Md Nafiu Rahman

Md Nafiu Rahman, Sadif Ahmed, Zahin Wahab et al.

GitHub and GitLab are widely used collaborative platforms whose issue-tracking systems contain large volumes of unstructured text, including logs, code snippets, and configuration examples. This creates a significant risk of accidental secret exposure, such as API keys and credentials, yet these platforms provide no mechanism to warn users before submission. We present \textsc{IssueGuard}, a tool for real-time detection and prevention of secret leaks in issue reports. Implemented as a Chrome extension, \textsc{IssueGuard} analyzes text as users type and combines regex-based candidate extraction with a fine-tuned CodeBERT model for contextual classification. This approach effectively separates real secrets from false positives and achieves an F1-score of 92.70\% on a benchmark dataset, outperforming traditional regex-based scanners. \textsc{IssueGuard} integrates directly into the web interface and continuously analyzes the issue editor, presenting clear visual warnings to help users avoid submitting sensitive data. The source code is publicly available at \href{https://github.com/disa-lab/IssueGuard}{https://github.com/disa-lab/IssueGuard} , and a demonstration video is available at \href{https://youtu.be/kvbWA8rr9cU}{https://youtu.be/kvbWA8rr9cU} .

Honghao Tan, Md Nafiu Rahman, Shin Hwei Tan

Since 2020, the Linux Foundation and the multi-organization Inclusive Naming Initiative (INI) have encouraged open-source projects to replace non-inclusive terms such as master/slave and whitelist/blacklist. Although these recommendations have been widely adopted, there is limited empirical evidence on their long-term adoption across Linux Foundation (LF) projects or their implications for AI-assisted software development. In this paper, we present NISCAN, a multilingual static-analysis framework that detects non-inclusive terminology across source code and related software artifacts using the INI vocabulary. Using NISCAN, we conduct the first ecosystem-scale study of inclusive naming across 461 Linux Foundation repositories. Our analysis shows that non-inclusive terminology has declined by approximately 47% since 2020, yet adoption remains incomplete: 62.7% of repositories still contain at least one Tier-1 non-inclusive identifier, while most remaining terminology resides outside source code in documentation, comments, configuration files, and other software artifacts. We further show that repository size, programming language, project functionality, and ecosystem are stronger predictors of term inclusiveness in LF repositories rather than foundation governance. To examine the implications for AI-assisted software development, we conduct a case study evaluating whether large language models (LLMs) can reconstruct legacy non-inclusive identifiers from surrounding program context. The results show that historical naming decisions remain embedded in model predictions even after identifiers have been renamed. Overall, our study findings provide the first ecosystem-scale assessment of inclusive naming adoption within the Linux Foundation and highlight the importance of addressing terminology residue to support responsible naming and ethically sourced code generation.

Mahir Labib Dihan, Sadif Ahmed, Md Nafiu Rahman

Bangla is a low-resource language for code generation, lacking large-scale annotated datasets and tools to transform natural language specifications into executable programs. This makes Bangla-to-code generation a challenging task requiring innovative solutions. To address this, we introduce BanglaForge, a novel framework for generating code from Bangla function descriptions. BanglaForge leverages a retrieval-augmented dual-model collaboration paradigm with self-refinement, combining in-context learning, llm-based translation, systematic prompt engineering, and iterative self-refinement based on execution feedback, where a coder generates initial solutions and a reviewer enhances them for robustness. On the BLP-2025 Bangla Code Generation benchmark, BanglaForge achieves a competitive Pass@1 accuracy of 84.00%, demonstrating the effectiveness of retrieval, model collaboration, and self-refinement for low-resource Bangla code generation.