Identify and Prioritize Technical Debt

In this tutorial, using MongoDB codebase as an example, we'll show you how to remediate technical debt based on impact. What parts of the codebase are getting better or worse?

Understanding Code Complexity

In this short tutorial, we'll walk you through the reasoning behind our Code Health Metric and explain its importance by examining different code smells and their complexity.

Communicating Delivery Uncertainty with Non-Technical Stakeholders

How can we communicate the impact of technical improvements to non-technical stakeholders? We're using a case study of a failed project to highlight the business cost of technical debt and how to bridge the gap between engineering and business.

CodeScene in Weekly Stand-ups

Let's look at how teams can use CodeScene as a talking point in their weekly team stand-ups to get everyone on the same page about code quality. Enable your team to gain a shared understanding of your codebase and software projects.

How Does CodeScene Differ From a Static Code Analysis?

In this tutorial, we'll explore how CodeScene differs from static code analysis tools, focusing on our perspective on code health and the human aspect of maintainability, specifically, how easily developers can understand, read, and modify code with minimal risk.

Hotspots & Refactoring Targets

In this short video we'll walk you through the system maps so you can quickly understand where your hotspots are and also identify your refactoring targets.

Virtual Code Review Deep Dive

Let's deep dive into a single file to undertand its low code health and what can be done to improve it with CodeScene's Virtual Code Review.

Fix Technical Debt with AI

Refactoring doesn’t have to be a burden. CodeScene ACE handles the heavy lifting, enabling targeted refactorings that streamline tasks and keep your team focused on innovation.

Automated Code Reviews

In this tutorial, we'll walk you through on how CodeScene integrates with pull/merge requests to provide code health feedback and detect code quality issues via an automated code review.

Code Health Feedback in Your IDE

In this tutorial, we'll walk you through how to get real-time code health feedback directly inside your editor. Learn how to tackle code complexity within the editor and ensure you only commit high-quality code. Available for JetBrains, Visual Studio, and VS Code.

Visualize Change Coupling in Your IDE

Explore how to leverage the Change Coupling feature right within your editor. High change coupling adds complexity to your codebase, often leading to hidden dependencies that can slow you down.

Improve Code Quality in Your Terminal

Let's walk you through on how you can improve your code quality directly in your terminal with CodeScene's CLI tool. By identifying potential issues early, you can address them before they make it into the codebase.

Extending Cursor with Automated Code Reviews

Let's walk you through the steps you need to take to integrate automated code reviews within Cursor AI for maintainable, high-quality code with real-time feedback and AI-refactoring.

Surviving the Bus Factor

This tutorial covers essential techniques and key organizational metrics vital for any software project’s success. Discover how to reduce Bus Factor risk by analyzing metrics that reveal knowledge distribution within a codebase.

Prioritizing Technical Debt as If Time & Money Matters

Many codebases have overly complicated code that is hard to understand and costly to change. Prioritizing technical debt is difficult in modern systems with millions of lines of code and multiple teams lacking a holistic overview. So what do we do?

Code Red: The Business Impact of Code Quality

Code quality is often seen as an abstract concept that struggles to gain traction at the business level, leading software companies to prioritize new features over quality. Without clear, quantifiable benefits, building a business case for code quality becomes challenging.

Exploring the Fact-based Realities of AI-Assisted Coding

In this talk, Adam discusses the short- and long-term implications of using AI assistants for coding, drawing on extensive CodeScene research analyzing over 100k AI refactorings in real-world codebases. This data challenges the productivity claims of current AI assistants, highlighting the distinction between code-writing speed and true productivity.

How Empirical Data Shatters the Speed vs Quality Myth

In this talk, Adam takes on the challenge by combining innovative code quality metrics with analyses of how the engineering organization works with the code. We then take those metrics a step further by connecting them to values like time-to-market, customer satisfaction, and road-map risks.

How Technical Problems Cause Organizational Friction

Successful software development requires that you keep code and people in balance so that one supports the other. It's a hard challenge since a piece of code doesn't reveal anything about its socio-technical context. Enter behavioral code analysis, an approach which combines code level metrics with data on how teams interact within the code.

Refactoring With AI — Thoughtworks Technology Podcast

In this episode of the Technology Podcast, Adam Tornhill, CTO and Founder of CodeScene, joins Thoughtworks' Rebecca Parsons (CTO Emerita), Birgitta Böckeler (Global Lead for AI-assisted software delivery) and Martin Fowler (Chief Scientist and author of the influential Refactoring book) to discuss all things AI and code.

The Quest for Evidence-Based Technical Debt Management

Markus Borg, Principal Researcher at CodeScene & Adjunct Associate Professor at Lund University, presents his Oπe conf 2023 Speech titled “The Quest for Evidence-Based Technical Debt Management”.

Refactoring vs Refuctoring: Code Quality in the Al Age

In this talk, we explore AI-assisted code generation, revealing hidden pitfalls of using these tools without proper safeguards. We'll also demonstrate how to use AI to produce reliable, trustworthy code.

Advancing the state of AI-automated code improvement

Some people prefer listening over reading, so we’ve created an AI-generated podcast hosted by our virtual duo, Amy and Jim.

In this episode, the duo discusses the research "Refactoring vs Refuctoring: Advancing the state of AI-automated code improvement", which reveals that current AI tools perform correct refactorings only 37% of the time.

A fact-checking approach improves that accuracy to 98%, outperforming humans and offering a major step forward in automating code improvements and decreasing technical debt.

Business Impact of Code Quality

Some people prefer listening over reading, so we’ve created an AI-generated podcast hosted by our virtual duo, Amy and Jim.

In this episode, the duo talks about the research paper "Code Red: The Business Impact of Code Quality," which explores how code quality affects software delivery and product outcomes.

The study shows that healthy code leads to 15x fewer bugs, twice the development speed, and 9x more predictable delivery times.