Dr. Khaled Ali Slhoub (pronounced Sal-houb) is an Assistant Professor in the Department of Computer Engineering and Sciences at Florida Institute of Technology. He received his Ph.D. in Computer Science from Florida Institute of Technology in the USA, where his research was on developing a standard framework for formalizing the analysis process of agent-based systems. Dr. Slhoub obtained his M.Sc. from the University of New Brunswick in Canada and his B.Sc. from Benghazi University in Libya. His main research interests are software engineering (software requirements, software testing and quality) and multi-agent systems. Currently, he is focusing on studying and analyzing the quality of existing agent-oriented methodologies in order to provide unified agent-oriented development approaches that can deliver practical means in industrial settings. He is also working on developing a framework to enable understanding and flagging disruptive behavior of distributed social agents (bots) in social networking platforms. In addition, Dr. Slhoub's research is focused on finding effective testing approaches to verify autonomous systems. He attempts to detect ways to verify the irregular behavior of agent-based systems.
Ph.D., Computer Science, Florida Institute of Technology, USA 2018
M.Sc., Computer Science, University of New Brunswick, Canada 2008
B.Sc., Computer Science, University of Benghazi, Libya 1999
- (CSE 4020/CIS 5210) Database Systems
- (CSE 4425) Software Testing
- (CSE 1101) Computing Disciplines and Careers
Software Engineering, Software Testing, Advanced Software Testing, Database Systems, Software Quality and Metrics, Software Development, C++ Programming, Data Structures in C, OOP Concepts (Java), Introduction to Computer Science, Introduction to Computer Programming, Operating Systems, Concepts of Programming Languages, Computer Disciplines and Careers, and Introduction to Engineering
- A. Averza, K. Slhoub and S. Bhattacharyya, "Evaluating the Influence of Twitter Bots via Agent-Based Social Simulation," in IEEE Access, vol. 10, pp. 129394-129407, 2022.
- F. Alsuliman, S. Bhattacharyya, K. Slhoub, N. Nur, & C. Chambers, "Social Media vs. News Platforms: A Cross-analysis for Fake News Detection Using Web Scraping and NLP", In Proceedings of the 15th International Conference on PErvasive Technologies Related to Assistive Environments (PETRA '22), Association for Computing Machinery (ACM), USA, 190–196, 2022.
- B. Wood & K. Slhoub, "Detecting Amazon Bot Reviewers Using Unsupervised and Supervised Learning", (Conf Best Paper Award ), 2022 IEEE World AI IoT Congress (AIIoT), pp. 01-08, 2022.
- K. Slhoub, F. Nembhard & M. Carvalho, "A Metrics Tracking Program for Promoting High-Quality Software Development", IEEE SoutheastCon2019, pp. 1-8, 2019.
- K. Slhoub, M. Carvalho & F. Nembhard, "Evaluation and Comparison of Agent- Oriented Methodologies: A Software Engineering Viewpoint", IEEE SYSCON2019), 2019.
- K. Slhoub & M. Carvalho, "Towards Process Standardization for Requirements Analysis of Agent-Based Systems", Advances in Science, Technology and Engineering Systems Journal, June 2018.
- K. Slhoub, M. Carvalho & W. Bond, "Recommended Practices for the Specification of Multi-Agent Systems Requirements", 8th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conf. (IEEE UEMCON 2017), Columbia University, NY, USA, Oct 2017.
- Khaled Slhoub, "A Software Quality Resource Tool That Improves Quality Management of Scaled-Down Development Environments", Proc. 11th IASTED International Conf. on Software Engineering (IASTED SE 2012), International Association of Science and Technology for Development (IASTED), Crete, Greece, June 2012.
- Khaled Slhoub, "A Strategy That Improves Quality of Software Engineering Projects in Classroom", Proc. 10th International Arab Conf. on Information Technology (ACIT), University of Benghazi, Libya, Dec 2010.
- Khaled Slhoub, "Managing Software Quality in Educational and Small Business Environments (Poster)", Proc. 4th Annual Research Exposition on Information Technology (UNB CS), University of New Brunswick, Faculty of Computer Science, Fredericton, Canada, April 2007.
Recognition & Awards
2022 Recipient of FY23 COES Institutional Research Incentive (IRI)
- Received IRI funding for the software engineering research - extracting software requirements directly from source code, USA
2022 KEEN Rising Star Award
- Named as Florida Tech's 2022 Campus Rising Star by the Kern Entrepreneurial Engineering Network National Organization (KEEN). Also be submitted for review and consideration for the National KEEN Rising Star award https://engineeringunleashed.com/content/2022-campus-keen-rising-stars
2013 Recipient of A Government Scholarship
- Selected by the Ministry of Higher Education and Scientific Research to undertake graduate studies in Computer Science in United State of America, Libya
- Software Engineering
- Software Testing
- Software Requirements
- Software Quality and Metrics
- Reverese Engineering
- Web Engineering
- Software Bots and Misinformation
- Multi-Agent Systems
- Agent-Oriented Software Engineering (AOSE)
I am currently accepting new graduate students (Masters and Doctoral) for the Spring 2024. Please reach out to me if you are interested and have SE/CS or other related background.
Towards a Framework to Extract Software Requirements Directly from Source Code - Funded by FIT-IRI
This research project aims to conduct research in developing an intuitive framework for analyzing source code files and generating requirement specifications to show the functions the code is trying to accomplish. Instead of attempting to comprehend and manually construct requirements of open-source systems or legacy systems, our proposed framework will extract the newly requirements directly from source code. While some attempts exist, a fully functional system that efficiently fulfills this task has not been created. This research effort will help us contribute to the ever-growing body of knowledge concerning requirements elicitation for open-source software or legacy systems.
A Framework to Model and Analyze the Behavior of Social Agents (Bots)
Co-PI: Dr. Siddhartha Bhattacharyya
The influence of social media in our daily lives has increased significantly over the last decade. This is evident from the change in stock prices guided by Reddit or the increase in participation of protesters/activists for a cause or the increase in new job opportunities, known as influencers. On Twitter, bot software can post content, respond to others, retweet content, create relationships with other users, and direct message other accounts. One of the key factors in this increasing influence of social media is the encouragement or information provided by recommending social agents; the phenomenon is known as Computational Propaganda. Computational Propaganda summarizes automated systems that spread fake/false information and make use of data-driven methods to shape public opinion. Politicians, for example, misuse bots in social media platforms to increase voting participation, influence voters to support them, and promote their campaign platform. It has been demonstrated through societal outcomes that Computational Propaganda can have lasting and overreaching influence that might lead to dangerous or unsafe societal outcomes.
This project's goal is to conduct research on developing a framework that leads to a better understanding, monitoring, and managing of the behavior of social agents to assure the integrity and prevent unsafe and unethical actions. This will help us contribute to the ever-growing body of knowledge concerning content manipulation by social agents that use social media to influence the stock market and society in general.
Managing Software Quality in Educational and Small Business Environments
The purpose of this research is to develope a small-scale modality for applying and managing software quality in classroom and small business projects, such as those typically conducted in in-house development environments. The modality consists of a lightweight development methodology along with a set of associated quality metrics and standards. The methodology should utilize several features of Agile-based practices that are well suited for small projects. Simplicity, flexibility, and maintainability are examples of such features. The quality metrics include metrics for tracking product and process quality, specifically risk, product satisfaction, prototype suitability, prototype development duration, workweek, productivity, efficiency, defect density, and maintainability with respect to coding standards and documentation standards. The standards include logging, coding, documentation, and benchmark procedures applied during the proposed development methodology. In addition, an automated tool was being developed to facilitate the application of the proposed modality, called the Software Quality Resource Tool. The tool can be useful in assisting developers in quality analysis activities.