MICHAEL PACHECO

About

Hi! I'm currently a Software Engineering Researcher at Huawei Canada. I am conducting research and development towards solving real-world software engineering problems, and delivering actionable insights for company and team innovation. I am actively working on cyber security via open source software vulnerability detection, including methods such as machine learning (LLMs), data science, data engineering, and data analytics. My work has contributed toward multiple published papers and patents.

I obtained my MSc in Computer Science at Queen's University under the supervision of Dr. Ahmed Hassan and Dr. Gustavo Oliva, as part of the Software Analysis and Intelligence Lab (SAIL). My MSc thesis investigates transaction processing times in the Ethereum blockchain.

My current research interests include Artificial Intelligence for Software Engineering (AI4SE), SE4AI, and Explainable AI (XAI).

Feel free to email me at [email protected], or connect with me on other social media to get in touch.

Recent publications:

• What makes Ethereum blockchain transactions be processed fast or slow? An empirical study. Michael Pacheco, Gustavo A. Oliva, Gopi Krishnan Rajbahadur, and Ahmed E. Hassan. Empirical Software Engineering (EMSE), 2023.

• CoLeFunDa: Explainable Silent Vulnerability Fix Identification. Jiayuan Zhou, Michael Pacheco, Jinfu Chen, Xing Hu, Xin Xia, David Lo, and Ahmed E. Hassan. 45th ACM/IEEE International Conference on Software Engineering (ICSE), 2023.

• Is my transaction done yet? An empirical study of transaction processing times in the Ethereum Blockchain Platform. Michael Pacheco, Gustavo A. Oliva, Gopi Krishnan Rajbahadur, and Ahmed E. Hassan. ACM Transactions on Software Engineering and Methodology (TOSEM), 2022.

• Finding A Needle in a Haystack: Automated Mining of Silent Vulnerability Fixes. Jiayuan Zhou, Michael Pacheco, Zhiyuan Wan, Xin Xia, David Lo, Yuan Wang, and Ahmed E. Hassan. 2021 36th IEEE/ACM International Conference on Automated Software Engineering (ASE), 2021.

Some things I've worked on:

• Redditor Studies - I designed several websites which were part of multiple research projects that focus on the unique activity and usage information of each Redditor participant. The websites are designed as surveys with questions unique to each user, and are asked alongside visualizations of the user's individual Reddit usage information. In collaboration with Dr. Anatoliy Gruzd, Dr. Bree McEwan, and Dr. Elizabeth Dubois.

• Social Media Data Collection Tools - I developed several tools which enable users to extract data from different social media websites with no programming knowledge required. Sites include Reddit (to collect data from comments and submissions between a data range on as pecified Subreddit), Pinterest (to collects data from Pins on a specified user board), Twitter (see My Tweeps), and more. My work has helped researchers in the Social Media Lab with their research and publications, including:

  • "Learning in the Wild: Coding Reddit for Learning and Practice". Caroline Haythornthwaite, Priya Kumar, Anatoliy Gruzd, Sarah Gilbert, Marc Esteve del Valle, and Drew Paulin. Proceedings of the 51st Hawaii International Conference on System Sciences. 2018.
  • "Mapping out Violence Against Women of Influence on Twitter Using the Cyber–Lifestyle Routine Activity Theory". Priya Kumar, Anatoliy Gruzd, and Philip Mai. American behavioral scientist 65.5 (2021): 689-711.

  Majority of these tools have repurposed in Communalytic - a computational social science research tool for studying online communities and discourse. In collaboration with Ryerson University's Social Media Lab and Dr. Anatoliy Gruzd.

• DotaDamus - Predicts the outcome of a Dota 2 game, and also suggests a character to pick to maximize the chance of winning - using machine learning. In collaboration with Andrei Apostoae, a Masters student at University of Edinburgh.

• My Tweeps - A “reverse” Twitter app that offers insights about your tweeps (people who follow you on Twitter) and their interests based on their tweets, accounts they follow, accounts that follow them, and more. In collaboration with Ryerson University's Social Media Lab.

• Reddit Submission Network Visualization - Given a Reddit submission, visualizes user interactions using a basic network graph.

• More on my GitHub, and YouTube!