Effective Automated Testing with Spring A Keyhole developer published a course on Effective Automated Testing with Spring via Pluralsight in January 2018. In the creation of the course modules, he found many important topics to talk about that he didn’t have time to cover in the course. His goal is to share what he learned about automated testing with other …
Technical Debt – Observe, React, Prevent
The term “technical debt” was coined over a decade ago to help highlight a common problem in software development. Generally you can think of technical debt like this:
The cost of additional rework created by choosing an easy, shortcut solution now instead of the better approach that would take longer.
Delivering software to meet deadlines or functional goals that are wrought with technical debt is like buying items before you can afford them. Both practices can drastically affect your ability to reach your future goals, both long term and short term.
This article is a gentle reminder to recognize, fix, and avoid technical debt in your software projects.
Four Common Mistakes That Make Automated Testing More Difficult
Attention: This article was published over 9 years ago, and the information provided may be aged or outdated. While some topics are evergreen, technology moves fast, so please keep that in mind as you read the post.This article is part of my blog series on automated testing promoting my new Pluralsight course Effective Automated Testing with Spring. Automated testing is …
Without Automated Testing You Are Building Legacy
I have worked with several different organizations in my career on initiatives to rewrite legacy applications. A common theme for each project was that the organization struggled to deliver both maintainable and “agile” applications.
As developers, we’re curious by nature. I needed to understand exactly why this happens. In my contemplation of this common challenge, I discovered Automated Testing and became fascinated by it. I have since worked to include it as a central step as I write and maintain applications.
In this article particularly, I lay out how automated testing, or rather the lack there of, lies at the heart of many of the struggles we face as developers…
Core ML After Dark
So you’ve made this great social media app, and you are about to sit back and wait for the money to roll in. But, there is a problem: people keep trying to upload nude photos to it.
What if we could have a trained machine learning model that could detect not safe for work (NSFW) content and do it on a iOS device, before any image would be uploaded to a server?
Developing this trained machine learning model is way out of scope for this blog post. Luckily, the good people at Yahoo have already done this with their open-sourced trained Caffe models. The question now is, how can we use this on an iOS device?
In this post: The sultry side of your iPhone can collide with acceptable use policies. We introduce a machine learning solution that can help your application decide what is truly too hot for the internet using Core ML on iOS…