{"id":20822,"date":"2025-11-02T20:37:35","date_gmt":"2025-11-02T17:37:35","guid":{"rendered":"https:\/\/su.edu.ye\/fcit\/?p=20822"},"modified":"2025-11-02T20:37:35","modified_gmt":"2025-11-02T17:37:35","slug":"masters-degree-awarded-to-ms-ibtisam-abu-heliqah-in-computer-science","status":"publish","type":"post","link":"https:\/\/su.edu.ye\/fcit\/en\/20822\/","title":{"rendered":"Master\u2019s Degree Awarded to Ms. Ibtisam Abu Heliqah in Computer Science"},"content":{"rendered":"

Ms. Ibtisam Nasser Ali Abu Heliqa was awarded a Master’s degree in Computer Science for her thesis titled: A Deep Learning-Based Model for Software Vulnerability Detection, which was submitted to the Department of Computer Science, Faculty of Computer and Information Technology \u2013 Sana\u2019a University. The MA defense was held on Sunday, October 26, 2025.<\/p>\n

The MA Viva-voce Committee, which was formed based on a resolution issued by the Graduate Studies and Scientific Research Council, consisted of the following:
\n#
\nCommittee Members
\nDesignation
\nPosition<\/p>\n

1
\nAssoc. Prof. Ayed Abdulaziz Ali Mohsen
\nExternal Examiner
\nChair<\/p>\n

2
\nProf. Ghaleb Hamoud Al-Jaafari
\nMain Supervisor
\nMember<\/p>\n

3
\nAsst. Prof. Fouad Abdullah Murshid
\nInternal Examiner
\nMember<\/p>\n

The study aimed to develop an intelligent model that integrates formal methods and software correctness metrics within the frameworks of deep learning and large language models (LLMs).<\/p>\n

The Study findings revealed that the proposed model achieved an exceptional detection accuracy rate of 99%, demonstrating its high efficiency and reliability in identifying software vulnerabilities.
\nBased on the findings, the researcher recommended further enhancement of the model to enable automated vulnerability repair in the future.
\nThe defense session was attended by a number of academics, researchers, and interested participants, colleagues and the researcher\u2019s family members.<\/p>\n

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