Scientific works on integrated circuits usually focus on technical challenges and contribute to improving the solutions. They often neglect intertwined social aspects that highly impact project success, as shown in recent Software Engineering research. This work is a case study to identify challenges in delivery processes between digital and analog disciplines on mixed-signal projects at a specific design house, considering technical and social perspectives. We performed 17 semi-structured interviews and examined the available process documents, analyzing the data using Socio-Technical Grounded Theory. We mapped 14 constructs, 13 propositions, and two explanations to identify those challenges faced by mixed-signal teams. This study contributes to the discussion of social aspects of Semiconductor’s scientific works and presents a list of possible delivery challenges in the context of the focal company. The proposed root causes are the incipient specification processes and the lack of practices related to collaboration in cross-functional teams. Requirement Engineering and DevOps studies provide insights to define implications for improvement in the organization.

Machine Learning (ML) has grown in popularity in the software industry due to its ability to solve complex problems. Developing ML Systems involves more uncertainty and risk because it requires identifying a business opportunity and managing the source code, data, and trained model. Our research aims to identify the existing practices used in the industry for building ML applications. The goal is to comprehend the orga- nizational complexity of adopting ML Systems. We conducted a Multivocal Literature Review (MLR) and used Grounded Theory (GT) to build a taxonomy with the practices applied to the ML System lifecycle from the industry and academic perspectives. We selected 41 posts from grey literature and 37 papers from scientific repositories. Following a systematic GT protocol, we mapped 91 practices, grouped in 6 core categories related to designing, developing, testing, and deploying ML Systems. The results can help organizations identify the gaps in their current ML processes and practices, and provide a roadmap for improving and optimizing their ML systems. The comprehensive taxonomy of practices developed in this research serves as a valuable tool for managers, practitioners, and researchers in the ML field, providing a clear and organized understanding of the complexity of managing ML systems.

The rapidly changing software industry presents challenges in teaching software engineering concepts and skills, especially in the area of DevOps. This encompasses both technical aspects, such as pipeline automation, and non-technical ones, such as culture, principles, and team interaction modes. This presents a significant challenge and there is high demand for skilled DevOps practitioners. To address these challenges, we propose a teaching strategy for DevOps that is based on real-world experiences from companies adopting DevOps and real projects. The strategy incorporates two teaching techniques: Research-based Teaching and Open Source Software projects. Our findings can help educators effectively plan, implement, and evaluate DevOps courses. The results also highlight several opportunities for practitioners to benefit from academic environments and experiment with new ideas to train abilities expected in new hires.

The software industry employs few women and underrepresented minorities. Several initiatives from organizations, practitioners, and OSS communities address equity, diversity, and inclusion in software development. However, most of these initiatives deal in a generalist and simplistic way, often confusing diversity with gender diversity, reinforcing an industry with a homogeneous workforce, which has little room for the inclusion of sub represented communities. One of these communities is the transgender, who suffers from several intersections of oppression concerning gender, race, class, age, and disabilities, thus having voices erased in the industry. Our research aims to identify the concepts of diversity in Software Engineering (SE) from a transgender perspective. We conduct a Systematic Grey Literature Review (GLR)  to map the concepts around diversity in SE, focused on trans perspective. We detail our GLR, and inspired in Grounded Theory (GT) we have a coding protocol to build a concept map. As a result, we obtained a concept map format, which illustrate some of the complexity of diversity issues in SE focused on trans people and how organizations and developers can contribute to trans inclusion in the software industry. The paper starts a discussion regarding the perspective of trans people in the area.  Research in diversity should observe the oppressions that exist and how they interact without simplifying and isolating issues such as treating gender in a binary way and not interconnected with other issues such as race. SE industry is strategic to the trans community. Although communities declare themselves to be diverse and open to entry, concrete actions are lacking.