Md Khorrom Khan

Assistant Professor | Milwaukee School of Engineering

Milwaukee, WI, UNITED STATES

Md Khorrom Khan has a demonstrated leadership skills and experience in the software industry as a software quality assurance engineer.

Education, Licensure and Certification (3)

Ph.D.: Computer Science and Engineering, University of North Texas 2023

M.S.: Applied Physics, Electronics and Communication Engineering, University of Chittagong, Bangladesh 2011

B.S.: Applied Physics, Electronics and Communication Engineering, University of Chittagong, Bangladesh 2010

Biography

Md Khorrom Khan completed his Ph.D. in Computer Science and Engineering (CSE) from the University of North Texas (UNT), where he also worked as a teaching assistant (TA).

As a teaching assistant, Khan actively engaged in diverse graduate and undergraduate courses, including Computer Science I, Computer Science II, Software Engineering, and Usability Testing. Furthermore, he contributed to developing a tailored Python programming curriculum for non-computer Science students in the Department of Mechanical Engineering, UNT. Beyond his TA role, Khan also served as an instructor for multiple summer camps and provided mentorship in the NSF-funded Research Experience for Undergraduates (REU) program. His commitment and effective teaching approach were recognized with the prestigious “Outstanding Teaching Assistant Award” from the Department of CSE. During Khan's tenure as a graduate student at UNT, he worked under the mentorship of Dr. Renee Bryce at the Research Innovation in Software Engineering (RISE) lab, focusing on innovative Android Graphical User Interface (GUI) testing techniques using reinforcement learning algorithms and novel Test Case Prioritization techniques for Android GUI test suites. The department of CSE at UNT recognized Khan's academic excellence with the “Outstanding Doctoral Student Award”.

Additionally, Khan has held leadership positions in multiple student organizations as a president, such as World Echoes, Bangladesh Student Association, and Association for Computing Machinery at UNT. His contributions as a student leader were acknowledged with the prestigious “UNT Golden Eagle Award”, affirming his exceptional leadership skills and significant impact on the academic and diverse community at UNT.

Areas of Expertise (5)

Computer Security

Database Management

Computer Foundations

Object-Oriented Programming

Data Structures and Algorithms

Accomplishments (5)

Outstanding Teaching Assistant Award (professional)

2023 Department of Computer Science, University of North Texas

UNT Golden Eagle Award (professional)

2023 Division of Student Affairs, University of North Texas

Outstanding Doctoral Student Award (professional)

2022 Department of Computer Science, University of North Texas

Graduate Student Officer of the Year Award (professional)

2020 Division of Student Affairs, University of North Texas

International Education Scholarship (professional)

2019 – 2020 and 2021-2022 International Affairs, University of North Texas

Affiliations (2)

The Institute of Electrical and Electronics Engineers (IEEE)
The Association for Computing Machinery (ACM)

Event and Speaking Appearances (3)

Post Prioritization Techniques to Improve Code Coverage for SARSA Generated Test Cases

IEEE 13th Annual Computing and Communication Workshop and Conference (CCWC) Las Vegas, NV

Android GUI Test Generation with SARSA

IEEE 12th Annual Computing and Communication Workshop and Conference (CCWC) Las Vegas, NV

Reinforcement learning for android gui testing

A-TEST 2018: Proceedings of the 9th ACM SIGSOFT International Workshop on Automating TEST Case Design, Selection, and Evaluation Lake Buena Vista, FL

Selected Publications (5)

Android GUI Test Generation with SARSA

2022 IEEE 12th Annual Computing and Communication Workshop and Conference (CCWC)

2022 Android applications are often challenging to test because of large event spaces with an exponential number of event sequences. Several studies employ reinforcement learning to generate test suites in an effort to optimize code coverage and fault-finding effectiveness under limited testing budgets. In this paper, we generate test cases using the SARSA rein-forcement learning algorithm for seven Android applications, each with a two-hour testing window.

Discovery of Real World Context Event Patterns for Smartphone Devices Using Conditional Random Fields

ITNG 2021 18th International Conference on Information Technology-New Generations

2021 Mobile applications are Event Driven Systems that react to user events and context events (e.g. changes in network connectivity, battery level, etc.) The large number of context events complicate the testing process. Context events may modify several context variables (e.g. screen orientation, connectivity status, etc.) that affect the behavior of an application.

Test Suite Prioritization with Element and Event Sequences for Android Applications

2021 IEEE 11th Annual Computing and Communication Workshop and Conference (CCWC)

2021 Several empirical studies show that test suite prioritization guided by combinatorial-based criteria improves the rate of fault detection for a variety of event-driven systems. This work examines test suite prioritization using novel sequences of elements and events for four Android applications. The results show that the prioritization criteria that use element and event sequences cover the test suite's elements, events, and code faster than random orderings.

Mobile Test Suite Generation via Combinatorial Sequences

ITNG 2021 18th International Conference on Information Technology-New Generations

2021 Mobile applications are event driven systems that are often driven primarily by user interactions through a GUI. The large event space for mobile applications poses challenges for testing. This work considers the architecture of modern mobile applications to generate test cases that systematically incorporate activities, elements, and events in different sequences for testing.

Reinforcement learning for Android GUI testing

A-TEST 2018: Proceedings of the 9th ACM SIGSOFT International Workshop on Automating TEST Case Design, Selection, and Evaluation

2018 This paper presents a reinforcement learning approach to automated GUI testing of Android apps. We use a test generation algorithm based on Q-learning to systematically select events and explore the GUI of an application under test without requiring a preexisting abstract model. We empirically evaluate the algorithm on eight Android applications and find that the proposed approach generates test suites that achieve between 3.31% to 18.83% better block-level code coverage than random test generation.