Machine Learning for Solving Root Cause Application Errors & Failures

Introduction

In today's world, even a single application error could have significant consequences for both businesses and individual developers. From lost revenue and damaged reputation to disrupted services and frustrated customers, the cost of not addressing bugs in your code can be quite high.

There are more examples than we can count, but some high profile outages in recent years have included Spotify, Amazon, or Twitch. In each case, affected businesses lost millions of dollars and disrupted services for countless users.

When you’re dealing with any type of application failure, every second spent on detecting and resolving the root cause of the issue is critical.

Unfortunately, most traditional methods of root cause analysis are time-consuming and resource-intensive, forcing your team into hours of manually analyzing log files and blindly pushing random fixes.

Possible solution: machine learning (ML). By using ML algorithms to analyze your error logs, your developers can identify the root cause of each bug much faster and more efficiently. AI-powered algorithms can find patterns within your error logs that might not be immediately obvious to your human analysts. By saving time on categorizing and prioritizing errors, you can speed up your resolution process and improve overall team efficiency.

In this blog post, we will explore the challenges of traditional root cause analysis, the potential of ML to improve the process, and our own approach to root cause analysis here at Railtown.ai through one-click platform integration.

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The Challenges of Traditional Root Cause Analysis

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Too Many Logs, Not Enough Time

When it comes to identifying the root cause of your application’s failure, one major challenge is simply getting through the volume of all the log files that you need to analyze.

Your log files are probably extensive, especially if you are working on a larger project or on a full team. With thousands of lines of logs, it’s impossible for any human developer to efficiently review every stack trace.

The logs your team is capable of reviewing, are probably limited in other ways. Most error logging tools only flag the stack trace directly related to a single exception, providing you with a snapshot from a short period of time and lacking other necessary context for understanding the root cause of your issue.

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Unstructured Data

Logs tend to contain unstructured data, making it difficult for you to quickly identify relevant information.

Without a clear understanding of what you’re searching for, you might struggle to effectively analyze your log data. And without proper analysis or ability to read a particular stack trace, it’s difficult to identify larger patterns and correlations across various exceptions or environments. You may be able to spot an error message in your logs, but without context or understanding of what caused the error in the first place, it can be difficult to determine the root cause of that error.

In these cases, machine learning can be a valuable tool. By using algorithms and statistical analysis, ML can quickly identify patterns and correlations in log data that may not be immediately apparent to human analysts. This can help to speed up the root cause analysis process and improve the efficiency of identifying and addressing software failures.

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Lack of Context

Lack of context can be a major hurdle in root cause analysis, as you might struggle to understand the circumstances surrounding a particular exception.

Even skilled engineers and support teams can struggle to effectively analyze log files without adequate context. Add to that the pressures of limited time, capacity, or angry customer messages within support tickets and your ability to discover and address bugs in your code goes down even further.

In order to effectively address software failures, you need to clearly understand the problem that you’re facing. Without sufficient context, it can be difficult for you to identify the root cause and implement a solution.

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How ML Can Identify Patterns and Correlations in Log Data

As software developers ourselves, we know the importance of proper root cause analysis in the debugging process. With increasing complexity of modern software, you might view improving your logs and error analysis as a daunting task. This is where machine learning (ML) and artificial intelligence (AI) can help.

One of the key benefits of using AI for debugging and error logging is the ability to quickly and accurately analyze large volumes of log data. With traditional methods, sift through logs to identify patterns and correlations can take hours, days, or even weeks depending on the complexity of your code. A properly configured ML algorithm, on the other hand, can handle this task within minutes, freeing your developers to focus on more important tasks.

AI is also great at identifying patterns and correlations that a human developer might miss. By taking into account multiple variables and considering their interactions, a machine learning algorithm can uncover hidden patterns and relationships between your separate errors, leading you to a root cause that you might not expect.

Finally, using ML for root cause analysis can improve the accuracy of your bug fixes and patches. Since AI can identify patterns and correlations that you might miss, you are more likely to discover underlying problems within your code that might be throwing individual exceptions. This perspective can ensure that you are properly addressing and resolving issues, instead of masking deeper problems within your application with superficial temporary fixes.

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Railtown.ai's Approach to Root Cause Analysis 

At Railtown.ai, we understand the importance of effective root cause analysis in ensuring the smooth operation of software systems. That's why we've developed a tool that leverages machine learning algorithms to group and pinpoint the root cause of errors within your code.

Our approach to root cause analysis also emphasizes the importance of human review. You can view all of your logs and insights within a simple and intuitive dashboard. This way, you and your team can review and confirm error buckets and root causes identified by our AI, ensuring that you can properly understand and address every bug.

Railtown.ai also automates the process of matching identified root causes with relevant tickets and fixes through one-click platform integration. This way, you can streamline the debugging and error logging process, saving time and effort for your team.

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First Time Error Notifications & Root Cause Analysis

Since we are able to use AI to match individual errors to previous exceptions within your logs, Railtown.ai can inform you whether any individual error is a first-time or repeat error. 

This way, you can diagnose issues more thoroughly by viewing the entire history of exceptions caused by an error and connect the dots across users, time, and your production and testing environments. When you incorporate Railtown.ai’s First Time Error and Repeat Error classification into your CI/CD system, your team can react quickly whenever a new error is introduced when you deploy code into a particular environment.

You can see what fixes your team has already pushed to try to resolve an issue and whether they’ve worked, as well as what team member has worked on that part of your code most recently.

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Final Thoughts

The use of machine learning and AI for root cause analysis can bring a number of benefits to you and your team.

By quickly and accurately analyzing large volumes of log data, AI algorithms can identify patterns and correlations to pinpoint the root cause of software errors. These insights can then improve the speed and efficiency at which your developers identify and resolve any software failures, minimizing downtime and improving your overall productivity.

At Railtown.ai, we combine the power of machine learning with the expertise of human analysts on your team, providing you with intelligent insights and a simple interface for interacting with our tool.

To improve the speed and efficiency of your root cause analysis process, consider implementing AI-driven techniques. If you want to see the power of machine learning in action, try out Railtown.ai for yourself.

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