πŸ”¨ Debugging: from print statements to A.I. assistance

We’re building Dashcam to become a full stack debugging tool: to debug anything, anywhere and to be used by anyone who wants to do debugging.

What is full-stack debugging?

Full stack debugging is a process that involves identifying, isolating, and resolving issues across multiple layers of an application.

It’ s called *full stack* because it touches the entire stack, from the user interface down to the underlying infrastructure, and includes both the front-end and back-end of an application.

When you work as (or with) a *full stack* developer you realize how complicated modern applications are, super-connected and bugs can hide anywhere.

That’s why full stack debugging is so important: the more complicated the application, the harder it gets to maintain a reliable and stable platform.

In today’s world debugging is seen as a chore and a time-consuming activity but luckily Dashcam is here to help developers build better, bug-free software – without the headaches – by giving them the tools they need to work efficiently.

What developers did before full stack debugging?

Honestly, a lot of manual processes and often very time-consuming work!

If you worked 20 years ago as a developer, you would spend hours sifting through lines of code to identify and isolate issues. This process, is today known as “print debugging“.

The way this worked was pretty straightforward: by inserting print statements into the code to display variable values and control flow, you could locate errors in the logic – this would take forever to do (I’ve done it) and sometimes you would still not understand why a piece of data.

Luckily things have changed, thankfully, with “time-travel debugging”

What’s time-travel debugging?

While adding print statements or logging variables to a console seems simple enough that anyone can do it, time-travel debugging is a less well-known, more accurate debugging process!

The way time-travel debugging works, is that it provides a detailed view of a program’s execution, it eliminates the guesswork and reduces the time spent on reproducing bugs.

That’s the category under which Dashcam sits, a full stack time-travel debugger with a lot of cool features!

What’s coming next?

Artificial Intelligence is pushing through every fissure in the tech world. Just like that, AI is nesting itself into the debugging world.

A.I. can help developers analyze vast amounts of data: it’s great at recognize patterns, and make predictions with very good accuracy.
Specifically in the context of debugging, AI can be trained to identify common coding errors, anomalies, or performance bottlenecks by analyzing historical data and learning from past bugs and resolutions. This is a very interesting space that we can look at, especially as LLM like Codex are making their way on to developers’ hands.
In the future it will be interesting to see how by analyzing code changes, A.I. will be able to predict if a certain piece of software is buggy and alert the developers about it.

A.I. and Dashcam?

Today, we are building Dashcam with the conscious realization that there is a lot more space for A.I. in our product: making developers proactively address bugs captured in a dash is the first step, but tutoring and optimizing code could potentially become another interesting feature that Dashcam could offer to its users (if they decide to opt-in!).

As these technologies continue to evolve, and we find the best way to integrate them into the current Dashcam platform, we can anticipate a future where debugging becomes a largely automated process, allowing developers to focus more on innovation and less on troubleshooting.

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