Table of Contents

• Thirty-Three Years of Mathematicians and Software Engineers: A Case Study of Domain Expertise and Participation in Proof Assistant EcosystemsGwenyth Lincroft, Minsung Cho, Katherine Hough, Mahsa Bazzaz, Jonathan Bell 0001. 1-13 [doi]
• Boosting API Misuse Detection via Integrating API Constraints from Multiple SourcesCan Li, Jingxuan Zhang, Yixuan Tang, Zhuhang Li, Tianyue Sun. 14-26 [doi]
• Availability and Usage of Platform-Specific APIs: A First Empirical StudyRicardo Job, Andre Hora. 27-31 [doi]
• AndroLibZoo: A Reliable Dataset of Libraries Based on Software Dependency AnalysisJordan Samhi, Tegawendé F. Bissyandé, Jacques Klein. 32-36 [doi]
• Goblin: A Framework for Enriching and Querying the Maven Central Dependency GraphDamien Jaime, Joyce El Haddad, Pascal Poizat. 37-41 [doi]
• Dataset: Copy-based Reuse in Open Source SoftwareMahmoud Jahanshahi, Audris Mockus. 42-47 [doi]
• Mining Our Way Back to Incremental Builds for DevOps PipelinesShane McIntosh. 48-49 [doi]
• Enhancing Performance Bug Prediction Using Performance Code MetricsGuoliang Zhao, Stefanos Georgiou, Ying Zou 0001, Safwat Hassan, Derek Truong, Toby Corbin. 50-62 [doi]
• An Investigation of Patch Porting Practices of the Linux Kernel EcosystemXingyu Li, Zheng Zhang, Zhiyun Qian, Trent Jaeger, Chengyu Song. 63-74 [doi]
• CrashJS: A NodeJS Benchmark for Automated Crash ReproductionPhilip Oliver, Jens Dietrich 0001, Craig Anslow, Michael Homer. 75-87 [doi]
• An Empirical Study on Just-in-time Conformal Defect PredictionXhulja Shahini, Andreas Metzger, Klaus Pohl. 88-99 [doi]
• Fine-Grained Just-In-Time Defect Prediction at the Block Level in Infrastructure-as-Code (IaC)Mahi Begoug, Moataz Chouchen, Ali Ouni 0001, Eman Abdullah AlOmar, Mohamed Wiem Mkaouer. 100-112 [doi]
• TrickyBugs: A Dataset of Corner-case Bugs in Plausible ProgramsKaibo Liu, Yudong Han, Yiyang Liu, Jie M. Zhang, Zhenpeng Chen, Federica Sarro, Gang Huang 0001, Yun Ma 0002. 113-117 [doi]
• GitBug-Java: A Reproducible Benchmark of Recent Java BugsAndré Silva, Nuno Saavedra, Martin Monperrus. 118-122 [doi]
• P3: A Dataset of Partial Program PatchesDirk Beyer 0001, Lars Grunske, Matthias Kettl, Marian Lingsch Rosenfeld, Moeketsi Raselimo. 123-127 [doi]
• BugsPHP: A dataset for Automated Program Repair in PHPK. D. Pramod, W. T. N. De Silva, W. U. K. Thabrew, Ridwan Shariffdeen, Sandareka Wickramanayake. 128-132 [doi]
• AW4C: A Commit-Aware C Dataset for Actionable Warning IdentificationZhiPeng Liu, Meng Yan, Zhipeng Gao, Dong Li, Xiaohong Zhang, Dan Yang 0001. 133-137 [doi]
• Predicting the Impact of Crashes Across Release ChannelsSuhaib Mujahid, Diego Elias Costa, Marco Castelluccio. 138-139 [doi]
• Zero-shot Learning based Alternatives for Class Imbalanced Learning Problem in Enterprise Software Defect AnalysisSangameshwar Patil, Balaraman Ravindran. 140-141 [doi]
• ChatGPT Chats Decoded: Uncovering Prompt Patterns for Superior Solutions in Software Development LifecycleLiangxuan Wu, Yanjie Zhao, Xinyi Hou, Tianming Liu, Haoyu Wang 0001. 142-146 [doi]
• Write me this Code: An Analysis of ChatGPT Quality for Producing Source CodeKonstantinos Moratis, Themistoklis Diamantopoulos, Dimitrios-Nikitas Nastos, Andreas L. Symeonidis. 147-151 [doi]
• Quality Assessment of ChatGPT Generated Code and their Use by DevelopersMohammed Latif Siddiq, Lindsay Roney, Jiahao Zhang, Joanna C. S. Santos. 152-156 [doi]
• Analyzing Developer Use of ChatGPT Generated Code in Open Source GitHub ProjectsBalreet Grewal, Wentao Lu, Sarah Nadi, Cor-Paul Bezemer. 157-161 [doi]
• How I Learned to Stop Worrying and Love ChatGPTPiotr Przymus, Mikolaj Fejzer, Jakub Narebski, Krzysztof Stencel. 162-166 [doi]
• Can ChatGPT Support Developers? An Empirical Evaluation of Large Language Models for Code GenerationKailun Jin, Chung-Yu Wang, Hung Viet Pham, Hadi Hemmati. 167-171 [doi]
• The role of library versions in Developer-ChatGPT conversationsRachna Raj, Diego Elias Costa. 172-176 [doi]
• AI Writes, We Analyze: The ChatGPT Python Code SagaMd. Fazle Rabbi, Arifa I. Champa, Minhaz Fahim Zibran, Md Rakibul Islam 0002. 177-181 [doi]
• ChatGPT in Action: Analyzing Its Use in Software DevelopmentArifa I. Champa, Md. Fazle Rabbi, Costain Nachuma, Minhaz F. Zibran. 182-186 [doi]
• Chatting with AI: Deciphering Developer Conversations with ChatGPTSuad Mohamed, Abdullah Parvin, Esteban Parra. 187-191 [doi]
• Does Generative AI Generate Smells Related to Container Orchestration?: An Exploratory Study with Kubernetes ManifestsYue Zhang, Rachel Meredith, Wilson Reeves, Julia Coriolano, Muhammad Ali Babar, Akond Rahman. 192-196 [doi]
• On the Taxonomy of Developers' Discussion Topics with ChatGPTErtugrul Sagdic, Arda Bayram, Md Rakibul Islam. 197-201 [doi]
• How to Refactor this Code? An Exploratory Study on Developer-ChatGPT Refactoring ConversationsEman Abdullah AlOmar, Anushkrishna Venkatakrishnan, Mohamed Wiem Mkaouer, Christian D. Newman, Ali Ouni 0001. 202-206 [doi]
• Analyzing Developer-ChatGPT Conversations for Software Refactoring: An Exploratory StudySoham Deo, Divya Hinge, Omkar Sandip Chavan, Yaxuan Olivia Wang, Mohamed Wiem Mkaouer. 207-211 [doi]
• How Do So ware Developers Use ChatGPT? An Exploratory Study on GitHub Pull RequestsMoataz Chouchen, Narjes Bessghaier, Mahi Begoug, Ali Ouni 0001, Eman Abdullah AlOmar, Mohamed Wiem Mkaouer. 212-216 [doi]
• Investigating the Utility of ChatGPT in the Issue Tracking System: An Exploratory StudyJoy Krishan Das, Saikat Mondal, Chanchal K. Roy. 217-221 [doi]
• Enhancing User Interaction in ChatGPT: Characterizing and Consolidating Multiple Prompts for Issue ResolutionSaikat Mondal, Suborno Deb Bappon, Chanchal K. Roy. 222-226 [doi]
• DevGPT: Studying Developer-ChatGPT ConversationsTao Xiao, Christoph Treude, Hideaki Hata, Kenichi Matsumoto. 227-230 [doi]
• Not all Dockerfile Smells are the Same: An Empirical Evaluation of Hadolint Writing Practices by ExpertsGiovanni Rosa, Simone Scalabrino, Gregorio Robles, Rocco Oliveto. 231-241 [doi]
• Supporting High-Level to Low-Level Requirements Coverage Reviewing with Large Language ModelsAnamaria-Roberta Preda, Christoph Mayr-Dorn, Atif Mashkoor, Alexander Egyed. 242-253 [doi]
• On the Executability of R Markdown FilesMd. Anaytul Islam, Muhammad Asaduzzman, Shaowei Wang. 254-264 [doi]
• APIstic: A Large Collection of OpenAPI MetricsSouhaila Serbout, Cesare Pautasso. 265-277 [doi]
• Improving Automated Code Reviews: Learning from ExperienceHong-Yi Lin, Patanamon Thongtanunam, Christoph Treude, Wachiraphan Charoenwet. 278-283 [doi]
• Multi-faceted Code Smell Detection at Scale using DesigniteJava 2.0Tushar Sharma 0001. 284-288 [doi]
• SATDAUG - A Balanced and Augmented Dataset for Detecting Self-Admitted Technical DebtEdi Sutoyo, Andrea Capiluppi. 289-293 [doi]
• Curated Email-Based Code Reviews DatasetsMingzhao Liang, Wachiraphan Charoenwet, Patanamon Thongtanunam. 294-298 [doi]
• TestDossier: A Dataset of Tested Values Automatically Extracted from Test ExecutionAndré C. Hora. 299-303 [doi]
• Greenlight: Highlighting TensorFlow APIs Energy FootprintSaurabhsingh Rajput, Maria Kechagia, Federica Sarro, Tushar Sharma 0001. 304-308 [doi]
• Automating GUI-based Test Oracles for Mobile AppsKesina Baral, Jack Johnson, Junayed Mahmud, Sabiha Salma, Mattia Fazzini, Julia Rubin, Jeff Offutt, Kevin Moran. 309-321 [doi]
• Global Prosperity or Local Monopoly? Understanding the Geography of App PopularityLiu Wang, Conghui Zheng, Haoyu Wang, Xiapu Luo, Gareth Tyson, Yi Wang, Shangguang Wang. 322-334 [doi]
• GuiEvo: Automated Evolution of Mobile Application GUIsSabiha Salma, SM Hasan Mansur, Yule Zhang, Kevin Moran. 335-347 [doi]
• Comparing Apples to Androids: Discovery, Retrieval, and Matching of iOS and Android Apps for Cross-Platform AnalysesMagdalena Steinböck, Jakob Bleier, Mikka Rainer, Tobias Urban, Christine Utz, Martina Lindorfer. 348-360 [doi]
• Keep Me Updated: An Empirical Study on Embedded JavaScript Engines in Android AppsElliott Wen, Jiaxiang Zhou, Xiapu Luo, Giovanni Russello, Jens Dietrich 0001. 361-372 [doi]
• Large Language Model vs. Stack Overflow in Addressing Android Permission Related ChallengesShahrima Jannat Oishwee, Natalia Stakhanova, Zadia Codabux. 373-383 [doi]
• DATAR: A Dataset for Tracking App ReleasesYasaman Abedini, Mohammad Hadi Hajihosseini, Abbas Heydarnoori. 384-388 [doi]
• AndroZoo: A Retrospective with a Glimpse into the FutureMarco Alecci, Pedro Jesús Ruiz Jiménez, Kevin Allix, Tegawendé F. Bissyandé, Jacques Klein. 389-393 [doi]
• Whodunit: Classifying Code as Human Authored or GPT-4 generated- A case study on CodeChef problemsOseremen Joy Idialu, Noble Saji Mathews, Rungroj Maipradit, Joanne M. Atlee, Meiyappan Nagappan. 394-406 [doi]
• GIRT-Model: Automated Generation of Issue Report TemplatesNafiseh Nikeghbal, Amir Hossein Kargaran, Abbas Heydarnoori. 407-418 [doi]
• MicroRec: Leveraging Large Language Models for Microservice RecommendationAhmed Saeed Alsayed, Hoa Khanh Dam, Chau Nguyen. 419-430 [doi]
• PeaTMOSS: A Dataset and Initial Analysis of Pre-Trained Models in Open-Source SoftwareWenxin Jiang 0001, Jerin Yasmin, Jason Jones, Nicholas Synovic, Jiashen Kuo, Nathaniel Bielanski, Yuan Tian 0008, George K. Thiruvathukal, James C. Davis 0001. 431-443 [doi]
• Data Augmentation for Supervised Code Translation LearningBinger Chen, Jacek Golebiowski, Ziawasch Abedjan. 444-456 [doi]
• On the Effectiveness of Machine Learning-based Call Graph Pruning: An Empirical StudyAmir M. Mir, Mehdi Keshani, Sebastian Proksch. 457-468 [doi]
• Leveraging GPT-like LLMs to Automate Issue LabelingGiuseppe Colavito, Filippo Lanubile, Nicole Novielli, Luigi Quaranta. 469-480 [doi]
• Exploring the Effect of Multiple Natural Languages on Code Suggestion Using GitHub CopilotKei Koyanagi, Dong Wang, Kotaro Noguchi, Masanari Kondo, Alexander Serebrenik, Yasutaka Kamei, Naoyasu Ubayashi. 481-486 [doi]
• A Four-Dimension Gold Standard Dataset for Opinion Mining in Software EngineeringMd Rakibul Islam 0002, Md. Fazle Rabbi, Youngeun Jo, Arifa I. Champa, Ethan Young, Camden Wilson, Gavin Scott, Minhaz Fahim Zibran. 487-491 [doi]
• Opening the Valve on Pure-Data: Usage Patterns and Programming Practices of a Data-Flow Based Visual Programming LanguageAnisha Islam, Kalvin Eng, Abram Hindle. 492-497 [doi]
• The PIPr Dataset of Public Infrastructure as Code ProgramsDaniel Sokolowski, David Spielmann, Guido Salvaneschi. 498-503 [doi]
• A Dataset of Microservices-based Open-Source ProjectsDario Amoroso d'Aragona, Alexander Bakhtin, Xiaozhou Li 0002, Ruoyu Su, Lauren Adams, Ernesto Aponte, Francis Boyle, Patrick Boyle, Rachel Koerner, Joseph Lee, Fangchao Tian, Yuqing Wang, Jesse Nyyssölä, Ernesto Quevedo, Md Shahidur Rahaman, Amr S. Abdelfattah, Mika Mäntylä, Tomás Cerný, Davide Taibi 0001. 504-509 [doi]
• SensoDat: Simulation-based Sensor Dataset of Self-driving CarsChristian Birchler, Cyrill Rohrbach, Timo Kehrer, Sebastiano Panichella. 510-514 [doi]
• Incivility in Open Source Projects: A Comprehensive Annotated Dataset of Locked GitHub Issue ThreadsRamtin Ehsani, Mia Mohammad Imran, Robert Zita, Kostadin Damevski, Preetha Chatterjee. 515-519 [doi]
• A Dataset of Atoms of Confusion in the Android Open Source ProjectDavi Tabosa, Oton Pinheiro, Lincoln S. Rocha, Windson Viana. 520-524 [doi]
• PlayMyData: a curated dataset of multi-platform video gamesAndrea D'Angelo, Claudio Di Sipio, Cristiano Politowski, Riccardo Rubei. 525-529 [doi]
• Questioning the Questions We Ask About the Impact of AI on Software Engineering : MSR 2024 KeynoteMargaret-Anne D. Storey. 530 [doi]
• Learning to Predict and Improve Build Successes in Package EcosystemsHarshitha Menon, Daniel Nichols, Abhinav Bhatele, Todd Gamblin. 531-542 [doi]
• The Impact of Code Ownership of DevOps Artefacts on the Outcome of DevOps CI BuildsAjiromola Kola-Olawuyi, Nimmi Rashinika Weeraddana, Meiyappan Nagappan. 543-555 [doi]
• A Mutation-Guided Assessment of Acceleration Approaches for Continuous Integration: An Empirical Study of YourBaseZhili Zeng, Tao Xiao, Maxime Lamothe, Hideaki Hata, Shane McIntosh. 556-568 [doi]
• Cohort Studies for Mining Software RepositoriesNyyti Saarimäki, Sira Vegas, Valentina Lenarduzzi, Davide Taibi 0001, Mikel Robredo. 569-570 [doi]
• Unveiling ChatGPT's Usage in Open Source Projects: A Mining-based StudyRosalia Tufano, Antonio Mastropaolo, Federica Pepe, Ozren Dabic, Massimiliano Di Penta, Gabriele Bavota. 571-583 [doi]
• DRMiner: A Tool For Identifying And Analyzing Refactorings In DockerfileEmna Ksontini, Aycha Abid, Rania Khalsi, Marouane Kessentini. 584-594 [doi]
• A Large-Scale Empirical Study of Open Source License Usage: Practices and ChallengesJiaQi Wu, Lingfeng Bao, Xiaohu Yang 0001, Xin Xia 0001, Xing Hu 0008. 595-606 [doi]
• Analyzing the Evolution and Maintenance of ML Models on Hugging FaceJoel Castaño, Silverio Martínez-Fernández, Xavier Franch, Justus Bogner. 607-618 [doi]
• On the Anatomy of Real-World R Code for Static AnalysisFlorian Sihler, Lukas Pietzschmann, Raphael Straub, Matthias Tichy, Andor Diera, Abdelhalim Dahou. 619-630 [doi]
• Encoding Version History Context for Better Code RepresentationHuy Nguyen, Christoph Treude, Patanamon Thongtanunam. 631-636 [doi]
• CodeLL: A Lifelong Learning Dataset to Support the Co-Evolution of Data and Language Models of CodeMartin Weyssow, Claudio Di Sipio, Davide Di Ruscio, Houari A. Sahraoui. 637-641 [doi]
• Bidirectional Paper-Repository Tracing in Software EngineeringDaniel Garijo, Miguel Arroyo, Esteban-González, Christoph Treude, Nicola Tarocco. 642-646 [doi]
• DistilKaggle: A Distilled Dataset of Kaggle Jupyter NotebooksMojtaba Mostafavi Ghahfarokhi, Arash Asgari, Mohammad Abolnejadian, Abbas Heydarnoori. 647-651 [doi]
• Estimating Usage Of Open Source ProjectsSophia Vargas, Georg J. P. Link, Jayoung Lee. 652-653 [doi]
• Options Matter: Documenting and Fixing Non-Reproducible Builds in Highly-Configurable SystemsGeorges Aaron Randrianaina, Djamel Eddine Khelladi, Olivier Zendra, Mathieu Acher. 654-664 [doi]
• How do Machine Learning Projects use Continuous Integration Practices? An Empirical Study on GitHub ActionsJoão Helis Bernardo, Daniel Alencar da Costa, Sérgio Queiroz de Medeiros, Uirá Kulesza. 665-676 [doi]
• A dataset of GitHub Actions workflow historiesGuillaume Cardoen, Tom Mens, Alexandre Decan. 677-681 [doi]
• gawd: A Differencing Tool for GitHub Actions WorkflowsPooya Rostami Mazrae, Alexandre Decan, Tom Mens. 682-686 [doi]
• RABBIT: A tool for identifying bot accounts based on their recent GitHub event historyNatarajan Chidambaram, Tom Mens, Alexandre Decan. 687-691 [doi]
• Quantifying Security Issues in Reusable JavaScript Actions in GitHub WorkflowsHassan Onsori Delicheh, Alexandre Decan, Tom Mens. 692-703 [doi]
• What Can Self-Admitted Technical Debt Tell Us About Security? A Mixed-Methods StudyNicolás E. Díaz Ferreyra, Mojtaba Shahin, Mansooreh Zahedi, Sodiq Quadri, Riccardo Scandariato. 704-715 [doi]
• Are Latent Vulnerabilities Hidden Gems for Software Vulnerability Prediction? An Empirical StudyTriet Huynh Minh Le, Xiaoning Du 0001, Muhammad Ali Babar 0001. 716-727 [doi]
• MalwareBench: Malware samples are not enoughNusrat Zahan, Philipp Burckhardt, Mikola Lysenko, Feross Aboukhadijeh, Laurie A. Williams. 728-732 [doi]
• Hash4Patch: A Lightweight Low False Positive Tool for Finding Vulnerability Patch CommitsSimone Scalco, Ranindya Paramitha. 733-737 [doi]
• MegaVul: A C/C++ Vulnerability Dataset with Comprehensive Code RepresentationsChao Ni 0001, Liyu Shen, Xiaohu Yang 0001, Yan Zhu, Shaohua Wang. 738-742 [doi]