

# Event Details

- **Event Name**: MSc Thesis Defense by: Nafisha Binte Moin
- **Event Start and End Date**: Mon, 19 Jan, 2026 at 01:00 pm – Mon, 19 Jan, 2026 at 03:00 pm
- **Event Description**: Evaluating Large Language Models to Support Software Engineering Tasks
MSc Thesis Defense by: Nafisha Binte MoinAbout this EventThe School of Computer Science is pleased to present… Evaluating Large Language Models to Support Software Engineering Tasks MSc Thesis Defense by: Nafisha Binte Moin Date: 19th January, 2026 Time: 1:00 pm Location: Lambton Tower Room 3105 Abstract: Large Language Models (LLMs) are increasingly used in software engineering (SE) tasks such as test generation and commit analysis, yet questions remain regarding the effectiveness of retrieval-augmented generation (RAG) techniques and the reliability of LLM outputs in practice. This research addresses these gaps through two complementary studies on automated unit test generation and bug-fix commit annotation. First, we study LLM-based unit test generation, evaluating four prompt strategies and integrating sparse (BM25, BM25L) and dense (SBERT-based FAISS, LSH, ANNOY, and HNSW) retrievers within a RAG framework. Results show that a few-shot instructional prompt achieves the highest correctness (99% pass rate) and branch coverage (72.56%). RAG further improves test robustness and diversity, with dense retrievers, particularly SBERT with HNSW, consistently outperforming sparse approaches. Compared to Pynguin, LLM-generated tests are more executable, structured, and semantically meaningful. Second, we evaluate LLMs as automated annotators for bug-fix commit identification across six GitHub repositories comprising over 23,000 commits. Experiments with GPT-4o and Claude 4.5 Sonnet under zero-shot, few-shot, and RAG configurations show that RAG-enhanced models achieve the best performance, with F1-scores between 0.70 and 0.85 and recall up to 0.95, substantially outperforming keyword-based heuristics. Overall, our results demonstrate that RAG-enhanced LLMs offer an effective and scalable solution for improving both unit test generation and bug-fix commit annotation in real-world SE settings. Datasets and implementations are publicly available. Keywords: Large Language Models, Software Engineering, Unit Test Generation, Retrieval-Augmented Generation, Prompt EngineeringThesis Committee: Defense Chair: Dr. Ikjot Saini Reader 1: Dr. Jessica Chen Reader 2: Dr. Dan Wu Advisor: Dr. Muhammad Asaduzzaman  https://cdn-az.allevents.in/events7/banners/8c6c6c00-f049-11f0-803f-eb2d67ed1927-rimg-w1200-h686-dcffffff-gmir.jpg
- **Event URL**: https://allevents.in/windsor/msc-thesis-defense-by-nafisha-binte-moin/100001980126509694
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## Event venue details

- **city**: Windsor
- **state**: ON
- **country**: Canada
- **location**: Lambton Tower
- **lat**: 42.30543979999999
- **long**: -83.0654242
- **full address**: Lambton Tower, 455 Sunset Avenue, Windsor, Canada

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- **affiliate_id**: 10
- **organizer**: School of Computer Science (https://allevents.in/org/school-of-computer-science/21745948)

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## FAQs

- **Q**: When is the event happening?
  - **A:** Mon, 19 Jan, 2026 at 01:00 pm
- **Q**: Where is the event happening?
  - **A:** Lambton Tower, 455 Sunset Avenue, Windsor, Canada
- **Q**: What is the ticket price?
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- **Q**: Who is organizing the event?
  - **A:** School of Computer Science
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