The article discusses best practices and examples for writing efficient unit tests in Java, emphasizing their importance in maintaining a healthy codebase: Writing efficient unit tests in Java: best practices & examples

The article outlines essential components for an effective гser acceptance testing as the final testing phase before software deployment as well as a structured checklist for successful UAT process: Complete Checklist for UAT Best Practices

The DORA 2024 Report is Out! And as always, there's A LOT of interesting stuff. This time, the big focus is on AI use in engineering teams. Here’s a quick rundown so you can decide if it’s worth reading the full report.

Continuous Improvement is the New "Elite"

The 2024 report brought a shift in focus: it's not just about reaching "elite" levels on metrics anymore—it’s about aiming for continuous improvement. They kept the classifications (elite, high, medium, low), but the mindset has changed. Now, it’s more about ongoing progress than fitting into labels. Pretty cool, I think.

AI Gaining Ground, But It’s Not All Sunshine

In 2023, AI adoption was already a topic, but the DORA 2024 report went deeper into the impact. Sure, individual productivity is up, and code quality has improved, but... overall delivery performance didn’t quite keep up with the hype. In some cases, AI even hurt delivery stability and frequency. (This caught my attention—I’ll definitely be doing a separate post to dive deeper into this.)

Platform Engineering on the Rise

Platform engineering usage took a leap forward to help boost team productivity, but it also brought some extra challenges for stability and delivery frequency. This wasn’t something emphasized as much in past reports, so it’s worth noting for anyone investing in this practice.

Burnout and Prioritizing Stability

In 2024, stable project priorities proved more critical than ever. The report highlights that constant changes in objectives can increase burnout and negatively affect productivity. Keeping a more stable focus is essential for team well-being.

There are more interesting insights in the full article: https://kodus.io/en/dora-accelerate-state-of-devops/

The article explores integrating Qodo Merge with AWS Bedrock to streamline generative AI coding workflows, improve collaboration, and ensure higher code quality as well as highlights specific features to facilitate these improvements to fill the gaps in traditional code review practices: Efficient Code Review with Qodo Merge and AWS: Filling Out the Missing Pieces of the Puzzle

I have always head about the tool `sed` but I never really got into it because it does not have a very beginner user interface in my opinion. Recently however, I saw a [video by Charles Cabergs](https://www.youtube.com/watch?v=akN2TFarz0A) which showed off exactly what `sed` could do and I got super interested as it seems like an invaluable tool when it comes to re-factoring code or otherwise editing large streams of data.

`sed` is a turing complete stream editor, which can be used to re-factor and re-arrange code in a number of ways which I find helpful on a daily basis. It is powerful enough to write [terminal tetris in](https://github.com/uuner/sedtris). I would recommend watching the video to see exactly how it can be used.

I implemented a, in my opinion, more user friendly hack-able version of `sed` which I call `sim`. It uses a json schema as its current front end and supports all of GNU `sed`s commands but can be extended in the following ways:

1. The front end can change without having to change the infrastructure of the program.

2. Commands can be added without awareness of the surrounding context. The only implementation that the developer is required to understand is the name of the command and a general function which has access to all of the information which the program has access to.

For a more detailed explanation of exactly how this can be accomplished you can see the [hacking guide](https://github.com/millipedes/sim/blob/develop/docs/dev/hacking\_sim.md).

I use this tool in my job daily and think that there are some cool abstractions in it that allow it to fit many workflows and thought I would share. Thanks for reading, if you have any questions I will answer them to the best of my ability.

My implementation can be found [here](https://github.com/millipedes/sim/tree/develop).

Hello.

I hate VSCode snippet syntax. It is tedious to write, and very limited. I have searched for alternative extensions, but the internet is too bloated with basic tutorials about how to write VSCode snippets. Is there any tool (even if it is external to VSCode) where you can use a proper programming language where needed? The closest thing I know are luasnip, but that is limited to only neovim, but something like that is what I'm looking for.

Introducing rjq - A Blazingly Fast JSON Filtering CLI Tool

I'm excited to announce the release of rjq, a Rust-based CLI tool for filtering JSON data with ease.

Key Features:

• Simple query syntax for effortless filtering
• Blazingly fast performance
• Support for streaming JSON data

With rjq, you can:

• Quickly extract specific data from large JSON datasets
• Filter and transform data with ease
• Automate JSON data processing tasks

Perfect for:

• Developers working with JSON data
• Data analysts seeking efficient data extraction
• DevOps teams automating data processing tasks

Explore rjq on GitHub: github.com/mainak55512/rjq

Get started with rjq today and accelerate your JSON data processing!

#rjq #JSONFiltering #CLI #Rust #DeveloperTools #Productivity

The 10 min video walkthrough explores the best practices of generating code with AI: 8 Best Practices to Generate Code Using AI Tools

It explains some aspects as how breaking down complex features into manageable tasks leads to better results and relevant information helps AI assistants deliver more accurate code:

1. Break Requests into Smaller Units of Work
2. Provide Context in Each Ask
3. Be Clear and Specific
4. Keep Requests Distinct and Focused
5. Iterate and Refine
6. Leverage Previous Conversations or Generated Code
7. Use Advanced Predefined Commands for Specific Asks
8. Ask for Explanations When Needed

Hey fellow devs,

Remember that VS Code extension I made after our discussion here on Reddit?
Well, it's grown quite a bit since then, and I'm excited to share the latest update with you.

What's new in v1.2.31 🎉

Ignore feature: Users can now select and use ignore files (like .gitignore) to exclude specific files or directories from mapping.

🔽 Download from the VSCode Marketplace: Folder Mapper v1.2.31

https://reddit.com/link/1fz45gm/video/s4pcuza98ktd1/player

Why it matters: As someone who uses AI for coding, I often found myself needing a tool to map my project structure. I couldn't find one, so I built it!
Now, with the new exclusion feature, you have even more control over what gets mapped.

With an ignore file you can:

• Exclude a specific file
• Exclude a specific directory and all its contents (directory won't appear in the map)
• Exclude all files with a specific extension
• Exclude all files that start with a specific prefix
• Exclude all files that end with a specific suffix
• Exclude all files inside a directory, but keep the directory itself in the map (directory will appear empty)
• Exclude all files of a specific type in any subdirectory
• Negate a rule (include a file that would otherwise be excluded)
• Exclude files or directories with spaces in their names (use quotes)
• Exclude multiple files or directories with similar names
• Exclude a range of files

I'm the sole developer of this project, and your feedback has been invaluable. From a simple Python script to a full-fledged VS Code extension, this journey has been absolutely incredible so far!

🔽 Download from the VSCode Marketplace: Folder Mapper v1.2.31

What exclusion patterns would you find most useful?
Any other features you'd like to see?

Hello all,

I am working introductory level with R for a bibliometric mapping analysis project. I am stuck with a RIS file with all my sources but I need it in a Bibtext format. Is there any easy way to convert this?

It got pretty tiring digging for code in old projects just to reuse in a new one.

Most projects/components/UI follow similar patterns that don’t need to be rewritten.

So, I built a code snippet vault which allows you to store the code you frequently use and grab it from there when you need it.

Code it once, reuse over and over.

You can also update your snippets, track the version changes and invite team members!

No more looking through old projects and unpicking bits - just rapid development!

It would be awesome to gather your feedback.

www.hycode.io

https://preview.redd.it/7zdb997cherd1.png?width=157&format=png&auto=webp&s=8ec1d90e21f419d8019716e89cdcc1cf9155fcfb

Hey r/programmingtools!

Tired of juggling GitLab issues and tasks across different tools? Meet Mochi, a keyboard-driven, GitLab-integrated Kanban board that lets you manage your tasks without ever touching your mouse.

Key Features:

• Kanban-style organization
• Seamless GitLab integration (issues, merge_requests and comments are synced)
• 100% keyboard-friendly (say goodbye to carpal tunnel!)
• CRUD tasks like a boss
• Open tasks directly in GitLab
• Keyboard-Driven (press h to view the help modal)

Check it out: GitHub - Mochi

Feedback is highly appreciated.

Hi all, to kick-start my career in my spare time I am working on a desktop application as a side project.

At work I am in a more Data versed environment so my front/backend development side knowledge is limited to what I remember from my university days which by necessity will be outdated by now.

My application for now consists of a backend side in python that goes to develop core functionality (mainly integration of LLM with other services and vector databases) and fin is quite my field.

My difficulty is first of all finding the right technology stack for what I want to do, which is a small, simple desktop application, but going to use tools and frameworks palatable in the market that allows me to develop resalable skills, mainly backend side.

I was thinking for the front end to use a browser directly and then develop using web development tools.

For the backend I see that FastAPI is in high demand, which in theory should allow me later to easily expand to other features and allow me to tie core features to the front end.

I apologize if I am imprecise and a there is some confusion in the reasoning but I am reapplying after years of something else.

Any advice, links to guides, tutorials or criticism is most welcome.

Thank you

The article below outlines the core principles of agile software development, including flexibility, collaboration, and using customer feedback for enhancing team productivity and adapting to changing requirements: Agile Software Development: Best Practices for Clean Code and CI

The comparison below discusses the best coding AI copilots for 2024 - as advanced tools that assist developers throughout the software development lifecycle by providing real-time code suggestions and completions (which distinguishes them from regular coding AI assistants that may only offer task-specific support): 4 Best Coding AI Copilots for 2024

It explains the key benefits of these copilots as increased efficiency, error reduction, consistent code quality, and natural language processing.

The 5-min video tutorial explores how to make the most of CodiumAI’s GitHub-based PR Agent for making your pull request workflow significantly more effective - by helping you improve the code in your PR, better understand what’s going on in the PR, generate top-notch documentation, and create PR description.

The article discusses the best coding AI copilots for 2024 - as advanced tools that assist developers throughout the software development lifecycle by providing real-time code suggestions and completions (which distinguishes them from regular coding AI assistants that may only offer task-specific support): 4 Best Coding AI Copilots for 2024

It explains the key benefits of these copilots as increased efficiency, error reduction, consistent code quality, and natural language processing.

The article below highlights how AI-driven automated test case prioritization and generation tools enhance productivity and accuracy in testing by automating repetitive tasks and making intelligent predictions based on data analytics: Top Automated Test Case Prioritization & Generation Tools

The article below provides an in-depth overview of conformance testing for ensuring that software systems adhere to specified standards and requirements: Conformance Testing for Software

It outlines the various approaches to conformance testing, including formal methods, model-based testing, and automated testing techniques as well as discusses its best practices, such as creating a clear testing strategy, utilizing appropriate tools, and ensuring thorough documentation.

The article discusses strategies to improve software testing methodologies by adopting modern testing practices, integrating automation, and utilizing advanced tools to enhance efficiency and accuracy in the testing process. It also highlights the ways for collaboration among development and testing teams, as well as the significance of continuous testing in agile environments: Enhancing Software Testing Methodologies for Optimal Results

The functional and non-functional testing methods analysed include the following:

• Unit testing
• Integration testing
• System testing
• Acceptance testing
• Performance testing
• Security testing
• Usability testing
• Compatibility testing

Hey everyone, today I'm very happy to have launched https://gist.cool which allows everyone to create link preview to their gists - entirely free!

Would love your feedback, thanks!

Hi ,

I have tried few AI based tool for code review but they are more or less like sonar, results are seems static only . Want to have something that say can detect memory or performance issue in a code rather just highlight hardcoded value or exceptional handling is not there etc Pls suggest

The article discusses best practices and various aspects of software testing, to provide a comprehensive checklist to ensure effective testing processes: Software Testing Best Practices Checklist

• Test Planning
• Test Design
• Test Execution
• Defect Management
• Continuous Improvement