Dear Readers, 

I am very happy to announce the sixth regular issue of 2026. In this issue, 6 articles by 16 authors from 6 countries (Algeria, Austria, Brazil, Finland, Spain, Türkiye) cover a variety of topical research aspects in computer science. Allow me to express my appreciation to all the authors for their sound research work and to thank the editorial board and guest reviewers for their extremely valuable reviews and suggestions for improvement. This continuous stream of relevant and novel contributions, along with the generous support of the KOALA initiative, helps to maintain the quality of our journal.

In the ongoing effort to further strengthen our journal, I would like to expand the editorial board: If you are a tenured associate professor or above with a strong publication record, you are welcome to apply to join our editorial board. We are also interested in high-quality proposals for special issues on new topics and trends. Please consider yourself and encourage your colleagues to submit high-quality articles or special issues for our journal.

In the sixth regular issue, I am very pleased to introduce the following six accepted articles:

Gema Gutierrez, Moisés Rodríguez, Javier Garzás, and Mario Piattini from Spain address the problem that organizations require high-quality, trustworthy data for digital transformation and AI but often lack structured methods aligned with business goals. Their article proposes OKR4DQ, integrating ISO/IEC 25012-based assessment with OKRs to drive targeted improvements. A real-world case study demonstrates measurable gains in key data quality dimensions using OKR4DQ, showing its effectiveness in improving data reliability while aligning quality initiatives with business performance objectives.

Gerhard Jurasek from Austria discusses the results of a longitudinal research on benefits realization for the implementation of ERP-systems based on a mixed methods approach. Additionally, a concept of benefits controlling is proposed to support benefits realization over the lifetime of an ERP-system in order to exploit benefits ideally without a time lag and to a high extent.

Jefferson Martins, Rodrigo Andrade, and Luis F. Alves Pereira from Brazil evaluate in their study whether LLM-generated implementations of ten classical algorithms, such as Heap Sort and Binary Search, in Java, Python, and C are correct and resource-efficient.  Five open models and six code assistant applications are assessed against human-written code from Rosetta Code and The Algorithms repositories, using Intel's RAPL to measure energy consumption, memory usage, and execution time. The findings show that LLMs generate correct implementations in over 97% of cases and tend to outperform human-written code in energy efficiency, though no single model is consistently superior across all metrics, with human-written code still proving more memory-efficient in Python and faster in Java.

Davut Çulha from Turkey investigates in this article the internal conceptual organization of LLMs by introducing a prompt engineering-based method that identifies maximally divergent conceptual regions within latent conceptual spaces using a geometric conceptual spaces framework. The experimental evaluation across various LLMs revealed substantial differences in conceptual diversity, suggesting that the proposed metric can provide complementary insights into the factual knowledge organization of LLMs.

Youssouf Abda, Zohra Mehenaoui, and Yacine Lafifi from Algeria propose in their research an enhanced FSLSM-based approach for analyzing and personalizing online learning content through the comparison of learners’ behavioral profiles and instructional content characteristics using a fuzzy logic model. The experimental results demonstrated that the proposed adaptive approach significantly improved learners’ cognitive progression and enhanced the alignment between pedagogical content and individual learning preferences.

In a collaborative work between researchers from Brazil and Finland, Edna Dias Canedo,  Gabriel Matheus da Rocha de Oliveira,  Fabiana Freitas Mendes, and  Heloise Acco Tives Bedin investigate in their research the growing challenge of selecting appropriate test generation tools in increasingly complex software development environments by combining a Multivocal Literature Review with a survey of 87 software practitioners to identify, compare, and classify testing tools used in academia and industry. The study contributes a practical reference guide for software practitioners and reveals a significant gap between academic research and industrial adoption, highlighting that practitioners prioritize factors such as integration, traceability, visibility, and maintainability when choosing testing tools. 

Enjoy Reading! 

Best regards, 

Christian Gütl, Managing Editor-in-Chief

Graz University of Technology, Graz, Austria