Enterprise AI Analysis
The Future of Software Engineering is in your hands, and I believe in you!
An in-depth analysis of the paper's core concepts and implications for enterprise AI strategy.
Wesley K. G. Assunção, Department of Computer Science, North Carolina State University, Raleigh, NC, USA
Executive Impact: Navigating the Future of Software Engineering with AI
This analysis highlights the critical role of talent development and technological adoption in advancing software engineering, offering strategic insights for organizations aiming to leverage AI for innovation and efficiency.
Deep Analysis & Enterprise Applications
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Abstract
Software engineering has been a crucial discipline to support the advancement of our modern world, since software systems power all segments of society. This is possible due to the development of the discipline in the last decades, to a state in which we have qualified people (software engineers, practitioners, and researchers), proper processes, and cutting-edge technologies. To achieve this current state, the discipline of software engineering has been advanced by private companies, open-source communities, government agencies, and universities. What do all those different institutions have in common? They have talented, smart, and creative people. The doctoral degree is the highest academic degree, focusing on educating and preparing talented, smart, and creative people. While the discipline of software engineering has seen great development, the new demands from society and the emergence of new technologies (e.g., generative artificial intelligence, quantum computing, Internet of Things) create new challenges and uncover old ones. In this context, the current generation of doctoral students will be in charge of taming such new technologies to meet the demands for the benefit of humanity. Given this crucial role of doctoral students in the future of software engineering, we need to discuss what the expectations, responsibilities, benefits, and barriers are faced by a doctoral student. In this talk, I share my experiences as a software developer, doctoral student, post-doc researcher, and professor directly working with software engineering in the past 20 years. This talk aims to inspire and guide students as they navigate the complexities and milestones of their doctoral studies. The topics of talk involve collaboration with your pairs, publication of your work results, understanding your community (e.g., researchers and practitioners working on the domain of variability and systematic reuse), what students should do and what they should not do, and some provocative discussion to motivate critical thinking by the audience. After the talk, the students will be equipped with practical suggestions and advice so that they can avoid pitfalls, recognize good opportunities to grow, and plan their future careers.
Keywords
PhD Studies, Research, Career in Software Engineering
ACM Reference Format
Wesley K. G. Assunção. 2025. The Future of Software Engineering is in your hands, and I believe in you!. In 29th ACM International Systems and Software Product Line Conference - Volume A (SPLC-A '25), September 01-05, 2025, A Coruña, Spain. ACM, New York, NY, USA, 1 page. https://doi.org/10.1145/3744915.3749151
Acknowledgement
I would like to thank all the supervisors and collaborators I have had, who have helped me to progress in my career as a Software Engineering professor and researcher. Also, I am immensely grateful for all the smart, creative, and hard-working students I have had the opportunity to work with over the years.
Calculate Your Potential AI ROI
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Your AI Implementation Roadmap
A typical timeline for integrating and optimizing AI solutions within enterprise software engineering environments.
Phase 1: Discovery & Strategy (2-4 Weeks)
Initial assessment of current software development lifecycle, identification of AI integration points, goal setting, and strategic planning.
Phase 2: Pilot & Development (6-12 Weeks)
Proof-of-concept AI solution development, integration with existing tools, initial testing, and feedback loop for refinement.
Phase 3: Scaled Deployment & Training (4-8 Weeks)
Full-scale deployment of AI tools, comprehensive team training, and establishment of monitoring and evaluation frameworks.
Phase 4: Optimization & Expansion (Ongoing)
Continuous performance monitoring, iterative improvements, and exploration of new AI applications and features.
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