There’s a quiet shift happening in how I think about software.

At first, it was about compliance — tracking what happens in a system, logging every event for auditing purposes. But something has changed. The deeper I dig into application-level telemetry, structured audit trails, and self-instrumented behavior, the more I realize: we’re crossing into a new territory of software engineering.

This isn’t just about tracking what a user does. It’s about helping a system understand itself — where it’s running, who is using it, what jurisdiction governs the request, and how to behave accordingly.

We’re talking about software that self-observes, adjusts its verbosity based on IP address, restricts access based on regional constraints, and toggles internal diagnostics based on who’s logged in — all without being told explicitly to do so.

Imagine systems that turn self-auditing on or off depending on:

• The user’s role
• The request’s geographic origin
• The environment (prod vs. test)
• Even the user interface they’re interacting with

And imagine those systems storing that information in well-organized data lakes — raw behavioral breadcrumbs ready for forensic analysis, compliance snapshots, or debugging insight.

What started as a technical feature has slowly become a philosophy of system design.

A Nascent Field?

I’ve started to think of this as a new academic discipline. Not cybersecurity. Not DevOps. Not classical software engineering.

This is behavioral software engineering — a branch of computer science that treats software not just as code that runs, but as systems that act, record, and explain.

It overlaps with AI, but it’s not AI.
It overlaps with observability, but it’s not just metrics and traces.

It’s something else. Something that might one day deserve its own curriculum. And maybe, just maybe, its own academic major.

From Debugging to Design

This new lens has even changed how I refactor.

Today, I spent hours moving logging responsibilities out of the stored procedures and into the application layer — where they belong. Why? Because systems should know why they’re logging what they’re logging. It’s not just about writing to a table — it’s about understanding context.

I’ve begun designing repositories that don’t just execute commands but narrate what happened and why. The software is slowly developing a kind of self-awareness — not in the sci-fi sense, but in the audit-log sense.

What Comes Next

I can see this going further. Systems that feed behavioral data into machine learning models. Platforms that detect anomalies in how they’re being used. Infrastructure that adjusts in real time, not just for performance, but for accountability.

I’m not quite ready to unveil the products I’m building in this space — they’re still in the oven. But I’m sharing this post as a marker. A timestamp. A way to say: this is the direction things are headed, and I want to help define it.

If you’re building compliance-focused systems, observability tools, or software that learns from its own behavior, let’s talk.

About the Author

Paul A. Jones, Jr.
B.S., Electrical Engineering & Physics (Clemson ’91, ’93)
M.S., Software Engineering (Mercer ’09)

📬 pauljonessoftware@gmail.com
🐦 @PaulAJonesJr
🌐 PaulJonesSoftware.com