Pester & PSScriptAnalyser - Power Test your PowerShell with PowerShell - Future Decoded 2017

Nov 2, 2017Download as PPTX, PDF1 like927 views

Everyone scripts in their own way. For example, do you put the '{' after the keyword, or on the next line? Is there help provided with your functions? Does a module manifest comply with your (company) standards? Let me show you how you can use Pester and PSScriptAnalyzer to help you write excellent scripts and help you and your colleagues evolve to deliver scripts that have one look-and-feel... without having to change the way YOU script.

Pester & PSScriptAnalyser - Power Test your PowerShell with PowerShell - Future Decoded 2017

Mark Nash
Graduate Engineer at
DevOpsGuys
Ed Dipple
Senior Engineer at
DevOpsGuys

$testFiles = Get-Item ".src*.Tests.ps1"
Invoke-Pester -Path $testFiles

Describing Dependency checking
Context Windows
[+] Should be able to add to the PATH variable 1.87s
Context Unix
[+] Should be able to add to the PATH variable 185ms
Context Mono is not installed
[+] Should throw exception if Mono not installed on Unix 186ms
[+] Should not throw exception if Mono not installed on Windows
125ms

Powershell Class
Create a fake
WebClient

Inline C# code
Import the FakeWebClient2 Class

# Call your function that uses the mock
1 -Scope It
}

$testFiles = Get-Item ".src*.tests.ps1"
Invoke-Pester -Path $testFiles
-OutputFormat NUnitXML
-OutputFile TestResults.xml

$testFiles = Get-Item ".src*.tests.ps1"
$sourceFiles = Get-Item ".src*.ps1" –Exclude *.Tests.ps1
Invoke-Pester -Path $testFiles
-CodeCoverage $sourceFiles
-CodeCoverageOutputFile coverage.xml

Unit Tests Functional Tests
• Test for expected output
values and exceptions
• Mock all commands that
change machine state
• Assert that mocks have
been called the
expected number of
times
• Test for actual state
change
• Minimal usage of Mock
objects
• Use TestDrive to isolate
state changes where
possible

Invoke-ScriptAnalyzer "./src"
Invoke-ScriptAnalyzer "./src" -Recurse

Describe 'PSScriptAnalyzer analysis' {
$ScriptAnalyzerResults = Invoke-ScriptAnalyzer
-Path "*.ps1"
-Severity Warning
It 'Should not return any violation' {
$ScriptAnalyzerResults | Should BeNullOrEmpty
}
}

https://github.com/MathieuBuisson/PowerShell-DevOps/

https://github.com/devopsguys/PSForge
https://github.com/devopsguys/FutureDecoded
https://blogs.technet.microsoft.com/heyscriptingguy/2015/12/16/unit-testing-
powershell-code-with-pester/
https://blogs.technet.microsoft.com/heyscriptingguy/2017/01/31/psscriptanaly
zer-deep-dive-part-1-of-4/

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Exceptions allow developers to handle error cases expected to occur infrequently. Ideally, good test suites should test both normal and exceptional behaviors to catch more bugs and avoid regressions. While current research analyzes exceptions that propagate to tests, it does not explore other exceptions that do not reach the tests. In this paper, we provide an empirical study to explore how frequently exceptional behaviors are tested in real-world systems. We consider both exceptions that propagate to tests and the ones that do not reach the tests. For this purpose, we run an instrumented version of test suites, monitor their execution, and collect information about the exceptions raised at runtime. We analyze the test suites of 25 Python systems, covering 5,372 executed methods, 17.9M calls, and 1.4M raised exceptions. We find that 21.4% of the executed methods do raise exceptions at runtime. In methods that raise exceptions, on the median, 1 in 10 calls exercise exceptional behaviors. Close to 80% of the methods that raise exceptions do so infrequently, but about 20% raise exceptions more frequently. Finally, we provide implications for researchers and practitioners. We suggest developing novel tools to support exercising exceptional behaviors and refactoring expensive try/except blocks. We also call attention to the fact that exception-raising behaviors are not necessarily “abnormal” or rare.

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This case study explores how we partnered with a mid-sized U.S. healthcare SaaS provider to help them scale from a successful pilot phase to supporting over 10,000 users—while meeting strict HIPAA compliance requirements. Faced with slow, manual testing cycles, frequent regression bugs, and looming audit risks, their growth was at risk. Their existing QA processes couldn’t keep up with the complexity of real-time biometric data handling, and earlier automation attempts had failed due to unreliable tools and fragmented workflows. We stepped in to deliver a full QA and DevOps transformation. Our team replaced their fragile legacy tests with Testim’s self-healing automation, integrated Postman and OWASP ZAP into Jenkins pipelines for continuous API and security validation, and leveraged AWS Device Farm for real-device, region-specific compliance testing. Custom deployment scripts gave them control over rollouts without relying on heavy CI/CD infrastructure. The result? Test cycle times were reduced from 3 days to just 8 hours, regression bugs dropped by 40%, and they passed their first HIPAA audit without issue—unlocking faster contract signings and enabling them to expand confidently. More than just a technical upgrade, this project embedded compliance into every phase of development, proving that SaaS providers in regulated industries can scale fast and stay secure.

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Not So Common Memory Leaks in Java WebinarTier1 app

This SlideShare presentation is from our May webinar, “Not So Common Memory Leaks & How to Fix Them?”, where we explored lesser-known memory leak patterns in Java applications. Unlike typical leaks, subtle issues such as thread local misuse, inner class references, uncached collections, and misbehaving frameworks often go undetected and gradually degrade performance. This deck provides in-depth insights into identifying these hidden leaks using advanced heap analysis and profiling techniques, along with real-world case studies and practical solutions. Ideal for developers and performance engineers aiming to deepen their understanding of Java memory management and improve application stability.

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What Do Contribution Guidelines Say About Software Testing? (MSR 2025)Andre Hora

Software testing plays a crucial role in the contribution process of open-source projects. For example, contributions introducing new features are expected to include tests, and contributions with tests are more likely to be accepted. Although most real-world projects require contributors to write tests, the specific testing practices communicated to contributors remain unclear. In this paper, we present an empirical study to understand better how software testing is approached in contribution guidelines. We analyze the guidelines of 200 Python and JavaScript open-source software projects. We find that 78% of the projects include some form of test documentation for contributors. Test documentation is located in multiple sources, including CONTRIBUTING files (58%), external documentation (24%), and README files (8%). Furthermore, test documentation commonly explains how to run tests (83.5%), but less often provides guidance on how to write tests (37%). It frequently covers unit tests (71%), but rarely addresses integration (20.5%) and end-to-end tests (15.5%). Other key testing aspects are also less frequently discussed: test coverage (25.5%) and mocking (9.5%). We conclude by discussing implications and future research.

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