Applying machine learning to predict software fault proneness using change metrics, static code metrics, and a combination of them YA Alshehri, K Goseva-Popstojanova, DG Dzielski, T Devine SoutheastCon 2018, 1-7, 2018 | 30 | 2018 |
Investigate, identify and estimate the technical debt: a systematic mapping study M BenIdris Available at SSRN 3606172, 2020 | 19 | 2020 |
Enterprise architecture of mobile healthcare for large crowd events AIES Eldein, HH Ammar, DG Dzielski 2017 6th International Conference on Information and Communication …, 2017 | 18 | 2017 |
Prioritizing software components risk: Towards a machine learning-based approach M BenIdris, H Ammar, D Dzielski, WH Benamer Proceedings of the 6th International Conference on Engineering & MIS 2020, 1-11, 2020 | 4 | 2020 |
Refactoring cost estimation for architectural technical debt S Deeb, M BenIdris, H Ammar, D Dzielski International Journal of Software Engineering and Knowledge Engineering 31 …, 2021 | 2 | 2021 |
The technical debt density over multiple releases and the refactoring story M BenIdris, H Ammar, D Dzielski International Journal of Software Engineering and Knowledge Engineering 31 …, 2021 | 2 | 2021 |
SREP+ SAST: A Comparison of Tools for Reverse Engineering Machine Code to Detect Cybersecurity Vulnerabilities in Binary Executables TR Devine, M Campbell, M Anderson, D Dzielski 2022 International Conference on Computational Science and Computational …, 2022 | 1 | 2022 |
A Systematic Mapping Study of the Advancement in Software Vulnerability Forecasting A Gautier, C Whitehead, D Dzielski, T Devine, J Hernandez SoutheastCon 2023, 545-552, 2023 | | 2023 |
Online Software Engineering Graduate Program Case Study: Significant Student Engagement and Career Impact MAM Trujillo, Y Alshehri, DG Dzielski, K Goseva-Popstojanova 2023 ASEE North Central Section Conference, 2023 | | 2023 |