Special Issue on Evaluation and Assessment in Industrial Software Engineering Submission Date: 2025-06-09 In recent years, industrial software engineering has increasingly embraced evidence-based practices to address the complex challenges of designing, developing, and maintaining software systems in dynamic, high-stakes environments. This shift reflects a growing recognition of the value of empirical insights and experimental methods to inform decision-making, optimize processes, and drive innovation. However, bridging the gap between academic research and industrial application remains a key challenge. Industry practitioners often face unique constraints and priorities that demand adaptable, validated, and scalable solutions rooted in real-world evidence.

Guest editors:

Professor Helena Holmström Olsson,

Department of Computer Science and Media Technology

Malmö University, Malmö Sweden

Dr. Gabriele De Vito,

Department of Computer Science

University of Salerno, Fisciano, Italy

Special issue information:

This special issue aims to offer practitioners and researchers insights, innovations, and solutions for enhancing software engineering practices through evidence-based approaches.

We both invite extended versions of the papers presented at EASE 2024, and solicit novel submissions that addresses practical challenges in industrial software engineering. Applied research reports and practitioner insights that provide real-world experiences, challenges, and lessons learned, including empirical studies, case studies, and actionable methods or tools relevant to industry are welcome.

Topics of interest include, but are not limited to:

Empirical assessment of development technologies, including cost-benefit analysis in industrial contexts.

Cross- and multi-disciplinary methods and studies that enhance software engineering practices.

Experimental studies, including case studies, action-research, design-science, field studies, and multi-level research designs.

Development and evaluation of empirical prediction systems or software estimation models.

Empirically-based decision making and its impact on software engineering processes.

Evaluation and comparison of techniques and models in industrial environments.

Experimentation in industrial settings, including A/B testing, quasi-experiments, and longitudinal studies.

Quality measurement and assessment of software products and processes.

Replication of empirical studies and families of studies to validate findings.

Industry applications of software engineering analytics.

Simulation studies to predict outcomes and improve software engineering practices.

Software project management and knowledge management strategies.

Technology transfer in software engineering, including industry adoption challenges.

Promoting evidence-based and experimental approaches to enhance industrial software systems.

Submissions must provide substantial contributions to the field and demonstrate practical validation through experimental analysis or empirical insights directly applicable to industrial software engineering. We encourage contributions that emphasize empirical evidence and rigorous experimentation.