4 papers · 1 filter
Towards Understanding the Impact of Data Bugs on Deep Learning Models in Software Engineering
Mehil B Shah, Mohammad Masudur Rahman, Foutse Khomh
Deep learning (DL) techniques have achieved significant success in various software engineering tasks (e.g., code completion by Copilot). However, DL systems are prone to bugs from…
On the Prevalence, Evolution, and Impact of Code Smells in Simulation Modelling Software
Riasat Mahbub, Mohammad Masudur Rahman, Muhammad Ahsanul Habib
Simulation modelling systems are routinely used to test or understand real-world scenarios in a controlled setting. They have found numerous applications in scientific research, en…
Harmonic LLMs are Trustworthy
Nicholas S. Kersting, Mohammad Rahman, Suchismitha Vedala +1
We introduce an intuitive method to test the robustness (stability and explainability) of any black-box LLM in real-time via its local deviation from harmoniticity, denoted as .…
Towards Enhancing the Reproducibility of Deep Learning Bugs: An Empirical Study
Mehil B. Shah, Mohammad Masudur Rahman, Foutse Khomh
Context: Deep learning has achieved remarkable progress in various domains. However, like any software system, deep learning systems contain bugs, some of which can have severe imp…