Why adding Log Analysis to your QA strategy can benefit you

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All major companies capture daily logs nowadays. Developers, quality assurance analysts, and executives all reference logs before making business-critical decisions. Companies use logs for system monitoring, error logging, and analytics. You can have both quantitative and qualitative approaches for QA testing through log analysis.

What exactly is log analysis? 

Logs are a trail of information that identifies and captures how a particular system functions during a specific period. Log analysis is the process of understanding that information and gathering useful data to mitigate risks for the benefit of the organization. Organizations use log analysis tools to acquire details from logs and understand them. 

The system creates logs for itself and its assigned devices.

A log collector stores logs in a particular pattern into datasets called log datasets. Analysts can scan these logs to check if there’s anything that needs attention. 

Here are the must-have components of a successful log strategy 

Normalization 

Normalization means changing the data present in the log to a standard syntax used by your company. It converts the data in such a way so that anyone who reads the data can easily understand it. For example, the standard for dates can be MM/DD/YYYY.

Tagging and Classification

Tagging and classification distribute the data/ metrics into categories that are easy to understand. It divides the entries through attributes and allows analysts to filter through those attributes.

Pattern recognition 

Pattern recognition helps in data analysis as it compares entries in the log with a particular pattern already logged into the system. It helps to identify anomalies in the data to understand triggers and actionable information.

Correlation analysis 

It’s the technique of collecting data from various sources to derive meaningful conclusions, i.e., to correlate them with each other. Each event has many incidents that are scattered throughout the log. Correlation analysis discovers connections between different incidents to understand and alert regarding any impending issue.

You have to set specific manual parameters for log capturing. You should ensure that the stack trace for the logs is full. All the details associated with an alert should be available in the logs, i.e., timestamp, alert type (error, information, warning, etc.), and the actual entry. Make sure logs don’t capture sensitive information (passwords, account details, etc.). Also, log capturing or analysis should not impact normal operations.

Now that you know what log analysis is and what its key components are, why should you invest in log analysis for your QA? Here’s why.

  • Logs contain data that demonstrates how users use a particular application, how long they use it, and what function they use the most. Logs also identify the number of active users. Through log analysis, you can pinpoint your most loyal customers and understand why they like a particular application. 

Log analysis also determines how the company uses available resources and how it should modify them according to the requirement. 

  • If you’re working for a startup, log analysis can be especially helpful. It shows user statistics and related insights that you can use to drive investment. Log data can also be used to drive sales.

Logs also help create a trail for reporting and a better understanding of executives and developers alike. 

  • Logs help determine bugs and issues on the production and the test systems. It also reports the location, infrastructure, and business impact.  

Logs can also identify if there has been abnormal behavior in the system. The QA team can catalog these incidents in case the normal operations are affected.

  • Log analysis helps to understand the security of the system. It identifies potential security breaches, and compliance with security policies, both internal and external. Log analysis can also identify data breaches. Logs can be used for data recovery. 
  • Logs analysis help design new testing scenarios for applications. Test runs can be done based on log analysis for bug reproduction. QA teams can use this information to determine what caused a particular problem. Log analysis helps to identify the probable root cause and how to avoid it in the future. 

Conclusion 

Log analysis is a critical process that can help you get important information regarding productivity and compliance of your system. It allows engineers to provide support to available devices and enables them to detect risks in the system. 

A QA professional can use log analytics tools to determine quality, usage, and efficiency. Through log analysis, they can optimize their processes, identify and understand issues, and eventually debug them. They can also attach log alerts with the reported incidents for better understanding. Of course, you have to make sure that the log analyzer works within the proper standards and doesn’t affect normal operations. But if used correctly, log analysis can enhance efficiency and increase visibility for the system. 

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