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--- 2023-24:fall [2023/08/31 14:30] – ruppert
+++ 2023-24:fall [2023/11/30 20:58] (current) – ruppert
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 **Required skills or prerequisites:**
-Good programming and data analysis skills overall, and experience in using Jupyter and/or R for data analysis.  Ability to work indecently.
+Good programming and data analysis skills overall, and experience in using Jupyter and/or R for data analysis.  Ability to work independently.
 Interest in usable privacy, critical analysis of privacy policies and privacy related regulation.
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 **Recommended skills or prerequisites:**
 Previous C++ experience.
+==== CiteFair: an online tool to detect and mitigate unfairness citation patterns in scientific articles ====
+**Course:**  EECS4080/4088/4090
+**Supervisor:**  Alvine Belle
+**Supervisor's email address:** alvine.belle@lassonde.yorku.ca
+**Project Description:**
+The number of citations of scientific articles has a huge impact on recommendations for funding allocations, recruitment decisions, and rewards, just to name a few. However, some researchers belonging to some socio-cultural groups (e.g., women) are usually less cited than other researchers coming from dominating groups. This may be due to the presence of some unfairness citation patterns in some scientific articles. These citation patterns are tangible examples of biases against researchers from some socio-cultural groups and may inevitably cause unfairness and inaccuracy in the assessment of articles impact. These citations patterns may therefore translate to significant disparities in promotion, retention, grant funding, awards, collaborative opportunities, and publications.
+The project will first start by analyzing the existing scientific literature to find out the various unfairness citations patterns that may be present in some scientific articles. Then, the project will focus on the exploration of existing mitigation solutions and their limitations.
+The project will then aim at developing an online tool called CiteFair that will be able to:
+  - Automatically analyze scientific articles to detect the potential presence of unfairness citation patterns
+  -Rely on existing bibliometric tools to provide some suggestions to articles authors to mitigate these citations patterns and increase the fairness citation score of their articles.
+The project will also consist in validating the accuracy of the CiteFair tool by making experiments on a sample of the scientific articles published within the last decade in a wide range of venues. Experiments will also focus on evaluating the usability and performance of the CiteFair tool.
+**Required skills or prerequisites:**
+Solid experience with JavaScript, HTML, and CSS
+**Recommended skills or prerequisites:**
+Experience with web-development frameworks (e.g., React JS, Spring Boot) and good oral and written skills in English
+==== Large Language Models based Test Case Generation ====
+**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 and 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 test input generations, test oracle generation, and test scenario generation. This project takes a solid initial step towards exploring the next-generation software testing techniques powered by LLMs.
+**Required skills or prerequisites:**
+Be familiar with DL libraries such as Tensorflow and Pytorch;
+**Instructions:**
+Send your c.v. and transcript to supervisor