Code Red: The Business Impact of Code Quality

Wednesday, 7:30 PM EST

Code quality is an abstract concept that fails to get traction at the business level. Consequently, software companies keep trading code quality for new features. The resulting technical debt is estimated to waste up to 42% of developers' time, causing stress and uncertainty, as well as making our job less enjoyable than it should be. Without clear and quantifiable benefits, it's hard to build a business case for code quality.

In this keynote, Adam takes on the challenge by tuning the code analysis microscope towards a business outcome. We do that by combining novel 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. This makes it possible to a) prioritize the parts of your system that benefit the most from improvements, b) communicate quality trade-offs in terms of actual costs, and c) identify high-risk parts of the application so that we can focus our efforts on the areas that need them the most. All recommendations are supported by data and brand new real-world research. This is a perspective on software development that will change how you view code. Promise.

Prioritize Technical Debt as if Time and Money Matter

Friday, 1:30 PM EST

Prioritizing technical debt is a hard problem as modern systems might have millions of lines of code and multiple development teams — no one has a holistic overview. In addition, there's always a trade-off between improving existing code versus adding new features so we need to use our time wisely.

What if we could mine the collective intelligence of all contributing programmers and start making decisions based on information from how the organization actually works with the code?

In this workshop, you'll learn how easily obtained version-control data lets you uncover the behavior and patterns of the development organization. This language-neutral approach lets you prioritize the parts of your system that benefit the most from improvements so that you can balance short- and long-term goals guided by data.

In this session, you’ll learn:

To prioritize technical debt in large-scale systems
Balance the trade-off between improving existing code versus adding new features
Visualize long time trends in technical debt
Take a data-driven approach to technical debt.

During this workshop, you get access to CodeScene – a behavioral code analysis tool that automates the analyses – which we use for the practical exercises. We’ll do the exercises on real world codebases in Java, C#, JavaScript and more to discover real issues.

Participants are also encouraged to take this opportunity to analyze their own codebase to get actionable take-away information.

Prioritize Technical Debt as if Time and Money Matter

Friday, 3:00 PM EST

Prioritizing technical debt is a hard problem as modern systems might have millions of lines of code and multiple development teams — no one has a holistic overview. In addition, there's always a trade-off between improving existing code versus adding new features so we need to use our time wisely.

What if we could mine the collective intelligence of all contributing programmers and start making decisions based on information from how the organization actually works with the code?

In this workshop, you'll learn how easily obtained version-control data lets you uncover the behavior and patterns of the development organization. This language-neutral approach lets you prioritize the parts of your system that benefit the most from improvements so that you can balance short- and long-term goals guided by data.

In this session, you’ll learn:

To prioritize technical debt in large-scale systems
Balance the trade-off between improving existing code versus adding new features
Visualize long time trends in technical debt
Take a data-driven approach to technical debt.

During this workshop, you get access to CodeScene – a behavioral code analysis tool that automates the analyses – which we use for the practical exercises. We’ll do the exercises on real world codebases in Java, C#, JavaScript and more to discover real issues.

Participants are also encouraged to take this opportunity to analyze their own codebase to get actionable take-away information.