A systematic literature review of model-driven engineering using machine learning

AC Marcén, A Iglesias, R Lapeña… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Model-driven engineering (MDE) is a software engineering paradigm based on the
systematic use of models. Over the past few years, engineers have significantly increased …

Development of method for identification the computer system state based on the decision tree with multi-dimensional nodes

SY Gavrylenko, VV Chelak… - … Computer Science, Control, 2022 - ric.zntu.edu.ua
Context. The problem of identifying the state of a computer system is considered. The object
of the research is the process of computer system state identification. The subject of the …

“Will artificial intelligence platforms replace designers in the future?” analyzing the impact of artificial intelligence platforms on the engineering design industry through …

Y Li - Engineering Applications of Artificial Intelligence, 2024 - Elsevier
This research investigates the impact of artificial intelligence platforms on the engineering
design industry by analyzing the perceptions of color and views on artificial intelligence …

A comprehensive MCDM-based approach for object-oriented metrics selection problems

M Maddeh, S Al-Otaibi, S Alyahya, F Hajjej, S Ayouni - Applied Sciences, 2023 - mdpi.com
Object-oriented programming (OOP) is prone to defects that negatively impact software
quality. Detecting defects early in the development process is crucial for ensuring high …

Automated detection of class diagram smells using self-supervised learning

A Alazba, H Aljamaan, M Alshayeb - Automated Software Engineering, 2024 - Springer
Abstract Design smells are symptoms of poorly designed solutions that may result in several
maintenance issues. While various approaches, including traditional machine learning …

Deep learning with class-level abstract syntax tree and code histories for detecting code modification requirements

OO Büyük, A Nizam - Journal of Systems and Software, 2023 - Elsevier
Improving code quality is one of the most significant issues in the software industry. Deep
learning is an emerging area of research for detecting code smells and addressing …

Intelligent system to detect software defects in autonomous cars

S Tanwar, SN Patel, JR Patel, NP Patel… - Authorea …, 2022 - essopenarchive.org
Autonomous cars have become increasingly popular in the last decade because of their
numerous benefits, such as lower travel time, increased safety, and improved fuel economy …

[PDF][PDF] Insights of effectivity analysis of learning-based approaches towards software defect prediction.

D Rai, JA Prashant - International Journal of Electrical & …, 2024 - pdfs.semanticscholar.org
Software defect prediction is one of the essential sets of operation towards mitigating issues
of risk management in software development known to contribute towards enhancing the …

[PDF][PDF] A proposed model for detecting defects in software projects

AN Mahmoud, A Abdelaziz, V Santos… - Indonesian Journal of …, 2024 - novaresearch.unl.pt
Defective modules that cause software execution failures are common in large software
projects. Source code for a significant number of modules may be found in several software …

Detecting Code Smell with a Deep Learning System

A Nizam, MY Avar, ÖK Adaş… - 2023 Innovations in …, 2023 - ieeexplore.ieee.org
Code smell detection is one of the most significant issues in the software industry. Metric-
based static code analysis tools are used to detect undesirable coding practices known as …