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--- 2024-25:summer [2024/04/16 00:15] – baljko
+++ 2024-25:summer [2024/04/16 00:31] (current) – [LLM4SE (Large Language Models for Software Engineering)] baljko
@@ Line 6: / Line 6: @@
 This listing is being updated until the start of the summer term.
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+==== AI-Assisted Heath Assessment: Front-End and Back-End Software Development ====
+**[added 2024-04-15]**
+**Course:**  EECS4080
+**Supervisor:**  Ebrahim Ghafar-Zadeh
+**Supervisor's email address:** egz@yorku.ca
+**Project Description:**
+The development of novel point-of-care diagnostic tools for health quality monitoring has recently attracted researchers' attention, particularly utilizing the mobile cell phone platform. The availability of sensors such as tiny cameras in cell phones has created an attractive platform for detecting various health-related parameters. We have recently developed a technology for the assessment of inflammatory diseases by combining medical and engineering techniques. This technology leverages Deep Learning methods to detect features extracted from images captured from microscopes.  In this project, there are several opportunities for computer science and software students to join this multidisciplinary project, including:      Development of a Mobile App to capture images and perform pre-image processing before transferring them to cloud-based software.     Enhancement of DNN (Deep Neural Network) models by studying various methods such as Yolo, Detectron, etc., and applying these new models to improve the accuracy of disease detection.
+**Required skills or prerequisites:**
+  *  Students who have completed their third year and are eligible for capstone projects or other fourth-year projects (e.g. 4080) are eligible for this project.
+  * Specific skills required: This project offers a great opportunity for students interested in applying computer science to medical and life science applications. Proficiency in programming (Python or other comparable programming languages), familiarity with fundamental concepts of App development, and some experience with Artificial Intelligence will be sufficient.
+**Recommended skills or prerequisites:**
+  * Knowledge of multi-thread programming and interprocess communication protocols will be useful
+**Instructions:**
+The interested students are required to submit their CV along with an abstract   Max 300 Words) describing their interests and plans for their future job, as well as a self-description if they require specific accommodations for taking this course. All students, including underrepresented groups, are most welcome to take this course and join my research team.
+----
+==== Design and Implementation of Knowledge Graph Framework ====
+**[added 2024-04-15]**
+**Course:**  EECS4080
+**Supervisor:**  Kostas Kontogiannis
+**Supervisor's email address:** kkontog@yorku.ca
+**Project Description:**
+The idea is to design and implement a graph-based model system which allows for dependencies between entities (e.g. software components, business processes) to be modeled. In addition a reasoning engine is to be implemented so events (e.g. a failure of a process) can be propagated efficiently to the dependent nodes. The project can involve a team of two and is part of a research project.
+**Required skills or prerequisites:**
+  * Excellent Java programming, Excellent Software Design and Architecture skills, very good data base skills
+**Recommended skills or prerequisites:**
+  * Knowledge of multi-thread programming and interprocess communication protocols will be useful
+**Instructions:**
+Please contact Prof. Kostas Kontogiannis at kkontog@yorku.ca and submit an unofficial transcript and your CV.
+----
+==== Large Language Models Based Mutation Testing ====
+**[added 2024-04-15]**
+**Course:**  EECS4080
+**Supervisor:**  Song Wang
+**Supervisor's email address:** wangsong@yorku.ca
+**Project Description:**
+Recently, pre-trained large language models (LLMs) have emerged as a breakthrough technology in natural language processing and artificial intelligence, with the ability to handle large-scale datasets and exhibit remarkable performance across a wide range of tasks. Meanwhile, software testing is a crucial undertaking that serves as a cornerstone for ensuring the quality and reliability of software products. As the scope and complexity of software systems continue to grow, the need for more effective software testing techniques becomes increasingly urgent, making it an area ripe for innovative approaches such as the use of LLMs. Our recent collaboration with Meta also confirms the limitations of existing widely used testing techniques in mutation testing. This project takes a solid initial step toward exploring the next-generation software mutation testing techniques powered by LLMs.
+**Required skills or prerequisites:**
+  * Familiarity with DL libraries such as Tensorflow and Pytorch;
+**Recommended skills or prerequisites:**
+  * Knowledge of Python programming;
+**Instructions:**
+Send c.v. and transcript to the project supervisor.
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