AI debugging future: what will Dashcam look like in 2 years?

Our global advancements in Artificial Intelligence cannot be ignored. Dashcam is absolutely going to use A.I. extensively in the future, like many other companies who understand the importance of A.I.

In this speculative article, I will try to analyze what a future version Dashcam could look and feel like.

Without further ado…

⏳ Hypothetical features from available on a future Dashcam

Explain errors in plain language

Dashcam could analyze stack traces, error messages, and code context to explain bugs in more understandable terms compared to raw error output. This makes debugging easier for developers.

Suggest fixes for bugs

Based on its analysis, Dashcam could suggest potential fixes or steps to take to resolve an error. This gives developers a headstart on debugging.

Know your code, connecting bugs together

Dashcam could becomes a central repository for your code. By referencing its knowledge, Dashcam could point developers to similar bugs or issues others have faced, along with their solutions. This allows developers to leverage solutions that worked in the past.

Real-time code quality issues

Dashcam could flag potential code quality issues like security vulnerabilities, anti-patterns, or optimizations before they’re committed or shared. This proactive monitoring could help developers write better quality code.

Automated code reviews

Dashcam could scan code changes/patches for potential bugs or improvements. Filing Jira or Github tickets, with fixes. This frees up developer time spent on manual code reviews.

Prioritize bugs

Dashcam could be trained to understand and prioritize bug severity and impact to help developers focus on the most critical issues first.

Video summarization

Dashcam could automatically generate summaries and high-level overviews of screen recordings, helping developers save time on writing issues.

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