Dear Readers,

It gives me great pleasure to announce the seventh regular issue of 2026. In this issue, 6 papers by 19 authors from 7 countries - Austria, Brazil, Egypt, Germany, India, USA, Vietnam - cover various topical aspects of computer science. In a continuous 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.

As always, I would like to thank all the authors for their sound research and the editorial board members and guest reviewers for their extremely valuable review effort and suggestions for improvement. I also want to thank the readers for their interest in our articles, which is reflected in the consistently high number of user accesses and PDF downloads. These contributions, together with the generous support of the KOALA initiative, maintain the quality of our journal.

In the seventh regular issue, I am very pleased to present the following 6 accepted articles: Sang Suh and Numery Zaber from the USA address in their manuscript the limitations of traditional keyword-based resume screening by proposing an AI-powered framework that leverages contextual sentence embeddings, machine learning, and explainable AI to better capture the semantic relationship between resumes and job descriptions. Experimental results demonstrate that the proposed approach achieves high classification performance while providing transparent and interpretable recommendations for more effective recruitment decisions.

Christian Schlager and Atif Mashkoor from Austria discuss in their research the challenges of time-consuming and costly separate assessments for ASPICE and ASPICE for Cybersecurity in the automotive industry by proposing a unified integrated assessment approach that systematically maps related processes and work products across the two models and schedules them closely together for parallel evaluation. In a real-world case study with two projects, this method achieved a measurable reduction in total assessment time (approximately 1.5 days from the typical 8.5 days) while preserving rigor, quality, and compliance, offering organizations greater efficiency with minimal additional overhead.

In their collaborative research between Vietnam and Germany, Dung Hai Dinh, Quan Nguyen Minh Tran, Quang Huan Dong, and Nicole Ondrusch conduct research on predictive modeling in project management, developing and validating machine learning models to forecast issue closures in the open-source TensorFlow project using data collected from GitHub. The findings suggest that the Lasso regression model, enhanced with advanced feature engineering, achieves the best predictive performance, while time-aware validation indicates the need for further refinement of temporal evaluation.

Vinicius Eduardo Ferreira, João Pedro Vidotti Cesaro, Gabriela Rosa, Edson Oliveira Jr, Gislaine Camila Lapasini Leal, and Renato Balancieri from Brazil investigate in their research the 2024 CrowdStrike Falcon blackout through an integrative multivocal literature review that combines academic and gray literature to analyze its impacts, technical root causes, and lessons learned for software engineering and cybersecurity. The study reveals that inadequate validation processes, weaknesses in update mechanisms, and organizational shortcomings amplified the incident's severity and provides evidence-based recommendations to strengthen software quality assurance, update governance, and the resilience of critical digital infrastructures.

Fatmaelzahra Hamdi, Ramadan Moawad, and Amr Mansour Mohsen from Egypt  conduct a systematic literature review of Automated Program Repair (APR) with Large Language Models (LLMs) to examine the recent advancements, research trends, evaluation practices, and challenges of this promising area. This review provides a comprehensive review of existing LLM-based APR approaches, points out key research gaps and evaluation challenges, and builds a foundation for future research toward more reliable and effective automated program repair.

Sangeetha E and Deny J from India report on the challenges of secure, energy-efficient routing in 6G edge networks by proposing the Energy Optimized Network Route Cluster Bandwidth (EONRCB) framework, which uses Priority Cycle Tags and a Service Level Route Count Rollback Node Aggregator (SLR-CRNA) for recursive, priority-based scheduling. The results show that EONRCB outperforms existing routing techniques, achieving higher throughput, packet delivery ratio, and network lifetime, along with a 20% reduction in energy consumption.

Enjoy Reading!

Best regards,

Christian Gütl, Managing Editor-in-Chief