arXiv subjects

Mridha Md. Nafis Fuad

Publications and source records attributed to Mridha Md. Nafis Fuad.

3 recordsLinked to original sources

arXiv2026

Alteron: A Tool for Behavioral Regression Testing Across NLP Classifier Versions

Evaluating evolving Natural Language Processing (NLP) models is important for ensuring reliable behavior across updates, but standard benchmark metrics do not fully capture how model behavior changes across versions. Existing work has focused mainly on testing models in isolation rather than comparing successive versions in continuous integration workflows. We present Alteron, a tool for detecting behavioral regressions across NLP model versions with metamorphic testing. Alteron constructs a test corpus from labeled source examples and compares model versions on metamorphically transformed inputs. In an evaluation spanning 10 metamorphic relations (MRs), 4 model versions, and 3 model-update transitions, Alteron identified 16 behavioral regressions, 11 of which were release-blocking. The results show that common model updates can preserve overall task performance while still introducing undesirable behavior changes, and that behavioral checks across model versions can reveal failures that aggregate benchmark metrics alone do not capture. The tool is open-source and available at https://github.com/shazzad5709/alteron. A screencast demonstration is available at https://youtu.be/szwiWW5O4do.

cs.SE↗

arXiv2026

PromptMark: A Prompt-Guided Iterative-Feedback Framework for Source Code Watermarking

Watermarking has become a crucial technique for ensuring provenance and accountability in AI-generated source code. As large language models (LLMs) are increasingly integrated into development workflows, reliable attribution remains challenging. In practice, most developers rely on commercial LLM APIs operating under black-box constraints, making existing approaches that require access to the decoding process less feasible for real-world integration. To address this limitation, we propose PromptMark, a black-box, prompt-guided watermarking framework that embeds invisible yet statistically detectable signals into generated code via structured input instructions. The method steers models toward subtle identifier and comment naming patterns while preserving the functional correctness and structural integrity of the generated code. Detection is performed using statistical tests designed to remain reliable across varying code lengths and model outputs. The embedding is further refined through an iterative feedback loop, where prompts are updated based on watermark detection scores. Experiments on the MBPP and HumanEval benchmarks show that PromptMark consistently achieves strong watermark detectability while maintaining high code correctness, outperforming baseline approaches.

cs.CR↗

arXiv2020

Impact on the Productivity of Remotely Working IT Professionals of Bangladesh during the Coronavirus Disease 2019

Similar to the rest of the world, the recent pandemic situation has forced the IT professionals of Bangladesh to adopt remote work. The aim of this study is to find out whether remote work can be continued even after the lockdown is lifted. As work from home may change various productivity related aspects of the employees, i.e., team dynamics and company dynamics, it is necessary to understand the nature of the change during WFH. Conducting a survey, we asked the IT professionals of Bangladesh how they perceive their level of productivity during WFH and how the factors related to productivity have changed. We analyzed the change and identified the areas affected by WFH. We discovered that resource and workspace related issues, emotional well-being of the employees have been hampered the most during WFH. We believe that the findings from this study will help to decide how to resolve those issues and will help to understand whether WFH can be continued even after the lockdown is lifted.

cs.CY↗