Software Engineering in the Year 2034

Most developers do not become more productive with AI tools, but create legacy code more quickly. Current data already shows that code quality on GitHub is falling measurably, fluctuation is increasing and copy-paste code is on the rise. While the hype around generative AI and natural programming is increasing, the truly future-proof skills remain the same: precision, good software testing and the ability to ask the right questions. The future does not belong to those who use tools blindly, but to those who understand what they actually want to achieve.

Podcast Episode: Software Engineering in the Year 2034

What will a developer’s day-to-day work look like in 2034, what environments, tools and practices will be used to create, test, deploy and operate software? What types of software systems will be ubiquitous? What systems will we use at work and what architectures and technologies will these systems be based on? Kevlin takes a look into the future and talks about possible developments in software development, the impact of digitalization, the role of AI, the influence of trends such as cryptocurrency, Web3 and the metaverse and new trends in software architecture, programming languages and work culture.

“Many developers will leave out the part of their job that they enjoy.” - Kevlin Henney

Kevlin Henney is an independent consultant, speaker, writer and trainer who has contributed to the current development of programming techniques, software architecture and unit testing. He has been a columnist for numerous magazines and websites and has served on many committees. He is also co-author of “A Pattern Language for Distributed Computing” and “On Patterns and Pattern Languages” from the “Pattern-Oriented Software Architecture” series, as well as editor of “97 Things Every Programmer Should Know” and co-editor of “97 Things Every Java Programmer Should Know”.

Highlights der Episode

• AI tools accelerate legacy code production: Developers generate low-maintenance code faster without understanding it.
• Programming language development is slow: Top 5 languages are from the 20th century, not the 2020s.
• Testing becomes a core competence: Those who cannot test AI output become pure maintenance programmers.
• Precision beats natural language: Code quality comes from exact notation, not from more flexible description.
• Software development breaks into two camps: quality-oriented teams versus AI-driven assembly line production with increasing error rates.

AI, agility, programming languages - developments in the next decade

Today I talk to Kevlin Henney about possible developments that lie ahead for software development by 2034. Kevlin’s crystal ball gazing reveals a subtle mix of expected developments and surprising constants.

Programming languages

Kevlin highlights a fascinating trend: despite rapid technological advances, most developers are still using programming languages that were developed in the 20th century. This suggests a slower-than-expected evolutionary pace and underscores the notion that mastery of current mainstream languages could remain relevant for a long time to come.

The agile misunderstanding and reality

Agile development has become a buzzword synonymous with modern software practices. However, Kevlin suggests that its true adoption is less widespread than one might assume. Looking ahead to 2034, it seems that certain practices may become more common, but the core principles of agile methodology may still struggle for true implementation.

Hype vs. practicability: technologies at a glance

From metaverse dreams to cryptocurrency bubbles and the ongoing obscurity of Web3, Kevlin critically scrutinizes various technology trends. While acknowledging their respective niches, he projects a tempered outlook on their influence through 2034 and suggests that some hyped innovations may not be able to deliver on their promised revolutionary roles.

AI: The double-edged sword

The discussion inevitably steers to the topic of artificial intelligence - a subject that evokes both curiosity and skepticism. Kevlin offers a nuanced view of the possible evolution of AI: a future in which AI transforms software development but does not replace human ingenuity because the constant need for human accuracy and ethical oversight remains.

The future developer’s toolkit

When looking to the future of software development, developers are given timeless advice: Prioritize foundational skills and principles over fleeting trends. Kevlin recommends focusing on testing skills, precise communication through code and natural language alike, and keeping an eye on emerging tools without losing sight of proven methodologies.

Cultivating collaboration and quality in software teams

Finally, we discuss another crucial aspect that is shaping the future of technology workplaces: Collaboration. With different approaches in large and small organizations, Kevlin highlights the critical need to reduce communication gaps within teams to promote true agility and quality - principles that are likely to remain crucial as we head towards 2034.