"AI is turning out to be a powerful tool for developing software. In a prior article, we articulated some ways that AI can be useful for teams in creating their software architecture. Inevitably, teams will be tempted to go beyond using AI as an assistant that helps them brainstorm alternatives to using it to generate code that implements a Minimum Viable Architecture (MVA). When this happens, the work of architecting may change substantially."
"AI's code generation capabilities can seem almost magical, at times, but along with these capabilities comes a conundrum: you really can't see or understand why the AI generated the code that it did; it's just the way the model works. Teams can use AI to generate code for their MVP that, implicitly, makes decisions about their MVA, but there are several architectural issues that teams need"
AI-generated code behaves as a largely opaque black box that embeds implicit architectural decisions no one has time to fully understand. Evaluating such behavior requires empirical experimentation rather than solely design reasoning. Software architects must identify which quality attribute requirements (QARs) most influence system structure and focus on validating those QARs through architectural testing. The architectural process shifts from prescribing internal structure to specifying trade-offs and validating outcomes. Teams must be explicit about trade-offs and the rationale so AI can produce implementations that satisfy constraints. Empirical validation becomes the primary mechanism for ensuring architectural qualities.
Read at InfoQ
Unable to calculate read time
Collection
[
|
...
]