ABSTRACT Context Software architecture plays a crucial role in the success or failure of software development and design. Over time, architectural degradation—known as architectural erosion—can compromise system quality and maintainability. Metrics‐based approaches have become a prevalent method for identifying and addressing this phenomenon. Objective This study aims to (1) investigate whether various metrics‐based approaches can effectively determine architectural erosion and support the development of a formal model, and (2) assess the construct reliability and validity of the proposed model. Methods A formal model was constructed based on selected metrics approaches derived from the literature. A questionnaire‐based survey was conducted with 130 software engineering professionals experienced in architectural erosion and metrics. Structural Equation Modelling (SEM) was employed to evaluate the model's construct reliability, construct validity, and research hypotheses. Results The analysis revealed significant relationships between most classifications of metrics‐based approaches and architectural erosion. However, no substantial relationships were found for the classifications of architectural complexity and architectural technical debt. Conclusion The proposed model offers a valuable foundation for the formal definition and evaluation of metrics‐based approaches to architectural erosion. Its empirical validation provides meaningful insights for both researchers and practitioners aiming to assess or mitigate architectural erosion in software systems.

Abstract The growing number of software startups has witnessed an open debate on the suitability and appropriateness of commonly used software development methodologies, including agile software development methodologies and practices. Startups, for example, tend to focus on producing minimum viable product, which challenge the use of these methods and calls for bespoke adaptation of these practices to suit startups. Agile adoption is not easy for software startup teams due to unreadiness, inadequate preparation and weak structure of these teams, focusing only on small part of agile practices, and high uncertainty in essential requirements and proper technology. A review of the state‐of‐the‐art reports on limited number of studies that have investigated the adoption of agile methods and practices to best suit the requirements software startups. This study uses design science research methodology to address this gap and develop a guideline for agile adaptation specifically for software startups. The developed guideline was validated and improved with the participation of 23 experts from 7 software startup teams through survey questionnaires and open discussion. This guideline includes 13 recommendations, categorized into three sections: selection of agile methods and practices, preparation for adaptation, and the adaptation of agile methods and practices. Evaluation of the results shows the simplicity of understanding the guideline, its usefulness, and its support for the expected agility of the software development process.

The evaluation, importance and variation nature of multiple security and privacy properties are the main issues that make the benchmarking of blockchain-based IoT healthcare Industry 4.0 systems fall under the multi-criteria decision-making (MCDM) problem. In this article, one of the recent MCDM weighting methods called fuzzy weighted with zero inconsistency (FWZIC) is effective for weighting the evaluation criteria subjectively without any inconsistency issues. However, considering the advantages of spherical fuzzy sets in providing a wide range of options to decision-makers and efficiently dealing with vagueness, hesitancy and uncertainty, this article formulated a new version of FWZIC for weighting the security and privacy properties, that is, spherical FWZIC (S-FWZIC). Moreover, an integrated MCDM framework was developed for benchmarking blockchain-based IoT healthcare Industry 4.0 systems on the basis of multi security and privacy properties. In the first phase of the methodology, a decision matrix is formulated based on the intersection of “blockchain-based Internet of Things healthcare Industry 4.0 systems” and “security and privacy properties” (i.e., user authentication, access control, privacy protection, integrity availability and anonymity). In the second phase, the weights of each security and privacy property are calculated through the S-FWZIC method. Then, these weights are employed to benchmark blockchain-based IoT healthcare Industry 4.0 systems through the combined grey relational analysis–technique for order of preference by similarity to ideal solution (GRA-TOPSIS) and the bald eagle search (BES) optimization method. Results indicate the following: First, the S-FWZIC method efficiently weighs the security and privacy properties, indicating that access control has the highest significance weight of 0.2070, while integrity has the lowest weight (0.0646); and second, the combination of the GRA-TOPSIS and the BES optimization method effectively ranks the systems. The evaluation was conducted using sensitivity analysis, revealing high correlation results over all the discussed scenarios of changing the weights of the criteria. The implications of this article can assist medical organisation administrators in selecting the most secure and appropriate system and the developers of such systems in future directions.