Scaling Building Height With Area Size Enhancing Code City Visualizations

Hey guys! Let's dive into how we can make our code city visualizations even better, especially when dealing with large projects. One of the challenges we often face is that the height metric, which is super useful for showing code size or complexity, can become less effective in medium to large codebases. Think about it: when you've got hundreds of thousands or even millions of lines of code (RLoC), the buildings in your code city can start to look like they're all the same height. This makes it tough to quickly spot the really big or complex areas.

The Problem with Height in Large Codebases

So, the main issue here is that in larger maps, the height metric doesn't clearly show the differences between different parts of the codebase. Imagine you're trying to visualize a project like teammates, which is a decent-sized project. When you render it as a squarified map, you might see something where all the buildings look almost the same height. This makes the height metric pretty useless, which is a bummer because it's such a valuable way to understand the structure and complexity of our code.

Visualizing the Issue

To really drive this home, let's look at some examples. If you take a project and visualize it as a squarified map, you might notice that the heights of the buildings are almost uniform. This is a problem because you lose the ability to quickly identify which parts of the system are the largest or most complex. Now, if you render the same data as a street map, suddenly, the differences in height become much more apparent. This tells us that the way we're scaling the height isn't working effectively for all types of visualizations.

The Height Scaling Dilemma

You might think, "Okay, let's just increase the height scaling!" But here's the catch: even if you crank up the scaling, the differences can still be minimal. This is because the range of values for the height metric might be too broad, or the scaling function we're using isn't effectively mapping those values to visual height. The goal is to make those height differences pop, so you can instantly see which parts of the codebase are the heavy hitters.

Proposed Solution: Enhancing Height Differentiation

Alright, so how do we fix this? The core idea is to make sure the building height clearly reflects the differences in the underlying data, even in massive codebases. We need a solution that works not just for small projects, but also for medium-sized projects like teammates and huge ones like OpenJDK or NetBeans. We want those skyscrapers to really stand out from the smaller buildings!

Adjusting Height Scaling for Clarity

One approach is to explore different scaling functions. Instead of a linear scale, which can flatten out differences at the higher end, we might try a logarithmic or exponential scale. These types of scales can help compress the range of heights, making it easier to see variations. Imagine squishing the very tall buildings down a bit and stretching the shorter ones up – this could make the overall height differences more apparent.

Adaptive Scaling Techniques

Another cool idea is to implement adaptive scaling. This means the scaling function changes depending on the size and distribution of the data. For instance, if we detect that the height values are heavily skewed, we could automatically adjust the scaling to compensate. This could involve calculating percentiles and mapping heights based on those, or using more advanced statistical techniques to normalize the data.

Combining Height with Area

Here's a thought: what if we combined height with area to represent different aspects of the code? For example, we could use the area to represent the number of files or classes, and the height to represent the lines of code or complexity within those files. This multi-dimensional approach could give us a richer understanding of the codebase. It’s like saying, “This building is big because it has a lot of apartments (files), and it’s tall because each apartment is huge (complex).”

Acceptance Criteria: What Success Looks Like

So, how do we know if we've nailed it? We need to define some clear acceptance criteria. Basically, the building height should show clear differences at different scales:

• Medium-Sized Projects: For projects like teammates, you should be able to easily see which modules or components are the largest and most complex just by looking at the height of the buildings.
• Large Projects: For massive projects like OpenJDK or NetBeans, the height metric should still provide meaningful information. You shouldn't just see a uniform skyline; you should see distinct peaks and valleys that correspond to different parts of the codebase.

In essence, we want the height metric to be a reliable guide, no matter the size of the city. It should help us quickly identify hotspots, understand the overall structure, and make informed decisions about where to focus our attention.

Diving Deeper into Scaling Techniques

To really get this right, let's dig into the nitty-gritty of scaling techniques. We've already touched on linear, logarithmic, and exponential scales, but there's a whole world of possibilities out there. The key is to find a scaling function that effectively maps the raw metric values (like lines of code or complexity) to a visual representation (the height of the building) in a way that's both accurate and easy to interpret.

Linear Scaling: The Straightforward Approach

Linear scaling is the simplest method: you map the minimum value in your dataset to the minimum height, and the maximum value to the maximum height, with everything else scaled proportionally. This is great for datasets where the values are evenly distributed, but it can fall short when you have a few very large values that skew the scale. Imagine a few skyscrapers towering over a city of bungalows – the bungalows become almost invisible.

Logarithmic Scaling: Taming the Giants

Logarithmic scaling is your friend when dealing with skewed data. It compresses the higher values, making it easier to see differences in the lower values. This is like zooming in on the bungalows while still keeping the skyscrapers in view. Log scaling can be particularly useful for codebases where a few files or modules are significantly larger than the rest.

Exponential Scaling: Emphasizing the Small

Exponential scaling, on the other hand, emphasizes the smaller values. This can be handy if you want to highlight subtle differences in the smaller buildings, perhaps to draw attention to areas that are deceptively complex despite their size. It's like shining a spotlight on the details that might otherwise be overlooked.

Power Scaling: A Flexible Middle Ground

Power scaling offers a flexible middle ground between linear, logarithmic, and exponential scaling. By adjusting the exponent, you can fine-tune the scaling to suit your specific dataset. This gives you more control over how the heights are distributed and can be particularly useful for creating visualizations that tell a specific story.

Quantile Scaling: Ensuring Fair Representation

Quantile scaling is a non-linear technique that divides your data into equal-sized groups (quantiles) and maps each group to a specific height range. This ensures that each quantile is represented visually, regardless of the actual value distribution. It’s like giving everyone a fair share of the visual space, even if some values are much larger than others.

Adaptive Scaling in Action

Now, let's talk about adaptive scaling in more detail. The idea here is that the scaling function isn't fixed; it adapts to the data. This can be a game-changer for large, complex codebases where the distribution of metric values can vary significantly.

Identifying Skew and Adjusting

One common adaptive technique is to detect skew in the data and adjust the scaling accordingly. If the data is heavily skewed towards larger values, you might automatically switch to a logarithmic or power scale. If it's skewed towards smaller values, you might use an exponential scale. This ensures that the visualization always provides a clear and informative view of the data.

Using Percentiles for Dynamic Range

Another approach is to use percentiles to define the height range. For example, you might map the 10th percentile value to the minimum height and the 90th percentile value to the maximum height. This effectively ignores outliers and focuses on the bulk of the data, which can make the visualization more meaningful.

Statistical Normalization Techniques

For more advanced adaptive scaling, you can employ statistical normalization techniques like Z-score normalization or min-max scaling. These methods transform the data to have a specific mean and standard deviation or to fit within a specific range, making it easier to compare values across different scales.

The Power of Combined Metrics

We've also touched on the idea of combining height with other visual cues, like area or color. This multi-dimensional approach can unlock even deeper insights into your codebase. Think of it as adding layers of information to your city map.

Area for Size, Height for Complexity

One compelling combination is to use the area of a building to represent the size of a module or component (e.g., the number of files or classes) and the height to represent its complexity (e.g., lines of code, cyclomatic complexity). This lets you quickly identify not only the largest parts of the system but also the most complex ones.

Color Coding for Categories or Types

Adding color can further enrich the visualization. You could use different colors to represent different types of files (e.g., source code, tests, configuration files) or different categories of modules (e.g., UI, business logic, data access). This makes it easier to spot patterns and relationships within the codebase.

Interactive Exploration

Of course, the best code city visualizations are interactive. Users should be able to zoom in, pan around, and drill down into individual buildings to explore the underlying code. This interactivity is crucial for making the visualization a truly useful tool for understanding and navigating large codebases.

Wrapping Up: Building Better Code Cities

So, there you have it – a deep dive into scaling building height for code city visualizations. By carefully choosing and tuning our scaling techniques, and by combining height with other visual cues, we can create powerful tools for understanding and navigating even the most massive codebases. The goal is to make those code cities come alive, revealing the structure and complexity hidden within the code. Let's get building!