Psychological safety in software workplaces: A systematic literature review
- URL: http://arxiv.org/abs/2508.03369v1
- Date: Tue, 05 Aug 2025 12:19:17 GMT
- Title: Psychological safety in software workplaces: A systematic literature review
- Authors: Beatriz Santana, Lidivânio Monte, Bianca Santana de Araújo Silva, Glauco Carneiro, Sávio Freire, José Amancio Macedo Santos, Manoel Mendonça,
- Abstract summary: Psychological safety (PS) is an important factor influencing team well-being and performance.<n>Despite its acknowledged significance, research on PS within the field of software engineering remains limited.
- Score: 0.8331356470329141
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Context: Psychological safety (PS) is an important factor influencing team well-being and performance, particularly in collaborative and dynamic domains such as software development. Despite its acknowledged significance, research on PS within the field of software engineering remains limited. The socio-technical complexities and fast-paced nature of software development present challenges to cultivating PS. To the best of our knowledge, no systematic secondary study has synthesized existing knowledge on PS in the context of software engineering. Objective: This study aims to systematically review and synthesize the existing body of knowledge on PS in software engineering. Specifically, it seeks to identify the potential antecedents and consequences associated with the presence or absence of PS among individuals involved in the software development process. Methods: A systematic literature review was conducted, encompassing studies retrieved from four digital libraries. The extracted data were subjected to both quantitative and qualitative analyses. Results: The findings indicate a growing academic interest in PS within software engineering, with the majority of studies grounded in Edmondson's framework. Factors antecedents of PS were identified at the individual, team, and organizational levels, including team autonomy, agile methodologies, and leadership behaviors. Conclusion: PS fosters innovation, learning, and team performance within software development. However, significant gaps persist in understanding the contextual factors influencing PS, its underlying mechanisms, and effective strategies for its enhancement. Future research should address these gaps by investigating the practical applications of PS within diverse organizational settings in the software engineering domain.
Related papers
- Federated Learning for Cyber Physical Systems: A Comprehensive Survey [49.54239703000928]
Federated learning (FL) has become increasingly popular in recent years.<n>The article scrutinizes how FL is utilized in critical CPS applications, e.g., intelligent transportation systems, cybersecurity services, smart cities, and smart healthcare solutions.
arXiv Detail & Related papers (2025-05-08T01:17:15Z) - I Felt Pressured to Give 100% All the Time: How Are Neurodivergent Professionals Being Included in Software Development Teams? [0.46873264197900916]
This study seeks to understand the work experiences of neurodivergent professionals acting in different software development roles.<n>We applied the Sociotechnical Theory (STS) to investigate how the social structures of organizations and their respective work technologies influence the inclusion of these professionals.
arXiv Detail & Related papers (2025-03-12T02:28:59Z) - Twenty Years of Personality Computing: Threats, Challenges and Future Directions [76.46813522861632]
Personality Computing is a field at the intersection of Personality Psychology and Computer Science.<n>This paper provides an overview of the field, explores key methodologies, discusses the challenges and threats, and outlines potential future directions for responsible development and deployment of Personality Computing technologies.
arXiv Detail & Related papers (2025-03-03T22:03:48Z) - Lingma SWE-GPT: An Open Development-Process-Centric Language Model for Automated Software Improvement [62.94719119451089]
Lingma SWE-GPT series learns from and simulating real-world code submission activities.
Lingma SWE-GPT 72B resolves 30.20% of GitHub issues, marking a significant improvement in automatic issue resolution.
arXiv Detail & Related papers (2024-11-01T14:27:16Z) - A Systematic Literature Review on the Use of Machine Learning in Software Engineering [0.0]
The study was carried out following the objective and the research questions to explore the current state of the art in applying machine learning techniques in software engineering processes.
The review identifies the key areas within software engineering where ML has been applied, including software quality assurance, software maintenance, software comprehension, and software documentation.
arXiv Detail & Related papers (2024-06-19T23:04:27Z) - Making Software Development More Diverse and Inclusive: Key Themes, Challenges, and Future Directions [50.545824691484796]
We identify six themes around the theme challenges and opportunities to improve Software Developer Diversity and Inclusion (SDDI)<n>We identify benefits, harms, and future research directions for the four main themes.<n>We discuss the remaining two themes, Artificial Intelligence & SDDI and AI & Computer Science education, which have a cross-cutting effect on the other themes.
arXiv Detail & Related papers (2024-04-10T16:18:11Z) - Charting a Path to Efficient Onboarding: The Role of Software
Visualization [49.1574468325115]
The present study aims to explore the familiarity of managers, leaders, and developers with software visualization tools.
This approach incorporated quantitative and qualitative analyses of data collected from practitioners using questionnaires and semi-structured interviews.
arXiv Detail & Related papers (2024-01-17T21:30:45Z) - Software development in startup companies: A systematic mapping study [4.881718571745022]
This study aims to structure and analyze the literature on software development in startup companies.
A total of 43 primary studies were identified and mapped, synthesizing the available evidence on software development in startups.
From the reviewed primary studies, 213 software engineering work practices were extracted, categorized and analyzed.
arXiv Detail & Related papers (2023-07-24T19:49:57Z) - Understanding Self-Efficacy in the Context of Software Engineering: A
Qualitative Study in the Industry [2.268415020650315]
Self-efficacy is a concept researched in various areas of knowledge that impacts various factors such as performance, satisfaction, and motivation.
This study aims to understand the impact on the software development context with a focus on understanding the behavioral signs of self-efficacy.
arXiv Detail & Related papers (2023-05-26T17:16:37Z) - Technology Readiness Levels for Machine Learning Systems [107.56979560568232]
Development and deployment of machine learning systems can be executed easily with modern tools, but the process is typically rushed and means-to-an-end.
We have developed a proven systems engineering approach for machine learning development and deployment.
Our "Machine Learning Technology Readiness Levels" framework defines a principled process to ensure robust, reliable, and responsible systems.
arXiv Detail & Related papers (2021-01-11T15:54:48Z) - Software engineering for artificial intelligence and machine learning
software: A systematic literature review [6.681725960709127]
This study aims to investigate how software engineering has been applied in the development of AI/ML systems.
Main challenges faced by professionals are in areas of testing, AI software quality, and data management.
arXiv Detail & Related papers (2020-11-07T11:06:28Z) - Machine Learning for Software Engineering: A Systematic Mapping [73.30245214374027]
The software development industry is rapidly adopting machine learning for transitioning modern day software systems towards highly intelligent and self-learning systems.
No comprehensive study exists that explores the current state-of-the-art on the adoption of machine learning across software engineering life cycle stages.
This study introduces a machine learning for software engineering (MLSE) taxonomy classifying the state-of-the-art machine learning techniques according to their applicability to various software engineering life cycle stages.
arXiv Detail & Related papers (2020-05-27T11:56:56Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.