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23rd SCAM 2023: Bogotá, Colombia
- Leon Moonen, Christian D. Newman, Alessandra Gorla:

23rd IEEE International Working Conference on Source Code Analysis and Manipulation, SCAM 2023, Bogotá, Colombia, October 2-3, 2023. IEEE 2023, ISBN 979-8-3503-0506-7 - David Reid, Audris Mockus

:
Applying the Universal Version History Concept to Help De-Risk Copy-Based Code Reuse. 1-12 - Luana Almeida Martins, Heitor A. X. Costa, Márcio Ribeiro, Fabio Palomba, Ivan Machado

:
Automating Test-Specific Refactoring Mining: A Mixed-Method Investigation. 13-24 - Gustavo Vale, Eduardo Fernandes

, Eduardo Figueiredo, Sven Apel:
Behind Developer Contributions on Conflicting Merge Scenarios. 25-36 - Himesh Nandani, Mootez Saad, Tushar Sharma:

Calibrating Deep Learning-based Code Smell Detection using Human Feedback. 37-48 - Cindy Wauters

, Jens Van der Plas
, Quentin Stiévenart
, Coen De Roover
:
Change Pattern Detection for Optimising Incremental Static Analysis. 49-60 - Jiangnan Huang, Bin Lin

:
CIGAR: Contrastive Learning for GitHub Action Recommendation. 61-71 - Md. Saeed Siddik

, Cor-Paul Bezemer:
Do Code Quality and Style Issues Differ Across (Non-)Machine Learning Notebooks? Yes! 72-83 - Sandro Schulze, Phillipp Engelke, Jacob Krüger:

Evolutionary Feature Dependencies: Analyzing Feature Co-Changes in C Systems. 84-95 - Ahmad Haji Mohammadkhani, Chakkrit Tantithamthavorn, Hadi Hemmati:

Explaining Transformer-based Code Models: What Do They Learn? When They Do Not Work? 96-106 - Amirhossein Deljouyi, Andy Zaidman:

Generating Understandable Unit Tests through End-to-End Test Scenario Carving. 107-118 - Oton Pinheiro, Lincoln S. Rocha, Windson Viana

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How They Relate and Leave: Understanding Atoms of Confusion in Open-Source Java Projects. 119-130 - Johannes Villmow, Viola Campos

, Jean Petry, Amine Abbad-Andaloussi
, Adrian Ulges, Barbara Weber:
How Well Can Masked Language Models Spot Identifiers That Violate Naming Guidelines? 131-142 - Hongyu Kuang

, Feng Yang, Long Zhang, Gaigai Tang, Lin Yang:
Leveraging User-Defined Identifiers for Counterfactual Data Generation in Source Code Vulnerability Detection. 143-150 - Mohammed Adib Khan, Morteza Noferesti, Naser Ezzati-Jivan:

PASD: A Performance Analysis Approach Through the Statistical Debugging of Kernel Events. 151-161 - Tao Peng, Shixu Chen, Fei Zhu, Junwei Tang, Junping Liu

, Xinrong Hu:
PTLVD:Program Slicing and Transformer-based Line-level Vulnerability Detection System. 162-173 - Ali Khatami, Andy Zaidman:

Quality Assurance Awareness in Open Source Software Projects on GitHub. 174-185 - Ward Muylaert

, Johannes Härtel
, Coen De Roover
:
Symbolic Execution to Detect Semantic Merge Conflicts. 186-197 - Ruben Opdebeeck

, Jonas Lesy, Ahmed Zerouali, Coen De Roover
:
The Docker Hub Image Inheritance Network: Construction and Empirical Insights. 198-208 - João Eduardo Montandon, Luciana Lourdes Silva, Cristiano Politowski, Ghizlane El-Boussaidi, Marco Túlio Valente:

Unboxing Default Argument Breaking Changes in Scikit Learn. 209-219 - Brittany Reid

, Christoph Treude
, Markus Wagner:
Using the TypeScript compiler to fix erroneous Node.js snippets. 220-230 - Carolin E. Brandt

, Danyao Wang, Andy Zaidman:
When to Let the Developer Guide: Trade-offs Between Open and Guided Test Amplification. 231-241 - Luke Swearngan, Mark Hills

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Enabling Go Program Analysis in Rascal. 242-247 - Philipp Gnoyke

, Sandro Schulze, Jacob Krüger:
On Developing and Improving Tools for Architecture-Smell Tracking in Java Systems. 248-253 - Andrew Chen, Yanfu Yan

, Denys Poshyvanyk:
ACER: An AST-based Call Graph Generator Framework. 254-259 - Faustino Aguilar, Samuel Grayson, Darko Marinov:

Reproducing and Improving the BugsInPy Dataset. 260-264 - Matteo Esposito

, Sergio Moreschini
, Valentina Lenarduzzi
, David Hästbacka, Davide Falessi:
Can We Trust the Default Vulnerabilities Severity? 265-270 - Jonathan Katzy, Maliheh Izadi, Arie van Deursen:

On the Impact of Language Selection for Training and Evaluating Programming Language Models. 271-276 - Matteo Esposito

, Davide Falessi:
Uncovering the Hidden Risks: The Importance of Predicting Bugginess in Untouched Methods. 277-282

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