Table of Contents

• Learning from Mistakes: Understanding Ad-hoc Logs through Analyzing Accidental CommitsYi-Hung Chou, Yiyang Min, April Yi Wang, James A. Jones. 1-13 [doi]
• On the calibration of Just-in-time Defect PredictionXhulja Shahini, Jone Bartel, Klaus Pohl. 14-26 [doi]
• An Empirical Study on Leveraging Images in Automated Bug Report ReproductionDingbang Wang, Zhaoxu Zhang, Sidong Feng, William G. J. Halfond, Tingting Yu 0001. 27-38 [doi]
• It's About Time: An Empirical Study of Date and Time Bugs in Open-Source Python SoftwareShrey Tiwari, Serena Chen, Alexander Joukov, Peter Vandervelde, Ao Li 0009, Rohan Padhye. 39-51 [doi]
• Enhancing Just-In-Time Defect Prediction Models with Developer-Centric FeaturesEmanuela Guglielmi, Andrea D'Aguanno, Rocco Oliveto, Simone Scalabrino. 52-62 [doi]
• Revisiting Defects4J for Fault Localization in Diverse Development ScenariosMd Nakhla Rafi, An Ran Chen, Tse-Hsun Peter Chen, Shaohua Wang 0002. 63-75 [doi]
• Mining Bug Repositories for Multi-Fault ProgramsDylan Callaghan, Bernd Fischer 0002. 76-80 [doi]
• HaPy-Bug - Human Annotated Python Bug Resolution DatasetPiotr Przymus, Mikolaj Fejzer, Jakub Narebski, Radoslaw Wozniak, Lukasz Halada, Aleksander Kazecki, Mykhailo Molchanov, Krzysztof Stencel. 81-85 [doi]
• SPRINT: An Assistant for Issue Report ManagementAhmed Adnan, Antu Saha, Oscar Chaparro. 86-90 [doi]
• Wolves in the Repository: A Software Engineering Analysis of the XZ Utils Supply Chain AttackPiotr Przymus, Thomas Durieux. 91-102 [doi]
• Software Composition Analysis and Supply Chain Security in Apache Projects: an Empirical StudySabato Nocera, Sira Vegas, Giuseppe Scanniello, Natalia Juristo. 103-115 [doi]
• Good practice versus reality: A landscape analysis of Research Software metadata adoption in European Open Science ClustersAnas El Hounsri, Daniel Garijo. 116-128 [doi]
• From Industrial Practices to Academia: Uncovering the Gap in Vulnerability Research and PracticeZhuang Liu, Xing Hu 0008, Jiayuan Zhou, Xin Xia 0001. 129-141 [doi]
• Patch Me If You Can - Securing the Linux KernelGunnar Kudrjavets. 142-143 [doi]
• OSS License Identification at Scale: A Comprehensive Dataset Using World of CodeMahmoud Jahanshahi, David Reid, Adam McDaniel, Audris Mockus. 144-148 [doi]
• SCRUBD: Smart Contracts Reentrancy and Unhandled Exceptions Vulnerability DatasetChavhan Sujeet Yashavant, MitrajSinh Chavda, Saurabh Kumar, Amey Karkare, Angshuman Karmakar. 149-153 [doi]
• ICVul: A Well-labeled C/C++ Vulnerability Dataset with Comprehensive Metadata and VCCsChaomeng Lu, Tianyu Li, Toon Dehaene, Bert Lagaisse. 154-158 [doi]
• A Dataset of Contributor Activities in the NumFocus Open-Source CommunityYouness Hourri, Alexandre Decan, Tom Mens. 159-163 [doi]
• Wild SBOMs: a Large-scale Dataset of Software Bills of Materials from Public CodeLuis Soeiro, Thomas Robert 0001, Stefano Zacchiroli. 164-168 [doi]
• MaLAware: Automating the Comprehension of Malicious Software Behaviours using Large Language Models (LLMs)Bikash Saha, Nanda Rani, Sandeep Kumar Shukla. 169-173 [doi]
• Combining Large Language Models with Static Analyzers for Code Review GenerationImen Jaoua, Oussama Ben Sghaier, Houari A. Sahraoui. 174-186 [doi]
• Harnessing Large Language Models for Curated Code ReviewsOussama Ben Sghaier, Martin Weyssow, Houari Sahraoui. 187-198 [doi]
• SMATCH-M-LLM: Semantic Similarity in Metamodel Matching With Large Language ModelsNafisa Ahmed, Hin Chi Kwok, Mohammad Hamdaqa, Wesley K. G. Assunção. 199-210 [doi]
• How Effective are LLMs for Data Science Coding? A Controlled ExperimentNathalia Nascimento, Everton Guimarães, Sai Sanjna Chintakunta, Santhosh Anitha Boominathan. 211-222 [doi]
• Do LLMs Provide Links to Code Similar to What They Generate? A Study with Gemini and Bing CoPilotDaniele Bifolco, Pietro Cassieri, Giuseppe Scanniello, Massimiliano Di Penta, Fiorella Zampetti. 223-235 [doi]
• Too Noisy To Learn: Enhancing Data Quality for Code Review Comment GenerationChunhua Liu, Hong-Yi Lin, Patanamon Thongtanunam. 236-248 [doi]
• Should Code Models Learn Pedagogically? A Preliminary Evaluation of Curriculum Learning for Real-World Software Engineering TasksKyi Shin Khant, Hong-Yi Lin, Patanamon Thongtanunam. 249-254 [doi]
• RepoChat: An LLM-Powered Chatbot for GitHub Repository Question-AnsweringSamuel Abedu, Laurine Menneron, SayedHassan Khatoonabadi, Emad Shihab. 255-259 [doi]
• Analyzing Dependency Clusters and Security Risks in the Maven Central RepositoryGeorge Lake, Minhaz F. Zibran. 260-264 [doi]
• Chasing the Clock: How Fast Are Vulnerabilities Fixed in the Maven Ecosystem?Md. Fazle Rabbi, Arifa Islam Champa, Rajshakhar Paul, Minhaz F. Zibran. 265-269 [doi]
• Decoding Dependency Risks: A Quantitative Study of Vulnerabilities in the Maven EcosystemCostain Nachuma, Md Mosharaf Hossan, Asif Kamal Turzo, Minhaz F. Zibran. 270-274 [doi]
• Faster Releases, Fewer Risks: A Study on Maven Artifact Vulnerabilities and Lifecycle ManagementMd Shafiullah Shafin, Md. Fazle Rabbi, S. M. Mahedy Hasan, Minhaz F. Zibran. 275-279 [doi]
• Insights into Dependency Maintenance Trends in the Maven EcosystemBarisha Chowdhury, Md. Fazle Rabbi, S. M. Mahedy Hasan, Minhaz F. Zibran. 280-284 [doi]
• Insights into Vulnerability Trends in Maven Artifacts: Recurrence, Popularity, and User BehaviorCourtney Bodily, Eric Hill, Andreas Kramer, Leslie Kerby, Minhaz F. Zibran. 285-289 [doi]
• Understanding Software Vulnerabilities in the Maven Ecosystem: Patterns, Timelines, and RisksMd. Fazle Rabbi, Rajshakhar Paul, Arifa Islam Champa, Minhaz F. Zibran. 290-294 [doi]
• Dependency Update Adoption Patterns in the Maven Software EcosystemBaltasar Berretta, Augustus Thomas, Heather Guarnera. 295-299 [doi]
• Analyzing Vulnerability Overestimation in the Maven EcosystemTaha Draoui, Faten Jebari, Chawki Ben Slimen, Munjaap Uppal, Mohamed Wiem Mkaouer. 300-303 [doi]
• Dependency Dilemmas: A Comparative Study of Independent and Dependent Artifacts in Maven Central EcosystemMehedi Hasan Shanto, Muhammad Asaduzzaman, Manishankar Mondal, Shaiful Chowdhury. 304-308 [doi]
• Cascading Effects: Analyzing Project Failure Impact in the Maven Central EcosystemMina Shehata, Saidmakhmud Makhkamjonoov, Mahad Syed, Esteban Parra. 309-313 [doi]
• Do Developers Depend on Deprecated Library Versions? A Mining Study of Log4jHaruhiko Yoshioka, Sila Lertbanjongngam, Masayuki Inaba, Youmei Fan, Takashi Nakano, Kazumasa Shimari, Raula Gaikovina Kula, Kenichi Matsumoto. 314-318 [doi]
• Mining for Lags in Updating Critical Security Threats: A Case Study of Log4j LibraryHidetake Tanaka, Kazuma Yamasaki, Momoka Hirose, Takashi Nakano, Youmei Fan, Kazumasa Shimari, Raula Gaikovina Kula, Kenichi Matsumoto. 319-323 [doi]
• On the Evolution of Unused Dependencies in Java Project Releases: An Empirical StudyNabhan Suwanachote, Yagut Shakizada, Yutaro Kashiwa, Bin Lin 0008, Hajimu Iida. 324-328 [doi]
• Out of Sight, Still at Risk: The Lifecycle of Transitive Vulnerabilities in MavenPiotr Przymus, Mikolaj Fejzer, Jakub Narebski, Krzysztof Rykaczewski, Krzysztof Stencel. 329-333 [doi]
• Popularity and Innovation in Maven CentralNkiru Ede, Jens Dietrich 0001, Ulrich Zülicke. 334-338 [doi]
• Software Bills of Materials in Maven CentralYogya Gamage, Nadia Gonzalez Fernandez, Martin Monperrus, Benoit Baudry. 339-343 [doi]
• The Ripple Effect of Vulnerabilities in Maven Central: Prevalence, Propagation, and Mitigation ChallengesEhtisham Ul Haq, Song Wang, Robert S. Allison. 344-348 [doi]
• Tracing Vulnerabilities in Maven: A Study of CVE lifecycles and Dependency NetworksCorey Yang-Smith, Ahmad Abdellatif. 349-353 [doi]
• Understanding Abandonment and Slowdown Dynamics in the Maven EcosystemKazi Amit Hasan, Jerin Yasmin, Huizi Hao, Yuan Tian 0008, Safwat Hassan, Steven H. H. Ding. 354-358 [doi]
• Characterizing Packages for Vulnerability PredictionSaviour Owolabi, Francesco Rosati, Ahmad Abdellatif, Lorenzo De Carli. 359-363 [doi]
• Understanding the Popularity of Packages in Maven EcosystemSadman Jashim Sakib, Muhammad Asaduzzaman, Curtis Bright, Cole Morgan. 364-368 [doi]
• Navigating and Exploring Software Dependency Graphs Using GoblinDamien Jaime, Joyce El Haddad, Pascal Poizat. 369-371 [doi]
• 50 Years of Programming Language Evolution through the Software Heritage looking glassAdèle Desmazières, Roberto Di Cosmo, Valentin Lorentz. 372-383 [doi]
• It Works (only) on My Machine: A Study on Reproducibility Smells in Ansible ScriptsGhazal Sobhani, Israat Haque, Tushar Sharma. 384-395 [doi]
• Are the Majority of Public Computational Notebooks Pathologically Non-Executable?Tien Nguyen, Waris Gill, Muhammad Ali Gulzar. 396-407 [doi]
• Understanding Test Deletion in Java ApplicationsSuraj Bhatta, Frank Kendemah, Ajay Kumar Jha. 408-420 [doi]
• A Public Benchmark of REST APIsAlix Decrop, Sara Eraso, Xavier Devroey, Gilles Perrouin. 421-433 [doi]
• What Do Contribution Guidelines Say About Software Testing?Bruna Falcucci, Felipe Gomide, Andre Hora. 434-438 [doi]
• Measuring InnerSource ValueChamindra de Silva, Daniel Izquierdo-Cortazar. 439-440 [doi]
• CoUpJava: A Dataset of Code Upgrade Histories in Open-Source Java RepositoriesKaihang Jiang, Bihui Jin, Pengyu Nie 0001. 441-445 [doi]
• EvoChain: A Framework for Tracking and Visualizing Smart Contract EvolutionIlham A. Qasse, Mohammad Hamdaqa, Björn Þór Jónsson 0001. 446-450 [doi]
• CoDocBench: A Dataset for Code-Documentation Alignment in Software MaintenanceKunal Suresh Pai, Premkumar T. Devanbu, Toufique Ahmed. 451-455 [doi]
• RefExpo: Unveiling Software Project Structures through Advanced Dependency Graph ExtractionVahid Haratian, Pouria Derakhshanfar, Vladimir Kovalenko, Eray Tüzün. 456-460 [doi]
• HyperAST: Incrementally Mining Large Source Code RepositoriesQuentin Le Dilavrec, Andy Zaidman. 461-464 [doi]
• How Much Do Code Language Models Remember? An Investigation on Data Extraction Attacks Before and After Fine-tuningFabio Salerno, Ali Al-Kaswan, Maliheh Izadi. 465-477 [doi]
• Can LLMs Generate Higher Quality Code Than Humans? An Empirical StudyMohammad Talal Jamil, Shamsa Abid, Shafay Shamail. 478-489 [doi]
• Prompt Engineering or Fine-Tuning: An Empirical Assessment of LLMs for CodeJiho Shin, Clark Tang, Tahmineh Mohati, Maleknaz Nayebi, Song Wang 0009, Hadi Hemmati. 490-502 [doi]
• Drawing Pandas: A Benchmark for LLMs in Generating Plotting CodeTimur Galimzyanov, Sergey Titov, Yaroslav Golubev, Egor Bogomolov. 503-507 [doi]
• SnipGen: A Mining Repository Framework for Evaluating LLMs for CodeDaniel Rodríguez-Cardenas, Alejandro Velasco, Denys Poshyvanyk. 508-512 [doi]
• The Ecosystem of Open-Source Music Production Software - A Mining Study on the Development Practices of VST Plugins on GitHubAndrei Bogdan, Mauricio Verano Merino, Ivano Malavolta. 513-525 [doi]
• Can LLMs Replace Manual Annotation of Software Engineering Artifacts?Toufique Ahmed, Premkumar T. Devanbu, Christoph Treude, Michael Pradel. 526-538 [doi]
• Investigating the Understandability of Review Comments on Code Change RequestsMd Shamimur Rahman, Zadia Codabux, Chanchal K. Roy. 539-551 [doi]
• Mining a Decade of Event Impacts on Contributor Dynamics in Ethereum: A Longitudinal StudyMatteo Vaccargiu, Sabrina Aufiero, C. Ba, Silvia Bartolucci, Richard G. Clegg, Daniel Graziotin, Rumyana Neykova, Roberto Tonelli, Giuseppe Destefanis. 552-563 [doi]
• Is it Really Fun? Detecting Low Engagement Events in Video GamesEmanuela Guglielmi, Gabriele Bavota, Nicole Novielli, Rocco Oliveto, Simone Scalabrino. 564-575 [doi]
• A Dataset of Software Bill of Materials for Evaluating SBOM Consumption ToolsRio Kishimoto, Tetsuya Kanda 0001, Yuki Manabe 0001, Katsuro Inoue, Shi Qiu, Yoshiki Higo. 576-580 [doi]
• Jupyter Notebook Activity DatasetTomoki Nakamaru, Tomomasa Matsunaga, Tetsuro Yamazaki. 581-585 [doi]
• CoPhi - Mining C/C++ Packages for Conan Ecosystem AnalysisVivek Sarkar, Anemone Kampkötter, Ben Hermann. 586-590 [doi]
• MARIN: A Research-Centric Interface for Querying Software Artifacts on Maven RepositoriesJohannes Düsing, Jared Chiaramonte, Ben Hermann. 591-595 [doi]
• GitProjectHealth: an Extensible Framework for Git Social Platform MiningNicolas Hlad, Benoît Verhaeghe, Kilian Bauvent. 596-600 [doi]
• MYRIAD PEOPLE Open Source Software for New Media ArtsBenoit Baudry, Erik Natanael Gustafsson, Roni Kaufman, Maria Kling. 601-605 [doi]
• OpenMent: A Dataset of Mentor-Mentee Interactions in Google Summer of CodeErfan Raoofian, Fatemeh H. Fard, Ifeoma Adaji, Gema Rodríguez-Pérez. 606-610 [doi]
• Under the Blueprints: Parsing Unreal Engine's Visual Scripting at ScaleKalvin Eng, Abram Hindle. 611-615 [doi]
• Build Code Needs Maintenance Too: A Study on Refactoring and Technical Debt in Build SystemsAnwar Ghammam, Dhia Elhaq Rzig, Mohamed Almukhtar, Rania Khalsi, Foyzul Hassan, Marouane Kessentini. 616-628 [doi]
• LLMSecConfig: An LLM-Based Approach for Fixing Software Container MisconfigurationsZiyang Ye, Triet Huynh Minh Le, M. Ali Babar. 629-641 [doi]
• How Do Infrastructure-as-Code Practitioners Update Their Dependencies? An Empirical Study on Terraform Module UpdatesMahi Begoug, Ali Ouni 0001, Moataz Chouchen. 642-653 [doi]
• TerraDS: A Dataset for Terraform HCL ProgramsChristoph Bühler, David Spielmann, Roland Meier, Guido Salvaneschi. 654-658 [doi]
• OSPtrack: A Labeled Dataset Targeting Simulated Execution of Open-Source SoftwareZhuoran Tan, Christos Anagnostopoulos 0001, Jeremy Singer. 659-663 [doi]
• CARDS: A collection of package, revision, and miscellaneous dependency graphsEuxane Tran-Girard, Laurent Bulteau, Pierre-Yves David. 664-668 [doi]
• GHALogs: Large-Scale Dataset of GitHub Actions RunsFlorent Moriconi, Thomas Durieux, Jean-Rémy Falleri, Raphaël Troncy, Aurélien Francillon. 669-673 [doi]
• Automatic High-Level Test Case Generation using Large Language ModelsNavid Bin Hasan, Md. Ashraful Islam, Junaed Younus Khan, Sanjida Senjik, Anindya Iqbal. 674-685 [doi]
• Prompting in the Wild: An Empirical Study of Prompt Evolution in Software RepositoriesMahan Tafreshipour, Aaron Imani, Eric Huang, Eduardo Santana de Almeida, Thomas Zimmermann, Iftekhar Ahmed 0001. 686-698 [doi]
• Towards Detecting Prompt Knowledge Gaps for Improved LLM-guided Issue ResolutionRamtin Ehsani, Sakshi Pathak, Preetha Chatterjee. 699-711 [doi]
• Intelligent Semantic Matching (ISM) for Video Tutorial Search using Transformer ModelsAhmad J. Tayeb, Sonia Haiduc. 712-724 [doi]
• Language Models in Software Development Tasks: An Experimental Analysis of Energy and AccuracyNegar Alizadeh, Boris Belchev, Nishant Saurabh, Patricia Kelbert, Fernando Castor. 725-736 [doi]
• TriGraph: A Probabilistic Subgraph-Based Model for Visual Code Completion in Pure DataAnisha Islam, Abram Hindle. 737-749 [doi]
• Inferring Questions from Programming ScreenshotsFaiz Ahmed, Xuchen Tan, Folajinmi Adewole, Suprakash Datta, Maleknaz Nayebi. 750-755 [doi]
• Human-In-The-Loop Software Development Agents: Challenges and Future DirectionsJirat Pasuksmit, Wannita Takerngsaksiri, Patanamon Thongtanunam, Chakkrit Tantithamthavorn, Ruixiong Zhang, Shiyan Wang, Fan Jiang, Jing Li, Evan Cook, Kun Chen, Ming Wu. 756-757 [doi]
• FormalSpecCpp: A Dataset of C++ Formal Specifications created using LLMsMadhurima Chakraborty, Peter Pirkelbauer, Qing Yi. 758-762 [doi]
• PyExamine: A Comprehensive, Un-Opinionated Smell Detection Tool for PythonKarthik Shivashankar, Antonio Martini 0001. 763-774 [doi]
• Does Functional Package Management Enable Reproducible Builds at Scale? YesJulien Malka, Stefano Zacchiroli, Théo Zimmermann. 775-787 [doi]
• Refactoring for Dockerfile Quality: A Dive into Developer Practices and Automation PotentialEmna Ksontini, Meriem Mastouri, Rania Khalsi, Wael Kessentini. 788-800 [doi]
• Smells-sus: Sustainability Smells in IaCSeif Kosbar, Mohammad Hamdaqa. 801-812 [doi]
• Evidence is All We Need: Do Self-Admitted Technical Debts Impact Method-Level Maintenance?Shaiful Chowdhury, Hisham Kidwai, Muhammad Asaduzzaman. 813-825 [doi]
• DPy: Code Smells Detection Tool for PythonAryan Boloori, Tushar Sharma. 826-830 [doi]
• CoMRAT: Commit Message Rationale Analysis ToolMouna Dhaouadi, Bentley James Oakes, Michalis Famelis. 831-835 [doi]
• E2EGit: A Dataset of End-to-End Web Tests in Open Source ProjectsSergio Di Meglio, Luigi Libero Lucio Starace, Valeria Pontillo, Ruben Opdebeeck, Coen De Roover, Sergio Di Martino. 836-840 [doi]
• TestMigrationsInPy: A Dataset of Test Migrations from Unittest to PytestAltino Alves, Andre Hora. 841-845 [doi]
• pyMethods2Test: A Dataset of Python Tests Mapped to Focal MethodsIdriss Abdelmadjid, Robert Dyer 0001. 846-850 [doi]
• DataTD: A Dataset of Java Projects Including Test DoublesMengzhen Li, Mattia Fazzini. 851-855 [doi]
• JPerfEvo: A Tool for Tracking Method-Level Performance Changes in Java ProjectsKaveh Shahedi, Maxime Lamothe, Foutse Khomh, Heng Li 0007. 856-860 [doi]