arXiv subjects

Mohammad D. Alahmadi

Publications and source records attributed to Mohammad D. Alahmadi.

5 recordsLinked to original sources

arXiv2026

VidTutorAssistant: Automating Responses to Programming Tutorial Questions

Programming tutorial videos on YouTube are an important information resource for software developers and students, and their comment sections have evolved into active spaces where viewers ask follow-up questions. The volume of these questions, however, often exceeds what content creators can address, leaving learners without the clarifications they need. We present VidTutorAssistant, a web platform that automates responses to viewer questions on programming video tutorials. VidTutorAssistant implements a retrieval-augmented generation pipeline that extracts a video's transcript, then segments it and embeds it. It then classifies each viewer comment as being a question or non-question, retrieves the most relevant transcript segments to each identified question via cosine similarity, and then generates an answer to the question using an LLM (GPT-4), while grounding the response using the retrieved transcript segments as context. We validate VidTutorAssistant through a study on a subset of 440 user comments selected from a larger dataset of 105,553 comments extracted from 7,522 Python and Java tutorials. VidTutorAssistant is evaluated on various criteria: a) its ability to identify the programming language in a video, achieving a 0.99 accuracy; b) its ability to classify comments into questions and non-questions, reaching a 0.96 accuracy; and c) its ability to produce correct and complete answers to questions, producing 98% correct and 99.5% complete responses, compared with 89% and 90% for the original creators' answers.

cs.SE↗

arXiv2026

The Perception and Impact of Non-inclusive Language in Software Artifacts

Terminology such as "whitelist/blacklist," "master/slave," "man-hours," or "dummy value" has long been part of the technical vocabulary used in software artifacts, including source code, version histories, and documentation. In recent years, however, many of these expressions have been recognized as potentially non-inclusive and unwelcoming to groups historically underrepresented in software development, such as people of color, women, and individuals with disabilities. Consequently, a growing movement within the software industry has sought to replace these terms with more inclusive alternatives. Despite these initiatives, little is empirically known about how software developers perceive such terminology or how its continued use may influence their professional experiences and sense of belonging. This paper addresses the knowledge gap by examining how software developers perceive non-inclusive terminology in software and its perceived impact on team dynamics, productivity, belonging, and well-being. We surveyed open-source contributors and received 1,517 responses, of which 1,212 were complete and analyzed. On average, respondents reported low negative workplace impact overall; however, perceptions and impacts varied by demographic group. Women and non-binary participants, as well as respondents residing in the United States, were more likely to view the terms as non-inclusive. Among those who considered the terminology non-inclusive, non-binary participants reported higher overall negative impacts than male respondents, and female participants reported higher impact specifically on their sense of belonging.

cs.SE↗

arXiv2025

Evaluating Large Language Models for Code Translation: Effects of Prompt Language and Prompt Design

Large language models (LLMs) have shown promise for automated source-code translation, a capability critical to software migration, maintenance, and interoperability. Yet comparative evidence on how model choice, prompt design, and prompt language shape translation quality across multiple programming languages remains limited. This study conducts a systematic empirical assessment of state-of-the-art LLMs for code translation among C++, Java, Python, and C#, alongside a traditional baseline (TransCoder). Using BLEU and CodeBLEU, we quantify syntactic fidelity and structural correctness under two prompt styles (concise instruction and detailed specification) and two prompt languages (English and Arabic), with direction-aware evaluation across language pairs. Experiments show that detailed prompts deliver consistent gains across models and translation directions, and English prompts outperform Arabic by 13-15%. The top-performing model attains the highest CodeBLEU on challenging pairs such as Java to C# and Python to C++. Our evaluation shows that each LLM outperforms TransCoder across the benchmark. These results demonstrate the value of careful prompt engineering and prompt language choice, and provide practical guidance for software modernization and cross-language interoperability.

cs.SE↗

arXiv2025

Optimizing RAG Pipelines for Arabic: A Systematic Analysis of Core Components

Retrieval-Augmented Generation (RAG) has emerged as a powerful architecture for combining the precision of retrieval systems with the fluency of large language models. While several studies have investigated RAG pipelines for high-resource languages, the optimization of RAG components for Arabic remains underexplored. This study presents a comprehensive empirical evaluation of state-of-the-art RAG components-including chunking strategies, embedding models, rerankers, and language models-across a diverse set of Arabic datasets. Using the RAGAS framework, we systematically compare performance across four core metrics: context precision, context recall, answer faithfulness, and answer relevancy. Our experiments demonstrate that sentence-aware chunking outperforms all other segmentation methods, while BGE-M3 and Multilingual-E5-large emerge as the most effective embedding models. The inclusion of a reranker (bge-reranker-v2-m3) significantly boosts faithfulness in complex datasets, and Aya-8B surpasses StableLM in generation quality. These findings provide critical insights for building high-quality Arabic RAG pipelines and offer practical guidelines for selecting optimal components across different document types.

cs.IR↗

arXiv2024

Investigating Developers' Preferences for Learning and Issue Resolution Resources in the ChatGPT Era

The landscape of software developer learning resources has continuously evolved, with recent trends favoring engaging formats like video tutorials. The emergence of Large Language Models (LLMs) like ChatGPT presents a new learning paradigm. While existing research explores the potential of LLMs in software development and education, their impact on developers' learning and solution-seeking behavior remains unexplored. To address this gap, we conducted a survey targeting software developers and computer science students, gathering 341 responses, of which 268 were completed and analyzed. This study investigates how AI chatbots like ChatGPT have influenced developers' learning preferences when acquiring new skills, exploring technologies, and resolving programming issues. Through quantitative and qualitative analysis, we explore whether AI tools supplement or replace traditional learning resources such as video tutorials, written tutorials, and Q&A forums. Our findings reveal a nuanced view: while video tutorials continue to be highly preferred for their comprehensive coverage, a significant number of respondents view AI chatbots as potential replacements for written tutorials, underscoring a shift towards more interactive and personalized learning experiences. Additionally, AI chatbots are increasingly considered valuable supplements to video tutorials, indicating their growing role in the developers' learning resources. These insights offer valuable directions for educators and the software development community by shedding light on the evolving preferences toward learning resources in the era of ChatGPT.

cs.SE↗