Improving Software Changes with Machine Learning

Software is everywhere! From mobile apps to computer programs, we rely on them for both fun and work. But as software gets bigger and more complex, changing them can be tricky. Sometimes, when you change one part, you need to change another part that’s connected to it. This is known as a “co-change relationship.” Imagine if your car’s brakes and engine both needed fixing at the same time — if you only focus on one, you could end up with a mess.

So, how do developers figure out which parts of software need to change together? Traditionally, they’ve had to rely on their memory, experience, and messy documentation. Spoiler alert: It’s not the most effective way. That’s where we come in with a smarter way to help.

#The Challenge of Change

Big software systems can be like a tightly-knit community. When one member gets a change, others might need to change too. This is especially true for methods in programming — think of them as helpful little functions that do specific tasks. If one method is updated, others that work closely with it might also need some attention.

Detecting these co-change relationships can be difficult. Previous methods often had a lot of false alarms — they flagged too many unrelated changes. Picture a fire alarm that goes off every time someone boils water; it creates panic without reason.

To tackle this problem, we need a better approach. Instead of just looking at the specific changes made, we need to consider the broader context — usually found in something called “Pull Requests,” which are like group changes made together.

#A New Way to Rank Co-Changes

We decided to bring in some brain power from machine learning, which is like teaching computers to learn from data. What if we could train a model that sorts out which methods are most likely to change together? This is called a “learning to rank” (LtR) method. It’s like giving a virtual assistant a list of tasks and asking it to pick the most important ones to focus on.

Our idea is to calculate how often methods have changed together in the past and rank them based on that. The more they’ve worked together, the higher they are on the list of things to check. This way, developers know where to direct their attention.

We ran tests on a whopping 150 open-source Java projects (that’s a lot!). With over 41 million lines of code, we definitely had our hands full. But we found out that our method works pretty well, especially with the Random Forest model. Think of it as a super smart voting system where many small decisions lead to one solid answer.

#What Are We Actually Testing?

When we dive deeper into our tests, we’re really curious about a few key questions:

1. How well does our model rank co-changed methods? We want to see if it’s good at predicting which methods are likely to change together.

2. Can our method beat traditional ways of ranking? We don’t want to just be better; we want to be a game changer.

3. What features matter most when it comes to making accurate predictions? Some features might be more critical than others, and knowing this can help streamline the process.

4. How long can our model stay accurate? If it goes stale too quickly, we’ll need to keep updating — and that can be a hassle.

#Testing Time!

To assess our method, we created several experiments. First, we built a “golden dataset” from the past changes among different methods. This dataset was split into training and testing parts. The training part helps the model learn, and the testing part helps us check how well the model’s learned.

With the training complete, we ran our model and measured its performance using a metric called NDCG, which is a fancy way of checking how well the ranking aligns with actual relevance.

Our tests showed that the Random Forest model was great at figuring out which methods needed attention together, achieving very high rankings compared to other models. It was like finding out your favorite restaurant has a secret menu — you just know it’s going to be good.

#The Features that Matter

In the world of predictions, not all features are created equal. Some are superstars; others just tag along. Our top feature? The number of times methods have co-changed in the past! This little guy has a huge impact on our rankings. Other important features include:

• Path similarity: How closely related the locations of the methods are in the project.
• Authors similarity: If the same people are working on both methods, there’s a higher chance they’ll change together.

On the flip side, some features didn’t have much impact at all. For instance, methods being similar in terms of code didn’t help predict co-changes as expected. It’s like assuming two cousins are best friends just because they share great-grandparents — not always accurate!

#Timing is Everything

Another interesting factor we looked at was how long we should use the past data for training. Too short, and the model may not learn enough; too long, and it might get outdated. After testing several time frames, we found that using 90 to 180 days of history works best. But after 60 days of new predictions, it’s wise to retrain the model. Otherwise, you risk having it send you on a wild goose chase.

#What Does This Mean for Developers?

So, what does all this mean for those coding away in their basements, offices, or coffee shops? Here’s the scoop:

1. Less Bugs: Knowing which methods are often changed together helps developers avoid those pesky bugs that can pop up when changes go unnoticed.

2. Better Quality Code: When developers recognize tightly linked methods, they can work on making them less dependent on each other, leading to cleaner code. It’s like decluttering a messy room; everything will be easier to find!

3. Enhanced Collaboration: By understanding co-change relationships, teams can assign related tasks to the same developers, which leads to more efficient work. Picture two chefs in a kitchen working together — they pass ingredients and ideas, resulting in a better dish.

4. Smarter Testing: Testers can focus on methods likely to be affected by changes, ensuring their testing efforts hit the mark. It’s like using a map instead of wandering around blindly.

#Wrapping Up

In the world of software, where things are always changing and evolving, having a smart way to track and manage these changes is a game changer. By using machine learning to identify and rank co-changed methods, we’ve created a tool that can help developers do their jobs better and faster.

As we continue to refine our approach, we may even branch out to other programming languages and tools, ensuring that this solution can benefit even more developers in the future. After all, who doesn’t love a good upgrade?