EasyB

This just in from Luis Espinal of MJUG:

http://www.easyb.org/

The EasyB syntax for writing stories and specifications is a lot more succinct than the one provided by Specs, the Scala BDD framework (at least when looked upon from a 10K foot view)
It also got me to think why TDD and BDD is not so common with plain Java. Java's atrocious verbosity makes it very hard to write tests and specs. At least superficially, it looks like a pleasure to use EasyB to write specs for Java and Scala programs.

Regards,

Luis Espinal
===
Scala appeals to developers because it's a functional language and not as verbose as most languages. You can see examples of another functional language, Clojure, in Contest Town. Eric Lavigne wrote an instant runoff election and a Wari program using Clojure.
An interesting discussion is going on in the MJUG mailing list. Thoughts?

Views: 30

Happy 10th year, JCertif!

Notes

Welcome to Codetown!

Codetown is a social network. It's got blogs, forums, groups, personal pages and more! You might think of Codetown as a funky camper van with lots of compartments for your stuff and a great multimedia system, too! Best of all, Codetown has room for all of your friends.

When you create a profile for yourself you get a personal page automatically. That's where you can be creative and do your own thing. People who want to get to know you will click on your name or picture and…
Continue

Created by Michael Levin Dec 18, 2008 at 6:56pm. Last updated by Michael Levin May 4, 2018.

Looking for Jobs or Staff?

Check out the Codetown Jobs group.

 

Enjoy the site? Support Codetown with your donation.



InfoQ Reading List

Presentation: The Time is Now: Delight Your Developers with User-Centric Platforms & Practices

Ana Petkovska discusses creating platform teams, establishing the team API, engagement of early adopters, easing adoption and providing a high quality product.

By Ana Petkovska

DeepSeek Open-Sources DeepSeek-V3, a 671B Parameter Mixture of Experts LLM

DeepSeek open-sourced DeepSeek-V3, a Mixture-of-Experts (MoE) LLM containing 671B parameters. It was pre-trained on 14.8T tokens using 2.788M GPU hours and outperforms other open-source models on a range of LLM benchmarks, including MMLU, MMLU-Pro, and GPQA.

By Anthony Alford

Article: A Framework for Building Micro Metrics for LLM System Evaluation

LLM accuracy is a challenging topic to address and is much more multi-dimensional than a simple accuracy score. Denys Linkov introduces a framework for creating micro metrics to evaluate LLM systems, focusing on goal-aligned metrics that improve performance and reliability. By adopting an iterative "crawl, walk, run" methodology, teams can incrementally develop observability.

By Denys Linkov

Google Releases Experimental AI Reasoning Model

Google has introduced Gemini 2.0 Flash Thinking Experimental, an AI reasoning model available in its AI Studio platform.

By Daniel Dominguez

Google Vertex AI Provides RAG Engine for Large Language Model Grounding

Vertex AI RAG Engine is a managed orchestration service aimed to make it easier to connect large language models (LLMs) to external data sources to be more up-to-date, generate more relevant responses, and hallucinate less.

By Sergio De Simone

© 2025   Created by Michael Levin.   Powered by

Badges  |  Report an Issue  |  Terms of Service