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See Bito’s AI Code Review Agent in action

Real-world examples of how AI improves your code reviews
Supported languages: C, C++, Java, JavaScript, PHP, Python, & more
Supported languages:
C, C++, Java, JavaScript, PHP, Python, & more
Choose a language to see examples (More examples coming soon)
Choose a language to see examples
(More examples coming soon)
Example 1:

Agent fixes issues in PR, streamlining the merge for Devika

Devika is an open-source alternative to Devin AI, an AI software engineering agent powered by large language models (LLM).
Bito’s AI Code Review Agent identified issues in a pull request that aimed to enable the configuration of the OpenAI API Base URL and to integrate DuckDuckGo as an alternative search engine alongside Bing.
Suggestion 1:
The Agent suggested a fix for a potential issue where the code might not handle empty or missing values for the base_url parameter during OpenAI client initialization.
				
					if not base_url:
    base_url = 'https://api.openai.com/v1'
self.client = OAI(
    api_key=api_key,
    base_url=base_url
)
				
			
Suggestion 2:
See how the Agent spotted type inconsistencies in the DuckDuckGoSearch class’s Search method, which could trigger runtime errors when consuming the method’s output.
				
					try:
    self.query_result = DDGS().text(query, max_results=5)
    return self.query_result
except Exception as err:
    # Log the error
    print(f'Error occurred: {err}')
    return []
				
			
Example 1:

AI identifies memory leak issue in Java code

Apache Dubbo is a high-performance, Java-based open-source RPC and microservice framework.
Bito’s AI Code Review Agent, capable of understanding Java code, identified issues in a pull request focusing on fixing a memory leak and adding new features. The Agent recommends adding unit tests for new code paths and ensuring thread safety to enhance code reliability.
Suggestion 1:
The Agent identified a potential resource leak in the Http2ServerChannelObserver class, where the closed flag is set without ensuring resource cleanup, which may cause performance issues.
				
					if (closed) {
    releaseAssociatedResources(); // Implement this method to clean up resources
}
				
			
Suggestion 2:
The Agent suggested validating the state of getStreamingDecoder() before invoking close() to avoid potential NullPointerException , which could cause runtime errors. 
				
					if (getStreamingDecoder() != null) {
    getStreamingDecoder().close();
}
				
			
Example 2:

Identify scalability or performance issues early and often

Netty is an asynchronous event-driven network application framework for rapid development of maintainable high performance protocol servers and clients. 
Bito’s AI Code Review Agent identified issues in a pull request that refactors AbstractScheduledEventExecutor to use a pluggable TaskScheduler interface for flexibility. The Agent suggested adding a TaskSchedulerFactory for dynamic creation and commended the new tests.

Suggestion 1:
See how the Agent addressed a scalability issue by suggesting an object pool for RunnableScheduledFuture instances instead of creating new ones, significantly improving performance by reducing object creation and garbage collection.
				
					private final ObjectPool<RunnableScheduledFuture<?>> futurePool;
public AbstractTaskScheduler(ObjectPool<RunnableScheduledFuture<?>> futurePool) {
    this.futurePool = futurePool;
}

@Override
public Future<Void> schedule(Runnable command, long delay, TimeUnit unit) {
    RunnableScheduledFuture<Void> task = futurePool.get();
    task.reset(command, delay, unit);
    return schedule(task);
}
				
			
Suggestion 2:
The Agent identified a performance issue in DefaultTaskScheduler , suggesting a custom approach for comparing RunnableScheduledFutureNode instances to enhance efficiency in managing scheduled tasks, particularly beneficial for large-scale operations.
				
					private static final Comparator<RunnableScheduledFuture<?>> TASK_SCHEDULER_COMPARATOR = new Comparator<RunnableScheduledFuture<?>>() {
    @Override
    public int compare(RunnableScheduledFuture<?> o1, RunnableScheduledFuture<?> o2) {
        return Long.compare(o1.getDelay(TimeUnit.NANOSECONDS), o2.getDelay(TimeUnit.NANOSECONDS));
    }
};

private PriorityQueue<RunnableScheduledFuture<?>> scheduledTaskQueue = new PriorityQueue<>(TASK_SCHEDULER_COMPARATOR);
				
			
Example 1:

Agent fixes issues in PR, streamlining the merge for Devika

Devika is an open-source alternative to Devin AI, an AI software engineering agent powered by large language models (LLM).
Bito’s AI Code Review Agent identified issues in a pull request that aimed to enable the configuration of the OpenAI API Base URL and to integrate DuckDuckGo as an alternative search engine alongside Bing.
Suggestion 1:
The Agent suggested a fix for a potential issue where the code might not handle empty or missing values for the base_url parameter during OpenAI client initialization.
				
					if not base_url:
    base_url = 'https://api.openai.com/v1'
self.client = OAI(
    api_key=api_key,
    base_url=base_url
)
				
			
Suggestion 2:
See how the Agent spotted type inconsistencies in the DuckDuckGoSearch class’s Search method, which could trigger runtime errors when consuming the method’s output.
				
					try:
    self.query_result = DDGS().text(query, max_results=5)
    return self.query_result
except Exception as err:
    # Log the error
    print(f'Error occurred: {err}')
    return []
				
			
Example 1:

AI identifies memory leak issue in Java code

Apache Dubbo is a high-performance, Java-based open-source RPC and microservice framework.
Bito’s AI Code Review Agent, capable of understanding Java code, identified issues in a pull request focusing on fixing a memory leak and adding new features. The Agent recommends adding unit tests for new code paths and ensuring thread safety to enhance code reliability.
Suggestion 1:
The Agent identified a potential resource leak in the Http2ServerChannelObserver class, where the closed flag is set without ensuring resource cleanup, which may cause performance issues.
				
					if (closed) {
    releaseAssociatedResources(); // Implement this method to clean up resources
}
				
			
Suggestion 2:
The Agent suggested validating the state of getStreamingDecoder() before invoking close() to avoid potential NullPointerException , which could cause runtime errors. 
				
					if (getStreamingDecoder() != null) {
    getStreamingDecoder().close();
}
				
			
Example 2:

Identify scalability or performance issues early and often

Netty is an asynchronous event-driven network application framework for rapid development of maintainable high performance protocol servers and clients. 
Bito’s AI Code Review Agent identified issues in a pull request that refactors AbstractScheduledEventExecutor to use a pluggable TaskScheduler interface for flexibility. The Agent suggested adding a TaskSchedulerFactory for dynamic creation and commended the new tests.

Suggestion 1:
See how the Agent addressed a scalability issue by suggesting an object pool for RunnableScheduledFuture instances instead of creating new ones, significantly improving performance by reducing object creation and garbage collection.
				
					private final ObjectPool<RunnableScheduledFuture<?>> futurePool;
public AbstractTaskScheduler(ObjectPool<RunnableScheduledFuture<?>> futurePool) {
    this.futurePool = futurePool;
}

@Override
public Future<Void> schedule(Runnable command, long delay, TimeUnit unit) {
    RunnableScheduledFuture<Void> task = futurePool.get();
    task.reset(command, delay, unit);
    return schedule(task);
}
				
			
Suggestion 2:
The Agent identified a performance issue in DefaultTaskScheduler , suggesting a custom approach for comparing RunnableScheduledFutureNode instances to enhance efficiency in managing scheduled tasks, particularly beneficial for large-scale operations.
				
					private static final Comparator<RunnableScheduledFuture<?>> TASK_SCHEDULER_COMPARATOR = new Comparator<RunnableScheduledFuture<?>>() {
    @Override
    public int compare(RunnableScheduledFuture<?> o1, RunnableScheduledFuture<?> o2) {
        return Long.compare(o1.getDelay(TimeUnit.NANOSECONDS), o2.getDelay(TimeUnit.NANOSECONDS));
    }
};

private PriorityQueue<RunnableScheduledFuture<?>> scheduledTaskQueue = new PriorityQueue<>(TASK_SCHEDULER_COMPARATOR);
				
			

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