New Trends in Computer Sciences

Implementing a rapid application development course in higher education and measuring its impact using Kirkpatrick’s model: a case study at Vilnius Gediminas Technical University

Urtė Radvilaitė, Diana Kalibatienė, Jelena Stankevič — Mon, 30 Sep 2024 00:00:00 +0300

Nowadays, technological development and improvement in business is happening rapidly, so higher education (HE), and not only, studies should constantly provide and develop new up-to-date knowledge and skills to students, in order to train competitive specialists, address digital transformation by developing digital readiness of higher institutions, and increase employment opportunities of students. Consequently, this paper discusses the implementation of the newly developed courses for teaching Rapid Application Development (RAD) on the Oracle Application Express platform into the studies at Vilnius Gediminas Technical University (VILNIUS TECH) and presents the effectiveness of the implementation of this course measured using Kirkpatrick’s model. The obtained results show that students’ knowledge of RAD increased after attending the course. In addition, a 76% agreed that this course increased their knowledge of the subject matter.

Just-In-Time Software Defect Prediction using a deep learning-based model

Rodrigo Alexandre Dos Santos — Tue, 31 Dec 2024 00:00:00 +0200

The increase in software complexity, driven by technological developments and user demands, has created major challenges for companies in Software Quality Assurance. Companies seek efficient ways to identify and mitigate defects, recognizing that they cause high financial costs and other problems with negative impacts on business. Among defect prediction approaches, Just-In-Time Software Defect Prediction has received increased attention from software industry professionals in recent years. This technique aims to identify and treat defects early, to improve the quality of the software development cycle. This study proposes a Deep Learning-based approach for Just-In-Time Software Defect Prediction using a large dataset of historical data from several popular software projects. The Deep Learning model was trained to identify defects by analyzing the software metrics provided by the dataset. The model achieved an accuracy of 82.08% in its predictions, and it was possible to determine the most relevant metrics for its conclusions through interpretability techniques. The results obtained demonstrate the potential of Just-In-Time Software Defect Prediction as a tool for improving software quality and encouraging the development of new studies and improvements in this area of research.

First published online 17 January 2025

Prediction of simulated factory layout throughput using artificial intelligence

Patrick Eschemann, Astrid Nieße, Jürgen Sauer — Tue, 31 Dec 2024 00:00:00 +0200

The use of artificial neural networks for the optimisation of factory layouts is not a common practice, primarily due to the challenge of collecting sufficient layout data to form datasets for artificial intelligence (AI) model training. This paper presents a supervised learning method derived from a PhD thesis that employs neural networks to assess factory layouts. The training data is generated using a random layout algorithm, which is capable of producing numerous layouts. These layouts are then labeled through a discrete event simulation. The combination of layouts and simulation metrics serves as the training basis for the neural network. The AI framework integrates a convolutional neural network with a multilayer perceptron, which is capable of handling both tabular and image data. Ultimately, this allows us to calculate of the simulated throughput.

First published online 20 January 2025