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Today’s software engineers often work in teams to develop complex software systems. Therefore, successful software engineering in practice require team members to possess not only sound programming skills such as analysis, design, coding and testing but also soft skills such as communication, collaboration and self-management. However, existing examination-based assessments are often inadequate for quantifying students’ soft skill development. The purpose of this paper is to explore alternative ways for assessing software engineering students’ skills through a data-driven approach.

In this paper, the exploratory data analysis approach is adopted. Leveraging the proposed online agile project management tool – Human-centred Agile Software Engineering (HASE), a study was conducted involving 21 Scrum teams consisting of over 100 undergraduate software engineering students in multi-week coursework projects in 2014.

During this study, students performed close to 170,000 software engineering activities logged by HASE. By analysing the collected activity trajectory data set, the authors demonstrate the potential for this new research direction to enable software engineering educators to have a quantifiable way of understanding their students’ skill development, and take a proactive approach in helping them improve their programming and soft skills.

To the best of the authors’ knowledge, there has yet to be published previous studies using software engineering activity data to assess software engineers’ skills.

Saudi universities have incorporated capstone projects in the final year of an undergraduate study. Although universities are following recommendations of the National Commission for National Commission for Academic Accreditation and Assessment (NCAAA) and Accreditation Board for Engineering and Technology (ABET), no detailed guidelines for management and assessment of capstone projects are provided by these accreditation bodies. Variation in the management and assessment practices of capstone project courses and analysis of the students' capabilities to align with industry demands, to realize Vision 2030, is challenging. This study investigates the current practices for structure definition, management and assessment criteria used for capstone project courses at undergraduate level for information technology (IT) programs at Saudi universities.

A web-based questionnaire is administered using a web service commonly used for questionnaires and polls to investigate the structure, management and assessment of capstone projects at the undergraduate level offering software engineering, computer science and information technology (SECSIT) programs. In total, 42 faculty members (with range of experience of managing/advising capstone projects from 1 to more than 10 years) from 22 Saudi universities (out of more than 30 universities offering SECSIT undergraduate programs) participated in the study.

The authors have identified that Saudi universities are facing challenges in the utilized process model, the distribution of work and marks, the knowledge sharing approach and the assessment scheme. To cope with these challenges, the authors recommend the use of an incremental development process, the utilization of a project-driven approach, the development of a national level digital archive and the implementation of homogeneous assessment scheme.

To contribute to the national growth and to fulfill the market demand, universities are recommended to align the capstone project courses with latest technology trends. Universities must collaborate with the industry and update the structure and requirements of capstone project courses accordingly. This will further facilitate to bridge the gap between industry and academia and will develop a win–win scenario for all the stakeholders.

Although universities are committed to increase innovative capacities of their students for enabling them to contribute to economic and social growth, it is still hard to know the knowledge creation and sharing at national level. Variations in the management and assessment practices for capstone projects further intensify this challenge. Hence, there is a need of smart assessment and management of software capstone projects being developed in Saudi universities. Incorporating latest technologies, such unified management can facilitate discovering the trends and patterns related to the domain and complexity.

The purpose of this paper is to investigate and quantify the impact of product complexity, including architectural complexity, on operator learning, productivity and quality performance in both assembly and disassembly operations. This topic has not been extensively investigated in previous research.

An extensive experimental campaign involving 84 operators was conducted to repeatedly assemble and disassemble six different products of varying complexity to construct productivity and quality learning curves. Data from the experiment were analysed using statistical methods.

The human learning factor of productivity increases superlinearly with the increasing architectural complexity of products, i.e. from centralised to distributed architectures, both in assembly and disassembly, regardless of the level of overall product complexity. On the other hand, the human learning factor of quality performance decreases superlinearly as the architectural complexity of products increases. The intrinsic characteristics of product architecture are the reasons for this difference in learning factor.

The results of the study suggest that considering product complexity, particularly architectural complexity, in the design and planning of manufacturing processes can optimise operator learning, productivity and quality performance, and inform decisions about improving manufacturing operations.

While previous research has focussed on the effects of complexity on process time and defect generation, this study is amongst the first to investigate and quantify the effects of product complexity, including architectural complexity, on operator learning using an extensive experimental campaign.

Manufacturers face various challenges and risks during their digital servitization (DS), due to the complexity caused by introducing breakthrough technologies, increasingly complex product-service solutions and new stakeholders in the business network. The process necessitates the implementation of various changes that usually happen over a long period of time. Using complexity management as a theoretical lens, this paper delves into manufacturers’ DS journeys and explores how manufacturers manage the associated complexities.

This paper investigates the DS journey of two manufacturers in a longitudinal case study from 2014 to 2021.

Three main complexity management actions during the DS journey were identified: shaping the digital service system, shaping the organization and shaping the network. Tied to different types of complexities, these actions demonstrate how manufacturers navigate their journey. The findings also reveal different complexity management approaches used at the different stages of this journey.

This paper offers a comprehensive framework for understanding complexity management in the DS journey, including the types of complexities, complexity management actions and complexity management approaches and their rationale. This paper shows that different requirements are created during emerge, consolidate and evolve stages of the DS journey. Manufacturers need a dynamic approach that considers changes in complexities and actions over time.

This paper aims to propose a conceptual model of policy components for software that supports modularizing and tailoring of information security policies (ISPs).

This study used a design science research approach, drawing on design knowledge from the field of situational method engineering. The conceptual model was developed as a unified modeling language class diagram using existing ISPs from public agencies in Sweden.

This study’s demonstration as proof of concept indicates that the conceptual model can be used to create free-standing modules that provide guidance about information security in relation to a specific work task and that these modules can be used across multiple tailored ISPs. Thus, the model can be considered as a step toward developing software to tailor ISPs.

The proposed conceptual model bears several short- and long-term implications for research. In the short term, the model can act as a foundation for developing software to design tailored ISPs. In the long term, having software that enables tailorable ISPs will allow researchers to do new types of studies, such as evaluating the software's effectiveness in the ISP development process.

Practitioners can use the model to develop software that assist information security managers in designing tailored ISPs. Such a tool can offer the opportunity for information security managers to design more purposeful ISPs.

The proposed model offers a detailed and well-elaborated starting point for developing software that supports modularizing and tailoring of ISPs.

Recent advancements in Artificial Intelligence (AI) and, at its core, Machine Learning (ML) offer opportunities for organizations to develop new or enhance existing capabilities. Despite the endless possibilities, organizations face operational challenges in harvesting the value of ML-based capabilities (MLbC), and current research has yet to explicate these challenges and theorize their remedies. To bridge the gap, this study explored the current practices to propose a systematic way of orchestrating MLbC development, which is an extension of ongoing digitalization of organizations.

Data were collected from Finland's Artificial Intelligence Accelerator (FAIA) and complemented by follow-up interviews with experts outside FAIA in Europe, China and the United States over four years. Data were analyzed through open coding, thematic analysis and cross-comparison to develop a comprehensive understanding of the MLbC development process.

The analysis identified the main components of MLbC development, its three phases (development, release and operation) and two major MLbC development challenges: Temporal Complexity and Context Sensitivity. The study then introduced Fostering Temporal Congruence and Cultivating Organizational Meta-learning as strategic practices addressing these challenges.

This study offers a better theoretical explanation for the MLbC development process beyond MLOps (Machine Learning Operations) and its hindrances. It also proposes a practical way to align ML-based applications with business needs while accounting for their structural limitations. Beyond the MLbC context, this study offers a strategic framework that can be adapted for different cases of digital transformation that include automation and augmentation of work.

The purpose of this paper is to review cases of artificial reefs built through additive manufacturing (AM) technologies and analyse their ecological goals, fabrication process, materials, structural design features and implementation location to determine predominant parameters, environmental impacts, advantages, and limitations.

The review analysed 16 cases of artificial reefs from both temperate and tropical regions. These were categorised based on the AM process used, the mortar material used (crucial for biological applications), the structural design features and the location of implementation. These parameters are assessed to determine how effectively the designs meet the stipulated ecological goals, how AM technologies demonstrate their potential in comparison to conventional methods and the preference locations of these implementations.

The overview revealed that the dominant artificial reef implementation occurs in the Mediterranean and Atlantic Seas, both accounting for 24%. The remaining cases were in the Australian Sea (20%), the South Asia Sea (12%), the Persian Gulf and the Pacific Ocean, both with 8%, and the Indian Sea with 4% of all the cases studied. It was concluded that fused filament fabrication, binder jetting and material extrusion represent the main AM processes used to build artificial reefs. Cementitious materials, ceramics, polymers and geopolymer formulations were used, incorporating aggregates from mineral residues, biological wastes and pozzolan materials, to reduce environmental impacts, promote the circular economy and be more beneficial for marine ecosystems. The evaluation ranking assessed how well their design and materials align with their ecological goals, demonstrating that five cases were ranked with high effectiveness, ten projects with moderate effectiveness and one case with low effectiveness.

AM represents an innovative method for marine restoration and management. It offers a rapid prototyping technique for design validation and enables the creation of highly complex shapes for habitat diversification while incorporating a diverse range of materials to benefit environmental and marine species’ habitats.

With the development of machine learning techniques, the artificial intelligence systems such as crowd networks are becoming more autonomous and smart. Therefore, there is a growing demand for developing a universal intelligence measurement so that the intelligence of artificial intelligence systems can be evaluated. This paper aims to propose a more formalized and accurate machine intelligence measurement method.

This paper proposes a quality–time–complexity universal intelligence measurement method to measure the intelligence of agents.

By observing the interaction process between the agent and the environment, we abstract three major factors for intelligence measure as quality, time and complexity of environment.

This paper proposes a calculable universal intelligent measure method through considering more than two factors and the correlations between factors which are involved in an intelligent measurement.

The aim of this paper is to frame the stakeholder-driven system mapping approach in the context of climate change, building on stakeholder knowledge of system boundaries, key elements and interactions within a system and to introduce a decision support tool for managing and visualising this knowledge into insightful system maps with policy implications.

This methodological framework is based on the concepts of market maps. The process of eliciting and visualising expert knowledge is facilitated by means of a reference implementation in MATLAB, which allows for designing technological innovation systems models in either a structured or a visual format.

System mapping can contribute to evaluating systems for climate change by capturing knowledge of expert groups with regard to the dynamic interrelations between climate policy strategies and other system components, which may promote or hinder the desired transition to low carbon societies.

This study explores how system mapping addresses gaps in analytical tools and complements the systems of innovation framework. Knowledge elicitation, however, must be facilitated and build upon a structured framework such as technological innovation systems.

This approach can provide policymakers with significant insight into the strengths and weaknesses of current policy frameworks based on tacit knowledge embedded in stakeholders.

The developed methodological framework aims to include societal groups in the climate policy-making process by acknowledging stakeholders’ role in developing transition pathways. The system map codifies stakeholder input in a structured and transparent manner.

This is the first study that clearly defines the system mapping approach in the frame of climate policy and introduces the first dedicated software option for researchers and decision makers to use for implementing this methodology.

The purpose of this paper is to characterize the interfaces between manufacturing companies and the Internet of Things (IoT) suppliers involved in their digital servitization.

This paper builds on an explorative case study of a manufacturing firm and its IoT suppliers. This paper relies on the Industrial Network Approach to study interfaces between buying firms and their suppliers.

This paper identifies three distinct types of supplier interfaces: connected, digital and digital-physical. They all contain technical resource interfaces with additional organizational and/or technical complexities that need to be managed. Connectivity, an Agile approach to software development and strong technical dependence emerged as key factors that impact the interactions between manufacturing firms and IoT suppliers and how their resources are combined.

This paper offers managerial implications regarding the importance of internal organization (such as appropriate cross-functional teams) to manage the dynamics of collaborations required by digital technologies, maintain interactions with IoT suppliers and identify and manage interdependences between IoT suppliers. Building close relationships with suppliers of crucial infrastructure (e.g. IoT cloud platform and data security systems) can also be beneficial for manufacturing firms to reduce risks. Finally, attention should be given to IoT technology strategy, which impacts both digital and digital-physical supplier interfaces.

In digital servitization, manufacturing firms are heavily reliant on external resources for IoT technology. Despite this, few studies have investigated the characteristics of their interfaces with IoT suppliers, how these can be managed and how resources are combined.