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All Journal Indonesian Journal of Electrical Engineering and Computer Science Journal of Technology and System Information
Faris Sattar Hadi
University of Kufa
Computer Science & IT

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University of Kufa unified laboratories management system using computer application Faris Sattar Hadi; Ali Al Farawn; Ahmed Hazim Alhilali
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i3.pp1617-1623

Abstract

The laboratory organization is essential in the university to maximize system convenience, accuracy, appropriateness, and sustainability. The laboratory data system is an information supplier that aids in arranging data sources and decision-making processes. In this work, unified laboratories management system (ULMS) was presented in the educational field for managing and scheduling lab equipment information. There are two steps in the research process: the first is to examine the old approach for organizing and scheduling laboratory equipment and suggest a better one. The approach adopted in this system is the first come, first served rule governs task priority (FCFS). The second step is creating a computerized system for scheduling and managing laboratory equipment. For programming, C# and structured query language (SQL) are used. This system's design included user and administrative integration (manager, technicians, and researchers). An information application system plan was created because of this research to manage the scheduling information for lab equipment. As a result, it can help the management of the laboratory describe all operational tasks in a way that is considerably simpler and produces outcomes more quickly and accurately.
Intelligent Search and Predictive Modeling Framework for Enhancing Software Reliability and Developer Productivity Faris Sattar Hadi
Journal of Technology and System Information Vol. 3 No. 1 (2026): January
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/jtsi.v3i1.5490

Abstract

The demand for smart automation to improve code quality, error fixing and developer efficiency has grown with the faster growth and complexity of today’s software. This work introduces the Intelligent Search and Predictive Modeling Framework (ISPMF) - an integrated data-driven framework that leverages neural predictive modeling, adaptive human-in-the-loop feedback, and semantic code retrieval to enhance software development. In order to model both syntactic and semantic relations in code, our approach adopts a hybrid Transformer–BiLSTM architecture equipped with retrieval-augmented generation (RAG), which leverages structural information brought from Abstract Syntax Trees (ASTs) and Graph Neural Networks (GNNs). ISPMF significantly improves from state-of-the-art baselines (SequenceR, CoCoNut, and GraphCodeBERT-Repair) on all the crucial metrics based on extensive experimental results conducted on real-world datasets such as Defects4J, QuixBugs and ManySStuBs4J. Our proposed approach decreased mean debugging time by 68%, and had an 83% acceptance rate from developers; it also achieved a Top-1 retrieval accuracy of 0.61, fix correctness of 89%, and compilation pass rate of 94%. This evidence confirms that the framework is scalable, robust and applicable in realistic settings. In addition, ISPMF advances explainable and human-centered AI in software engineering by combining data-driven automation with transparent, adaptive feedback along the development process. This work opens the door to future directions including multi-language repair, reinforcement learning-based adaptability, and next-generation intelligent development environments (IDEs) that seamlessly integrate predictive analytics with developer cognition.
Co-Authors Ahmed Hazim Alhilali Ali Al Farawn