ESEC/FSE 2023 CoLos
31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE 2023)
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19th International Conference on Predictive Models and Data Analytics in Software Engineering (PROMISE 2023), December 8, 2023, San Francisco, CA, USA

PROMISE 2023 – Proceedings

 Contents - Abstracts - Authors
Twitter: https://twitter.com/esecfse

19th International Conference on Predictive Models and Data Analytics in Software Engineering (PROMISE 2023)

Frontmatter

Title Page
Welcome from the Chairs
PROMISE 2023 Organization

Invited Talk

Harnessing Predictive Modeling and Software Analytics in the Age of LLM-Powered Software Development (Invited Talk)
Foutse Khomh
(Polytechnique Montréal, Canada)
Publisher's Version

Papers

BuggIn: Automatic Intrinsic Bugs Classification Model using NLP and ML
Pragya Bhandari and Gema Rodríguez-Pérez
(University of British Columbia, Canada)
Publisher's Version
Do Developers Fix Continuous Integration Smells?
Ayberk Yaşa, Ege Ergül, Hakan Erdogmus, and Eray Tüzün
(Bilkent University, Turkiye; Carnegie Mellon University, USA)
Publisher's Version
Large Scale Study of Orphan Vulnerabilities in the Software Supply Chain
David Reid, Kristiina Rahkema, and James Walden
(University of Tennessee at Knoxville, USA; University of Tartu, Estonia; Northern Kentucky University, USA)
Publisher's Version
The FormAI Dataset: Generative AI in Software Security through the Lens of Formal Verification
Norbert Tihanyi, Tamas Bisztray, Ridhi Jain, Mohamed Amine Ferrag, Lucas C. Cordeiro, and Vasileios Mavroeidis
(Technology Innovation Institute, United Arab Emirates; University of Oslo, Norway; University of Manchester, UK)
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Comparing Word-Based and AST-Based Models for Design Pattern Recognition
Sivajeet Chand, Sushant Kumar Pandey, Jennifer Horkoff, Miroslaw Staron, Miroslaw Ochodek, and Darko Durisic
(Chalmers University of Technology, Sweden; University of Gothenburg, Sweden; Poznan University, Poland; Volvo Cars, Gothenburg, Sweden)
Publisher's Version
On Effectiveness of Further Pre-training on BERT Models for Story Point Estimation
Sousuke Amasaki
(Okayama Prefectural University, Japan)
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Automated Fairness Testing with Representative Sampling
Umutcan Karakas and Ayse Tosun
(Istanbul Technical University, Turkiye)
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Model Review: A PROMISEing Opportunity
Tim Menzies
(North Carolina State University, USA)
Publisher's Version

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