I’m driven by a deep curiosity. I channel this into research that matters to academics and addresses real-world challenges.
Artificial Intelligence for Software Engineering (AI4SE)
The integration of Artificial Intelligence (AI) into software engineering offers transformative potential but also presents complex challenges. My research is grounded in empirical evidence and focuses on understanding and optimizing the adoption and impact of AI across the software development lifecycle. I study:
AI Adoption Dynamics: I explore the factors that influence the adoption of AI tools in software engineering. By examining technical, organizational, and psychological aspects, I aim to identify the key drivers and barriers to AI integration in real-world settings.
Disruptive Innovations: Using a structured research playbook, I investigate how disruptive technologies like Generative AI, AR, and VR impact software engineering practices. This includes assessing both the potential benefits and the challenges these technologies bring to the industry.
Enhancing Creativity: I study how AI can enhance the creative capabilities of software developers. By automating routine tasks, AI tools allow developers to focus on innovation and more complex problem-solving, leading to more sophisticated and user-centric software products.
Ethical AI Integration: My research emphasizes the importance of ethical considerations in AI adoption. The Copenhagen Manifesto outlines principles such as responsibility, transparency, inclusivity, and sustainability, guiding the ethical integration of AI into software engineering practices.
Agile Transformation
Agile methods are popular in software development. But, they have challenges. My research is based on facts, not just opinions. I study:
Agile Dynamics: I explore Agile processes. I look at technical, organizational, and psychological aspects. I aim to understand these elements in real settings.
Tailored Agile: A single approach doesn’t always work. I guide businesses to adjust Agile methods to fit them. This approach aims to get the most from Agile and avoid problems.
Team Work: I study what makes Agile teams work well. I look at team dynamics and communication. My research gives insights to build effective Agile teams.
Future of Work & Digital Resilience
The COVID-19 pandemic changed how we work. It highlighted the need for digital strength. I study:
Work Dynamics: I look at the “Future of Work”. I focus on remote and hybrid work models. These models bring challenges and opportunities, especially for software engineers. I aim to give insights to improve work in this new era.
Well-being & Productivity: The pandemic affected software engineers’ well-being. I study its impact on their mood, work output, and how they adjust to new work settings. My goal is to find best practices to support a balanced work-life.
Business Resilience: Many businesses sped up their digital changes. I study their strategies and outcomes. I aim to give insights to help businesses be digitally strong against disruptions.
Diversity & Inclusion
Understanding the dynamics of diversity and inclusion is critical for a socially sustainable future. My research focuses on:
Diversity and Inclusion: In software engineering, diverse teams can drive innovation but also present unique challenges. I examine the impacts of gender, age, role, and cultural diversity on team dynamics, conflict, and effectiveness. My goal is to provide actionable insights to foster inclusive, high-performing software development environments.
Personality Traits and Team Dynamics: Understanding how personality traits differ between genders and their effects on teamwork is essential. My research uses advanced models to explore these differences and their implications for team cohesion, productivity, and conflict resolution. I aim to guide the creation of balanced and effective teams.
Psychological Safety and Team Performance: Psychological safety is a cornerstone of effective teamwork. I study its role in mitigating conflicts and enhancing team performance in diverse teams. My goal is to highlight strategies that create safe and supportive work environments for software engineers.
Advanced Data Analysis
Navigating the complexities of software engineering research requires innovative and robust analytical methods. My dedication to evidence-based insights and empirical standards has led to explore and advocate for the following:
Soft Theory Advocacy: In scenarios where controlled experiments aren’t feasible, we advocate for the use of soft theory, particularly soft modeling techniques. These approaches, grounded in scientific research, offer pragmatic solutions without compromising on data integrity or research quality.
SEM: SEM is a tool to study complex relationships. My work in ACM Computing Surveys talks about the use of PLS-SEM in software engineering.
Publication Standards: Peer-review has challenges. I support the idea of Empirical Standards in Software Engineering. This gives clear guidelines for authors and reviewers.
Soft Theory: When we can’t do controlled experiments, we use soft theory. These methods are based on research. They give practical solutions without losing data quality.
There is no end. There is no beginning. There is only the infinite passion of life.
– Federico Fellini