One of the key parts of a software development process is the ability to debug a failure. Let’s face it, your software, even if it is perfect, would face failure some day or the other. In the world of micro services and clouds, there are a lot of integration points any one of which may fail at given certain time. When any one of them fails, how do you debug the failure – the answer is by checking the logs – or in simple words – by implementing proper logging mechanism.
Logging is an essential part of any software development process, including test automation. It helps in identifying issues, debugging problems, and tracking the progress of test cases. When you’re implementing a test automation framework – be it UI,API or performance testing, it is always a good idea to have a certain level of logging implemented in your tests. Let’s discuss what are some good practices for logging in test automation –
Here are some good practices for logging in test automation:
Use a logging framework
Use a logging framework such as Log4j, Logback, or Java Util Logging to make logging more manageable and efficient. These frameworks provide features such as log levels, log rotation, and log formatting.
Define a logging strategy
When you and your team sit down to discuss test strategy for a product or a certain integration point, it is always a good idea to define a logging strategy that outlines what information to log, when to log it, and at what level. This strategy should be reviewed and updated regularly to ensure it is still relevant.
Let’s see what points can be included to design a good logging strategy for test automation-
Here are some key components to consider when developing a logging strategy for test automation:
- Identify what to log: Determine what information is important to log. This might include details such as test case ID, test step description, test data, and any relevant metadata like timestamps, user IDs, or device information.
- Decide when to log: Determine when to log the information, such as before and after a test step, at the end of a test case, or when an error occurs.
- Define log levels: Define the different log levels, such as error, warning, info, or debug, and when to use them. This helps in limiting the amount of logging output and provides the ability to filter logs based on their severity.
- Implement log formatting: Define the format of log messages, including the layout, date format, and message structure. This helps to ensure consistency in logging messages and makes it easier to read and understand the logs.
- Centralise logging: Decide on how to store logs – and the frequency with which they are stored. When deciding this, it would be a good idea to consider a logging platform like Splunk, ELK etc. However, when deciding this, there could be a lot of factors that may come into play – like the how to send data, what data to be sent, frequency etc- which may be decided with the team that owns these platforms.
- Review and analyze logs: Regularly review and analyze logs to identify patterns or issues that could be causing problems in the test automation process. This will help to refine the logging strategy and improve the overall testing process.
Log Messages
Use descriptive log messages that provide information about the test case, the action being performed, and any relevant data. A generic error message like “Some error occurred” or “Internal Server Error” doesn’t clearly indicate what error occurred. However we should be very careful when sharing error messages or log messages. Any user related information or any stack related information which may give out important details should be avoided or masked in the log messages.
Log all errors and exceptions
Log all errors and exceptions, including stack traces, to help identify and debug issues quickly.
Log before and after actions
Log before and after actions to track the progress of test cases and identify any issues. It is always a good practice to log before and after information of certain events – like the start of a certain flow in UI test case and then when the flow is completed, there should a appropriate log messages to specify that the flow has ended.
Use contextual information
Use contextual information such as timestamps, thread IDs, and user IDs, or some internal IDs to help with debugging and identifying issues. These information are very important at many times to identify issues that might be occurring intermittently. Also the format of the timestamps, is important – it should be in a user-friendly format – like a date stamp is much more readable format then providing the information in epoch format.
Log Storage
Store logs centrally to make it easier to access and analyze them. You can use tools like ELK stack, Splunk, or Graylog for centralized logging. Splunk is a very good logging platform which gives the ability to access and analyse data which is ingested by it. Splunk also has this capability to create dashboards and also alerts , which may be useful for reporting purposes.
Limit Logging Output
While it is good practice to include relative information in logs, too much information in the logs may hamper the ability to correctly read them or identify issues in them. Limit the amount of logging output to avoid overwhelming the system or creating unnecessarily large log files. Use log levels to control the amount of logging output.
Analysis of Logs
Review and analyze logs regularly: Regularly review and analyze logs to identify issues and improve the logging strategy. You can create dashboards using the logs that you have which gets updated on regular basis or timely basis to analyse and then present the findings to gauge the overall health of the automation framework.
Overall, logging is an essential part of test automation, and following these good practices will help you get the most out of your logging framework and improve the efficiency of your testing process. We’re following most of these in our team and I’ve love if someone has more information or suggests more ways to improve log analysis.
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