare, Varsha Apte, Kaushikraj Maharajan, Nithin Chandra Gupta Samudrala, Ganesh Ramakrishnan, Srikanth Tamilselvam, Sainath Vavilapalli. 171-181 [doi]
• Students' Program Comprehension Processes in a Large Code BaseAnshul Shah, Thanh Tong, Elena Tomson, Steven Shi, William G. Griswold, Adalbert Gerald Soosai Raj. 182-193 [doi]
• Overlord: A C++ Overloading InspectorBotond István Horváth, Richárd Szalay, Zoltán Porkoláb. 194-198 [doi]
• Investigating Execution-Aware Language Models for Code OptimizationFederico Di Menna, Luca Traini, Gabriele Bavota, Vittorio Cortellessa. 204-215 [doi]
• Understanding Data Access in Microservices Applications Using Interactive TreemapsMaxime ANDRÉ, Marco Raglianti, Anthony Cleve, Michele Lanza. 216-220 [doi]
• Divergence-Driven Debugging: Understanding Behavioral Changes Between Two Program VersionsRémi Dufloer, Imen Sayar, Anne Etien, Steven Costiou. 221-225 [doi]
• Kotsuite: Unit Test Generation for Kotlin Programs in Android ApplicationsFeng Yang, Qi Xin, Zhilei Ren, Jifeng Xuan. 226-236 [doi]
• Optimizing Datasets for Code Summarization: Is Code-Comment Coherence Enough?Antonio Vitale, Antonio Mastropaolo, Rocco Oliveto, Massimiliano Di Penta, Simone Scalabrino. 237-249 [doi]
• Cmdesum: A Cross-Modal Deliberation Network for Code SummarizationZhifang Liao, Xiaoyu Liu, Peng Lan, Song Yu, Pei Liu. 250-261 [doi]
• DLCoG: A Novel Framework for Dual-Level Code Comment Generation Based on Semantic Segmentation and In-Context LearningZhiyang Zhang, Haiyang Yang, Qingyang Yan, Hao Yan, WeiHuan Min, Zhao Wei, Li Kuang, Yingjie Xia. 275-285 [doi]
• Explaining GitHub Actions Failures with Large Language Models: Challenges, Insights, and LimitationsPablo Valenzuela-Toledo, Chuyue Wu, Sandro Hernández, Alexander Boll, Roman Machácek, Sebastiano Panichella, Timo Kehrer. 286-297 [doi]
• Large Language Models Are Qualified Benchmark Builders: Rebuilding Pre-Training Datasets for Advancing Code Intelligence TasksKang Yang 0001, XinJun Mao, Shangwen Wang, Yanlin Wang 0001, Tanghaoran Zhang, Bo Lin, Yihao Qin, Zhang Zhang, Yao Lu, Kamal Al-Sabahi. 298-309 [doi]
• Using Large Language Models to Generate Concise and Understandable Test Case SummariesNatanael Djajadi, Amirhossein Deljouyi, Andy Zaidman. 322-326 [doi]
• Towards Generating the Rationale for Code ChangesFrancesco Casillo, Antonio Mastropaolo, Gabriele Bavota, Vincenzo Deufemia, Carmine Gravino. 327-338 [doi]
• Terminal Lucidity: Envisioning the Future of the TerminalMichael MacInnis, Olga Baysal, Michele Lanza. 339-349 [doi]
• Exploring Code Comprehension in Scientific Programming: Preliminary Insights from Research ScientistsAlyssia Chen, Carol Wong, Bonita Sharif, Anthony Peruma. 350-354 [doi]
• Method Names in Jupyter Notebooks: An Exploratory StudyCarol Wong, Gunnar Larsen, Rocky Huang, Bonita Sharif, Anthony Peruma. 355-366 [doi]
• Scalar: A Part-of-Speech Tagger for IdentifiersChristian D. Newman, Brandon Scholten, Sophia Testa, Joshua A. C. Behler, Syreen Banabilah, Michael L. Collard, Michael John Decker, Mohamed Wiem Mkaouer, Marcos Zampieri, Eman Abdullah AlOmar, Reem S. Alsuhaibani, Anthony Peruma, Jonathan I. Maletic. 367-371 [doi]
• Toward Neurosymbolic Program ComprehensionAlejandro Velasco, Aya Garryyeva, David N. Palacio, Antonio Mastropaolo, Denys Poshyvanyk. 377-381 [doi]
• Mining Code Change Patterns in Ada ProjectsRobin van Straeten, Bin Lin. 387-397 [doi]
• Telling Software Evolution Stories with SonificationCarmen Armenti, Michele Lanza. 398-402 [doi]
• Attributed Multiplex Learning for Analogical Third-Party Library Recommendation and RetrievalBaihui Sang, Liang Wang, Jierui Zhang, XianPing Tao. 403-413 [doi]
• LLM2FedLLM - A Tool for Simulating Federated LLMs for Software Engineering TasksJahnavi Kumar, Siddhartha Gandu, Sridhar Chimalakonda. 414-418 [doi]
• Code Ranking with Structure Awareness Contrastive LearningHailin Huang, Liuwen Cao, Jiexin Wang, Tianchen Yu, Yi Cai. 419-430 [doi]
• Algorithmic Inversion: A Learnable Algorithm Representation for Code GenerationZhongyi Shi, Fuzhang Wu, Weibin Zeng, Yan Kong, Sicheng Shen, Yanjun Wu. 431-441 [doi]
• Studying How Configurations Impact Code Generation in LLMs: The Case of ChatGPTBenedetta Donato, Leonardo Mariani, Daniela Micucci, Oliviero Riganelli. 442-453 [doi]
• Quality In, Quality Out: Investigating Training Data's Role in AI Code GenerationCristina Improta, Rosalia Tufano, Pietro Liguori, Domenico Cotroneo, Gabriele Bavota. 454-465 [doi]
• Enhancing Code Generation for Low-Resource Languages: No Silver BulletAlessandro Giagnorio, Alberto Martin-Lopez, Gabriele Bavota. 478-488 [doi]
• Coft: Making Large Language Models Better Zero-Shot Learners for Code GenerationWeijia Li, Yongjie Qian, Ke Gao, Haixin Chen, Xinyu Wang, Yuchen Tong, Ling Li, Yanjun Wu, Chen Zhao. 489-499 [doi]
• GELog: a GPT-Enhanced Log Representation Method for Anomaly DetectionWenwu Xu, Peng Wang, Haichao Shi, Guoqiao Zhou, Junliang Yao, Xiao-Yu Zhang. 524-535 [doi]
• LLM-BL: Large Language Models are Zero-Shot Rankers for Bug LocalizationZhengliang Li, Zhiwei Jiang, Qiguo Huang, Qing Gu 0001. 548-559 [doi]
• Improved IR-Based Bug Localization with Intelligent Relevance FeedbackAsif Mohammed Samir, Mohammad Masudur Rahman 0001. 560-571 [doi]
• Towards Enhancing IR-Based Bug Localization Leveraging Texts and Multimedia from Bug ReportsShamima Yeasmin, Chanchal K. Roy, Kevin A. Schneider, Mohammad Masudur Rahman 0001, Kartik Mittal, Ryder Hardy. 572-576 [doi]
• Building Bridges, Not Walls: Fairness-Aware and Accurate Recommendation of Code Reviewers via LLm-Based Agents CollaborationLuqiao Wang, Qingshan Li, Di Cui, Mingkang Wang, Yutong Zhao, Yongye Xu, Huiying Zhuang, Yangtao Zhou, Lu Wang. 577-588 [doi]
• Code Review Comprehension: Reviewing Strategies Seen Through Code Comprehension TheoriesPavlína Wurzel Gonçalves, Pooja Rani 0001, Margaret-Anne D. Storey, Diomidis Spinellis, Alberto Bacchelli. 589-601 [doi]