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Performance Test Report: Between the Effective and Ineffective Reports
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100 days of Skilled Testing retweeted
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We use axe core selenium flavour plus wave.These are amazing tools for accessibility.
100 days of Skilled Testing retweeted
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I don’t know the trends but learning the basics of is the cure trend as most job roles or positions are mentioning as a need of the hour.
100 days of Skilled Testing retweeted
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1. Re-Use functional test automation for load testing
2. Automatically scan for security issues while doing functional testing / test automation
3. Feed Visual and HTML into AI to get hints on how to improve A/B, usability, seo
100 days of Skilled Testing retweeted
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Also, test cases can be converted from column wise format to BDD format (Gherkin - Given When Then)
100 days of Skilled Testing retweeted
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Sharing our test ideas as bullet pointers to GPT and asking it to write elaborative cases as per our needed templates.
However, this needs good prompt engineering along with scrutinized review of its output.
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It is critical to know what part of the system & its architecture is being evaluated with the information & model we build. Jumping to learn the user behavior & traffic pattern without this, it will not be value adding in performance engineering & tests.
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100 days of Skilled Testing retweeted
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Performance Testing - The Unsusual Ignorance in Practice & Culture
The performance is not just - how [why, when, what, where] fast or slow?
Sometimes serving the request by what you call as slow, is a performance.
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100 days of Skilled Testing retweeted
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Take snapshots before tests and revert the database to the snapshot afterward.
Seed the database with predefined data before tests for a consistent starting point.
Limit modifications to relevant data, avoiding unnecessary changes
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Test with diverse data, including extreme values and outliers.
100 days of Skilled Testing retweeted
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Protect sensitive information by masking or anonymizing data.
Use consistent seed data across environments and version-control it.
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I am not sure but here is what i know.
Data privacy regulations like GDPR shape test data management, requiring explicit consent, anonymization, data minimization, robust security, retention policies, and documentation for compliance.
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Identify and eliminate duplicate records to prevent redundancy.
Validate and rectify errors in data transformations.
Ensure uniformity in formats, units, and values across sources.
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Likewise, Data Virtualization supports dynamic layers, but it requires additional infrastructure and can be resource intensive.
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Similarly, if we take the case of data masking.
The pros are protecting sensitive information and preserves integrity. While cons are Complex implementations, potential performance overhead.
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For example, Test Data Generation Tools:
Pros maybe it is Automated and may be efficient for large datasets.
Cons of it are Initial setup, may require careful configuration.
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Protect sensitive information in test data and create smaller, representative subsets.
Store test data configurations in version control systems for tracking and management.
Establish schedules for regular test data refresh to maintain relevance.
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Behind the Every Test Data, There is a ?!
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What's the secret recipe? Just like a well-stocked pantry, keep your test data organized and accessible. Think of it as meal prepping for your testing cycles!
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Clearly communicate the purpose of the documentation and the specific areas where you are seeking feedback and the timelines.
Schedule regular meetings or checkpoints where team members can discuss the documentation and share feedback.





