Effective software development relies on managing both collaboration and technology, but socio-technical challenges can harm team dynamics and increase technical debt. Although teams working on ML-enabled systems are interdisciplinary, research has largely focused on technical issues, leaving their socio-technical dynamics underexplored. This study aims to address this gap by examining the prevalence, evolution, and interrelations of “community smells”, in open-source ML projects. We conducted an empirical study on 188 repositories from the NICHE dataset using the CADOCS tool to identify and analyze community smells. Our analysis focused on their prevalence, interrelations, and temporal variations. We found that certain smells—such as Prima Donna Effects and Sharing Villainy—are more prevalent and fluctuate over time compared to others like Radio Silence or Organizational Skirmish. These insights might provide valuable support for ML project managers in addressing socio-technical issues and improving team coordination.

Successful software development hinges on effective communication and collaboration, which are significantly influenced by human and social dynamics. Poor management of these elements can lead to the emergence of ‘community smells’, i.e., negative patterns in socio-technical interactions that gradually accumulate as ‘social debt’. This issue is particularly pertinent in machine learning-enabled systems, where diverse actors such as data engineers and software engineers interact at various levels. The unique collaboration context of these systems presents an ideal setting to investigate community smells and their impact on development communities. This article addresses a gap in the literature by identifying the types, causes, effects, and potential mitigation strategies of community smells in machine learning-enabled systems. Using Partial Least Squares Structural Equation Modeling (PLS-SEM), we developed hypotheses based on existing literature and interviews, and conducted a questionnaire-based study to collect data. Our analysis resulted in the construction and validation of five models that represent the causes, effects, and strategies for five specific community smells. These models can help practitioners identify and address community smells within their organizations, while also providing valuable insights for future research on the socio-technical aspects of machine learning-enabled system communities.

Nowadays cloud systems are becoming increasingly important, and at the same time, there is a need to ensure the security of those systems that pose specific problems. There is the lack of adequate skills and knowledge to deal with the complexity and challenges determined by security in cloud systems, particularly skills related to project management. This research aims to investigate the software project management issues and challenges in the design, development, maintenance, and operation of cloud systems while ensuring adequate security, understanding best practices to address them. The ultimate goal of the research is to create a framework that offers practical recommendations to enhance overall project understanding and support project managers in managing projects and programs related to the security of cloud systems.

Cloud computing has become integral to modern organizational operations, offering efficiency and agility. However, security challenges such as data loss and downtime necessitate tailored compliance solutions. Risk assessment is crucial for identifying and mitigating cloud-related threats, yet a standardized approach remains elusive. Our study aims to fill this gap by conducting a systematic mapping study on the prevailing methodologies. Through a meticulous analysis of 21 scholarly papers, we explore various aspects of security risk assessment for the cloud. The results provide valuable insights into delivery models, standards, and validation practices, contributing to a comprehensive understanding of cloud risk assessment.

To provide a measurable representation of social issues in software teams, the research community defined a set of anti-patterns that may lead to the emergence of both social and technical debt, i.e., “community smells”. Researchers have investigated community smells from different perspectives; in particular, they have analyzed how product-related aspects of software development, such as architecture and introducing a new language, could influence community smells. However, how technical project characteristics may be in relation to the emergence of community smells is still unknown. Different from those works, we aim to investigate how adopting specific programming languages might influence the socio-technical alignment and congruence of the development community, possibly inducing their overall ability to communicate and collaborate, leading to the emergence of social anti-patterns, i.e., community smells. We studied the relationship between the most used programming languages and the community smells in 100 open-source projects on G ITHub. Key results of the study show a low statistical correlation for specific community smells like Prima Donna Effects, Solution Defiance, and Organizational Skirmish, highlighting the fact that for some programming languages, its adoption could not be an indicator of the presence or absence of community smells.