PROMISE 2025
Thu 26 Jun 2025 Trondheim, Norway
co-located with FSE 2025
Thu 26 Jun 2025 16:31 - 16:45 at Vega - Session 3 Chair(s): Yinxi Liu

Smart contracts underpin decentralized applications but face significant security risks from vulnerabilities, while traditional analysis methods have limitations. Large Language Models (LLMs) offer promise for vulnerability detection, yet adapting these powerful models efficiently, particularly generative ones, remains challenging. This paper investigates two key strategies for the efficient adaptation of LLMs for Solidity smart contract vulnerability detection: (1) replacing token-level generation with a dedicated classification head during fine-tuning, and (2) selectively freezing lower transformer layers using Low-Rank Adaptation (LoRA). Our empirical evaluation demonstrates that the classification head approach enables models like Llama 3.2 3B to achieve high accuracy (77.5%), rivaling the performance of significantly larger models such as the fine-tuned GPT-3.5. Furthermore, we show that selectively freezing bottom layers reduces training time and memory usage by approximately 10-20% with minimal impact on accuracy. Notably, larger models (3B vs. 1B parameters) exhibit greater resilience to layer freezing, maintaining high accuracy even with a large proportion of layers frozen, suggesting a localization of general code understanding in lower layers versus task-specific vulnerability patterns in upper layers. These findings present practical insights for developing and deploying performant LLM-based vulnerability detection systems efficiently, particularly in resource-constrained settings.

Thu 26 Jun

Displayed time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change

16:00 - 18:00
Session 3PROMISE 2025 at Vega
Chair(s): Yinxi Liu Rochester Institute of Technology
16:00
15m
Talk
Leveraging LLM Enhanced Commit Messages to Improve Machine Learning Based Test Case Prioritization
PROMISE 2025
Yara Q Mahmoud Ontario Tech University, Akramul Azim Ontario Tech University, Ramiro Liscano Ontario Tech University, Kevin Smith International Business Machines Corporation (IBM), Yee-Kang Chang International Business Machines Corporation (IBM), Gkerta Seferi International Business Machines Corporation (IBM), Qasim Tauseef International Business Machines Corporation (IBM)
16:16
14m
Talk
Designing and Optimizing Alignment Datasets for IoT Security: A Synergistic Approach with Static Analysis Insights
PROMISE 2025
Ahmad Al-Zuraiqi Queen's University Belfast, Desmond Greer Queens University 
16:31
14m
Talk
Efficient Adaptation of Large Language Models for Smart Contract Vulnerability Detection
PROMISE 2025
Fadul Sikder Department of Computer Science and Engineering, The University of Texas at Arlington, Jeff Yu Lei University of Texas at Arlington, Yuede Ji Department of Computer Science and Engineering, The University of Texas at Arlington
16:46
14m
Talk
A Combined Approach to Performance Regression Testing Resource Usage Reduction
PROMISE 2025
Milad Abdullah Charles University, David Georg Reichelt Lancaster University Leipzig, Leipzig, Germany, Vojtech Horky Charles University, Lubomír Bulej Charles University, Tomas Bures Charles University, Czech Republic, Petr Tuma Charles University
17:01
14m
Talk
Security Bug Report Prediction Within and Across Projects: A Comparative Study of BERT and Random Forest
PROMISE 2025
Farnaz Soltaniani TU Clausthal, Mohammad Ghafari TU Clausthal, Mohammed Sayagh ETS Montreal, University of Quebec
17:16
9m
Talk
Towards Build Optimization Using Digital Twins
PROMISE 2025
Henri Aïdasso École de technologie supérieure (ÉTS), Francis Bordeleau École de Technologie Supérieure (ETS), Ali Tizghadam TELUS
17:26
4m
Day closing
Closing
PROMISE 2025


Information for Participants
Thu 26 Jun 2025 16:00 - 18:00 at Vega - Session 3 Chair(s): Yinxi Liu
Info for room Vega:

Vega is close to the registration desk.

Facing the registration desk, its entrance is on the left, close to the hotel side entrance.

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