89 citations · 124 across the 12 of their papers we have counts for
12 papers
An Empirical Study of Security Calibration in Large Language Models for Code
Mohammed Latif Siddiq, Md. Nafiu Rahman, Joanna C. S. Santos
Large Language Models (LLMs) are rapidly transforming software development, yet their use in security-critical contexts raises a key question: do models know when their generated c…
An Empirical Study on Remote Code Execution in Machine Learning Model Hosting Ecosystems
Mohammed Latif Siddiq, Tanzim Hossain Romel, Natalie Sekerak +2
Model-sharing platforms, such as Hugging Face, ModelScope, and OpenCSG, have become central to modern machine learning development, enabling developers to share, load, and fine-tun…
Security in the Age of AI Teammates: An Empirical Study of Agentic Pull Requests on GitHub
Mohammed Latif Siddiq, Xinye Zhao, Vinicius Carvalho Lopes +2
Autonomous coding agents are increasingly deployed as AI teammates in modern software engineering, independently authoring pull requests (PRs) that modify production code at scale.…
Assessing the Software Security Comprehension of Large Language Models
Mohammed Latif Siddiq, Natalie Sekerak, Antonio Karam +3
Large language models (LLMs) are increasingly used in software development, but their level of software security expertise remains unclear. This work systematically evaluates the s…
Large Language Models for Software Engineering: A Reproducibility Crisis
Mohammed Latif Siddiq, Arvin Islam-Gomes, Natalie Sekerak +1
Reproducibility is a cornerstone of scientific progress, yet its state in large language model (LLM)-based software engineering (SE) research remains poorly understood. This paper…
Large Language Models in Computer Science Education: A Systematic Literature Review
Nishat Raihan, Mohammed Latif Siddiq, Joanna C. S. Santos +1
Large language models (LLMs) are becoming increasingly better at a wide range of Natural Language Processing tasks (NLP), such as text generation and understanding. Recently, these…
The Fault in our Stars: Quality Assessment of Code Generation Benchmarks
Mohammed Latif Siddiq, Simantika Dristi, Joy Saha +1
Large Language Models (LLMs) are gaining popularity among software engineers. A crucial aspect of developing effective code generation LLMs is to evaluate these models using a robu…
SALLM: Security Assessment of Generated Code
Mohammed Latif Siddiq, Joanna C. S. Santos, Sajith Devareddy +1
With the growing popularity of Large Language Models (LLMs) in software engineers' daily practices, it is important to ensure that the code generated by these tools is not only fun…
FRANC: A Lightweight Framework for High-Quality Code Generation
Mohammed Latif Siddiq, Beatrice Casey, Joanna C. S. Santos
In recent years, the use of automated source code generation utilizing transformer-based generative models has expanded, and these models can generate functional code according to…
Using Large Language Models to Generate JUnit Tests: An Empirical Study
Mohammed Latif Siddiq, Joanna C. S. Santos, Ridwanul Hasan Tanvir +3
A code generation model generates code by taking a prompt from a code comment, existing code, or a combination of both. Although code generation models (e.g., GitHub Copilot) are i…
Enhancing Automated Program Repair through Fine-tuning and Prompt Engineering
Rishov Paul, Md. Mohib Hossain, Mohammed Latif Siddiq +3
Sequence-to-sequence models have been used to transform erroneous programs into correct ones when trained with a large enough dataset. Some recent studies also demonstrated strong…
Shashthosheba: Dissecting Perception of Bangladeshi People towards Telemedicine Apps through the Lens of Features of the Apps
Waqar Hassan Khan, Md Al Imran, Ahmed Nafis Fuad +2
Bangladesh, a developing country with a large and dense population, has recently seen significant economic as well as technological developments. The growth of technology has resul…