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"Software Architecture in Transition" by Uwe Friedrichsen

Software Architecture in Transition

An article by Uwe Friedrichsen

Gener­ative AI (GenAI) is currently a hot topic. Massive changes are being predicted across all indus­tries – including IT. AI agents based on GenAI technologies are expected to enter the realm of software devel­opment. Some even claim that, in the future, software devel­opment will be entirely handled by collab­o­rating AI agents. In this article, we explore what AI could mean for the role of software architects.

Fact or fiction?

To assess AI’s impact on the role of software archi­tects, we first need to take a closer look at AI itself. And that brings us to the first challenge: it’s nearly impos­sible to get reliable information about the capabil­ities of current and future AI solutions.

For some, AI is the answer to all questions in the universe – the ultimate remedy for all modern (workplace) problems. For others, it’s just smoke and mirrors: overhyped, overpromising, and under­de­liv­ering. So what’s fact, and what’s fiction? The two are often hard to distin­guish. If we truly wanted to under­stand AI, we would have to try every­thing ourselves. But testing all the contra­dictory claims is simply unfea­sible. In short: it’s complicated.

Blurred bound­aries

We also need to take a closer look at software architecture – and this, too, comes with its own set of challenges. The bound­aries of software architecture are not clearly defined. Accord­ingly, there’s much debate about what exactly belongs to it and what doesn’t, which tasks archi­tects are respon­sible for and which not. This, too, is complicated.

For simplicity’s sake – and to have a concrete foundation – I’ll focus on the following four core activ­ities of architectural work:

  • Analysis – Gaining a holistic under­standing of the problem
  • Design – Creating potential solution options
  • Evalu­ation – Assessing the pros and cons of those options
  • Collab­o­ration – Working with others, e.g., to gather information or commu­nicate outcomes

Possi­bil­ities and limitations

When we look at these four core activ­ities, we get a nuanced picture of how AI might influence the field of software architecture (see figure).

software_architektur_im_wandel_v2_en

In analysis, AI already provides valuable support: analyzing code, documen­tation, and requirements; gaining an overview; identi­fying gaps; and highlighting oppor­tu­nities for improvement. This is already happening today and will only become more widespread.

Design is less straight­forward. For common standard solutions – like e‑commerce platforms – AI can already generate solid architectural proposals. (Though I still wonder why companies continue to build their own e‑commerce solutions rather than using off-the-shelf software or SaaS offerings.)
However, the more specialized and original the solution, the less helpful AI becomes – simply because it lacks relevant examples in its training data. In such cases, GenAI tools tend to inter­polate, or in other words, “hallu­cinate.” This limitation is unlikely to change signif­i­cantly in the near future.

AI can already assist us to a certain extent in evalu­ation, partic­u­larly by pointing out aspects that might have been overlooked. AI support in this area is likely to grow stronger.

When it comes to collab­o­ration, the potential for support is limited. Documen­tation becomes important whenever direct inter­action between people is not possible. AI already offers useful support here – for example, by transcribing and summa­rizing meetings or workshops. Beyond that, however, the potential thins out. The actual core of architectural work – finding and evalu­ating solutions, and collab­o­rating with others – will likely remain a task for humans. Machines, or more precisely AI, can’t truly support us in that regard.

There are many other aspects and details worth exploring. But given the length of this article, I’ll leave it there.

Every­thing stays different

So what does this mean for software archi­tects? Their work will undoubtedly change: AI will become part of their daily routine. New tools will complement the existing toolbox. Whether AI will fully replace current tools remains to be seen. The technology is still too young to say for sure.

And what about the role of the software architect? Will it eventually be replaced by AI? I don’t think so – at least not in the foreseeable future. The role involves a wide range of tasks that AI can’t yet perform meaning­fully or suffi­ciently – simply because the required training data is lacking. And let’s not forget: software devel­opment is still a very human endeavor. As long as humans are part of the devel­opment process – especially on the requirements side – we will also need human software architects.

What does this mean for training programs such as those offered by iSAQB? New oppor­tu­nities and tools must continue to be integrated into curricula and training programs to stay up to date and relevant. While modules like SWARC4AI Software Architecture for AI Systems have already been added, other training courses will undergo funda­mental changes as AI handles more tasks. Overall, the need to train software archi­tects will remain.

So every­thing changes, yet every­thing stays the same – as is so often the case when progress comes knocking.


About the author:
Uwe Friedrichsen is CTO of codecentric AG and a member of iSAQB. He is a regular speaker at inter­na­tional confer­ences, a specialist author, and editor of two IT publications.

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