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Last few years, I have been reading and writing a lot on sustainability with performance and resilience testing. I already published 6+ articles and presented in couple of international conferences on this intense topic.&nbsp;



Organizations are now getting matured after digital transformations and already aware of the facts that performance and resilience testing assists in sustainability in below 2 ways:



<ul><li>First, by making the application or websites faster (if application is faster, then it uses less data to serve the request and eventually it will generate less carbon to the environment)</li><li>Second, by optimizing the server resources (if server resources are optimized, then it consumes less energy which is better from sustainability perspective)&nbsp;</li></ul>



Organizations are also leveraging the benefits of cloud computing for cost savings as well as for sustainability. As a next step, organizations are now thinking about how they can uplift their performance testing process for better sustainability and less cost (in addition with ensuring enhanced end-user experience and confirming consistency).&nbsp;



In this blog, I will talk about my personal perspective on the matured performance and resilience testing for improved sustainability post-digital transformation along with continuous FinOps activities.



<div id="toc_container" class="no_bullets">Table Of Contents<ul class="toc_list"><li><a href="#While_Digital_Transformation">1 While Digital Transformation:</a></li><li><a href="#Post_Digital_Transformation">2 Post Digital Transformation:</a></li><li><a href="#Conclusion">3 Conclusion:</a></li></ul></div>
<h2 id="while-digital-transformation">While Digital Transformation:</h2>



In my earlier LinkedIn blog, I talked about organizations need to be very serious when conducting full-blown performance and resilience test executions for minimizing carbon footprint. <a href="https://www.linkedin.com/pulse/organizations-need-very-serious-when-conducting-full-blown-dutta" target="_blank" rel="noreferrer noopener nofollow">Source</a> 



Performance testing absolutely assists in reducing carbon footprint, however executing too many full-blown performance or resilience tests create higher carbon footprint. We all know automated testing is having better sustainability and performance testing is always automated. So, it is having less carbon footprint from that angle.&nbsp;



Then again, performance and resilience testing (load, stress, capacity planning, endurance, volume or game day testing or different kinds of resilience testing) is having a huge workload as it is driven by thousands of users if not millions. For the Internet of Things, there are millions of devices. This huge workload creates a higher carbon footprint.



So, while organizations are in the process of digital transformations and migrating towards cloud, full blown performance testing was necessary to ensure the as-is performance or improved performance for their end-users. Even organizations must think about the ongoing cloud cost (already talked about this in detail in EuroSTAR Huddle back in 2020: <a href="https://huddle.eurostarsoftwaretesting.com/applications-dont-run-fast-in-cloud-without-continuous-performance-testing/" target="_blank" rel="noreferrer noopener nofollow">Source.</a>



Continuous performance testing and monitoring is absolutely required for happy end-users, for cost savings and for sustainability. Even, continuous performance testing is beneficial for cloud cost savings (FinOps). Organizations are already leveraging different techniques, processes, or methodologies to make the improved application performance which assists in less carbon footprint.&nbsp;



Adopting optimized resource driven high-availability concepts, application containerization or different web performance optimization practices both on client side and server side etc. are carried out to ensure reduced carbon footprint.&nbsp;



Cloud is using green and clean energy. Organizations are also ensuring green energy for their data centres. Organizations can also think about sustainable capacity planning for reduced carbon footprint, published in Quality Matters magazine. <a href="https://www.quality-matters.org/index.php?page=qm-issues-archive&amp;issue=15" target="_blank" rel="noreferrer noopener nofollow">Source</a>



<h2 id="post-digital-transformation">Post Digital Transformation:</h2>



We all know the main objective of performance and resilience testing services is to efficiently use all the system resources and that directly impacts energy efficiency. While with digital transformations, organizations are fully focused on ensuring optimum performance for end-users and optimum utilizations (thus by both saving cost and ensuring sustainability).&nbsp;



Post digital transformations, organizations will be more interested in less carbon footprint for all their SDLC activities along with cost savings without compromising the quality attributes. As performance and resilience testing is having huge workload, it becomes priority for organizations to look closely on performance testing activities to reduce carbon footprint.



First, unnecessary, or unplanned performance and resiliency test execution should be strictly avoided. Second, conducting full-blown performance and resilience testing in very low scalable environment or hardware/software configuration mismatch with the live environment should be strictly escaped. Both activities assist not only in reducing carbon footprint but also save a lot of effort and costs.&nbsp;



Third, Organizations should not conduct full blown performance testing frequently rather impact-based performance testing should be conducted. Once in a while, full blown performance testing will be good enough post-digital transformations until added huge workload.



&nbsp;Impact based performance testing will be having less workload and component level, resulting in less carbon emission. By identifying and resolving issues at the early component level will not only be cost-effective but also reduce energy consumption for debugging and fixing defects.



Fourth, time duration of performance testing or steady state duration should be reduced to have less carbon footprint (30 mins from 1 Hour or 2 hour). Long duration endurance testing should not be conducted frequently. Even for those conducted endurance test, execution window should not be more than 4 hours from 8 or 16 or 24 hours. Endurance testing workload should be average load instead of peak load.



Fifth, when performance test duration and user workload is reduced, huge amount of performance test data preparation activities simply will not be required. That will also minimize huge carbon footprint.&nbsp;



Sixth, when user workload is reduced, corresponding load generator or injector machine counts will also be reduced. Goal should be achieving more users with few resources. This again lowers the carbon emission.&nbsp; We should try to use cloud load generators, if possible, for better sustainability. Even efficient management of performance test environment (Pre-prod/Stage/Performance) and performance test lab environment (Controllers/Load Generators) are absolutely required for better sustainability.



Overall, impact-based, low volume, short duration, specific goal-oriented performance, and resiliency testing will be very helpful.&nbsp;



Seventh, conducting the performance and resilience testing at off-hours will lead to less carbon footprint. Even green testing techniques like optimizing the performance test scenarios will assist in reducing carbon footprint.



Eighth, on-premise organizations can use the existing hardware for a longer duration (extending the hardware lifespan for hardware efficiency) to reduce less carbon footprint and have less e-waste. Even try to evaluate the power consumption of background applications on mobile, devices and servers and, if possible, minimize it for better sustainability.



Ninth, organizations should also continuously look to reduce the amount of data transfer and distance that they cover (like using Content Delivery Work).



Tenth, using the emulator instead of real devices will be having less carbon footprint. In case of real devices, device lifecycles must be increased. For example, a device which is replaced in 3 years &#8211; try to extend it to 5 years. However, proper support from device organizations is also required so that device is effective in this changing technological world. Optimized device battery usage should be another way of sustainability.



In a nutshell, all the above reduce the costs and provide better sustainability. which every organization should be interested in, post digital transformation.



<h2 id="h-conclusion">Conclusion:</h2>



Post digital transformation, organizations should be conducting performance testing in test early, test only when required instead of test always or test often fashion. Continuous impact-based low volume, shorter-duration <a href="https://testsigma.com/blog/performance-testing/" target="_blank" rel="noreferrer noopener">performance testing</a> rather than always on, full volume, longer duration performance testing should be priority for better sustainability.

Performance & Resilience Testing for Improved Sustainability Post Digital Transformation

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In the ever-evolving tech landscape, Artificial Intelligence (AI) stands out as the next frontier, promising transformative advancements in various domains. From intelligent automation to predictive analytics, AI has the potential to reshape the way we live and work. Harnessing the power of AI not only opens doors to innovation but also presents the need for testing AI applications to ensure its reliability and effectiveness.&nbsp;



In this blog, we&#8217;ll explore the test strategy for AI, challenges, and best practices to ensure the seamless integration and optimal performance of AI-powered systems.



Embracing AI is not just a technological leap but a journey that demands a robust testing framework to unlock its full potential.



Read here &#8211; <a href="https://testsigma.com/blog/is-ai-really-important-in-software-test-automation/" target="_blank" rel="noreferrer noopener">AI Testing</a>



<div id="toc_container" class="no_bullets">Table Of Contents<ul class="toc_list"><li><a href="#Understanding_the_AI_Landscape">1 Understanding the AI Landscape</a><ul><li><a href="#The_Rise_of_AI">1.1 The Rise of AI</a></li></ul></li><li><a href="#Challenges_in_Testing_AI_Systems">2 Challenges in Testing AI Systems</a></li><li><a href="#Strategies_for_Testing_AI_Applications">3 Strategies for Testing AI Applications</a></li><li><a href="#Best_Practices_for_Testing_AI_Applications">4 Best Practices for Testing AI Applications</a></li><li><a href="#How_to_Test_AI_Apps">5 How to Test AI Apps?</a></li><li><a href="#Tips_for_AI_Apps_Testing">6 Tips for AI Apps Testing</a></li><li><a href="#Conclusion">7 Conclusion</a></li></ul></div>
<h2 id="h-understanding-the-ai-landscape">Understanding the AI Landscape</h2>



<h3 id="h-the-rise-of-ai">The Rise of AI</h3>



Artificial Intelligence has emerged as a driving force behind numerous technological advancements. From machine learning algorithms to natural language processing, AI systems are becoming integral to applications ranging from healthcare to finance and beyond. As organizations increasingly incorporate AI into their products and services, the importance of rigorous testing cannot be overstated. For insights into advanced AI applications, explore a detailed guide on <a href="https://www.revechat.com/blog/generative-ai-chatbot/" target="_blank" rel="noreferrer noopener">generative AI chatbot</a> solutions and their impact on modern customer interactions.



Read here &#8211; <a href="https://testsigma.com/blog/ai-and-ml-in-software-testing/" target="_blank" rel="noreferrer noopener">AI and ML in Software Testing</a>



<h2 id="h-challenges-in-testing-ai-systems">Challenges in Testing AI Systems</h2>



Testing AI systems poses unique challenges compared to traditional software testing. One major challenge is the complexity of AI algorithms, which often involve intricate neural networks and deep learning models. Ensuring the accuracy, reliability, and ethical considerations of these systems requires a specialized approach that goes beyond conventional testing methodologies.



<h2 id="h-strategies-for-testing-ai-applications">Strategies for Testing AI Applications</h2>



Data Quality and Bias Testing:



AI models heavily depend on the quality and diversity of training data. Testing must include checks for biased data and the potential reinforcement of societal prejudices. A comprehensive strategy involves identifying and rectifying bias in training datasets to ensure fair and ethical AI.



Performance Testing:



The performance of an AI system is crucial for its effectiveness. Performance testing involves evaluating how well the system handles varying workloads, data volumes, and concurrent users. This ensures optimal responsiveness and resource utilization.



Explainability and Transparency Testing:



AI decisions can sometimes seem like a &#8220;black box,&#8221; making it challenging to understand the reasoning behind specific outcomes. Testing for explainability and transparency involves validating that AI models can provide comprehensible explanations for their decisions, crucial for regulatory compliance and user trust.



Security Testing:



AI systems are susceptible to adversarial attacks. Security testing is essential to identify and address vulnerabilities that could compromise the confidentiality and integrity of the AI model, ensuring robust protection against potential threats.



As per the above strategies we should not forget that AI application testing will also follow something we all are aware of which is the <a href="https://testsigma.com/guides/stlc-in-software-testing/" target="_blank" rel="noreferrer noopener">testing lifecycle</a> and this is vital because it will mean that there is a process to follow just like testers follow in their day to day testing.&nbsp;







<h2 id="h-best-practices-for-testing-ai-applications">Best Practices for Testing AI Applications</h2>



Comprehensive Test Coverage:



Encompass various testing types, including unit testing, integration testing, and end-to-end testing, to provide comprehensive coverage. This ensures that every aspect of the AI system, from individual components to the integrated solution, is thoroughly tested.



<a href="https://testsigma.com/continuous-testing">Continuous Testing</a>:



Implement continuous testing practices to integrate testing seamlessly into the AI development pipeline. This enables quick feedback on changes, accelerates the development lifecycle, and enhances the overall quality of the AI system.



Collaboration Across Teams:



Promote collaboration between data scientists, developers, and testers. Effective communication ensures a shared understanding of requirements, objectives, and potential challenges, fostering a collaborative testing environment.



Adopting <a href="https://testsigma.com/blog/is-ai-really-important-in-software-test-automation/" target="_blank" rel="noreferrer noopener">AI in Software Testing</a>:



Leverage AI in test automation to enhance efficiency and accuracy. <a href="https://testsigma.com/tools/ai-testing-tools/" target="_blank" rel="noreferrer noopener">AI testing tools</a>, like Testsigma, can intelligently identify test scenarios, predict potential issues, and autonomously execute repetitive testing tasks, allowing testers to focus on more complex aspects.



Apart from the above, there are more best practices such as :



<ul><li>Clearly define the objectives, scope, and success criteria of your AI application testing efforts. Determine the specific aspects of the AI system to be tested, such as accuracy, performance, robustness, and fairness.</li><li>Ensure high-quality training data is used to train the AI models. The training data should be representative, diverse, and cover various scenarios to promote accurate and unbiased learning.</li><li>Establish baseline metrics or results to compare the AI system&#8217;s performance against known benchmarks.</li><li>Leverage <a href="https://testsigma.com/data-driven-testing">data-driven testing</a> to achieve higher efficiency. AI-powered software testing tools are also helpful these days. If you decide to go with tools that support such testing, GenAI-powered testing tools, like <a href="https://testsigma.com/">Testsigma</a>, there are many popular names out there that support data-driven testing in many popular data formats to help you test AI-based systems better.&nbsp;</li></ul>



<h2 id="h-how-to-test-ai-apps">How to Test AI Apps?</h2>



Even though we are hearing all about the rise of AI and Generative AI, one thing to not forget is that AI is helping us all test better and save testers a lot of time, however we need to factor in extra time for testing manually too as exploratory testing has shown many benefits as It helps review the quality of a product from a user perspective. Furthermore, AI cannot tell us about the look and feel of a product, so we cannot fully rely on AI.&nbsp;



Testing AI systems comes with some challenges, and that means there is a need for some special techniques:&nbsp;



<ul><li>The AI-based system test results can be Stochastic which means they generate different results for the same parameters&nbsp;</li><li>Stochastic results don&#8217;t mean the results are wrong, it means that we need to verify more details</li><li>Also, there can be human bias when training the model/data which needs to be eliminated during AI application testing</li><li>AI performs best when given advanced input models. If the inputs are not of high quality, the issues become more complicated, requiring a significant amount of time and effort to address.</li><li>AI is an intricate system, and even small defects are magnified significantly, also it&#8217;s not necessary that all results that don&#8217;t match are an anomaly. This problem becomes more challenging to resolve as well. However, with the help of the developers testers and developers can both crack the issue and find the solution. I have experienced this in the past with data science models and it can be daunting, but what is magic is &#8220;Pairing&#8221;, when we pair with the developers who build the models, we can not only learn from them the intricacies but also test better.</li></ul>



Read here &#8211; <a href="https://testsigma.com/blog/generative-ai-in-software-testing/" target="_blank" rel="noreferrer noopener">Generative AI in Software Testing </a>



<h2 id="h-tips-for-ai-apps-testing">Tips for AI Apps Testing</h2>



<ol><li>Check the input data and verify the data set used for the model</li><li>Check the SHAPE of your data, testing the outputs will explain if the values are of the right type and the data columns are matching</li><li>Have some bounds around averages&nbsp;</li><li>Set some target result thresholds of 3-5%</li><li>Make sure no data dropped, no duplicates</li><li>Testing different parameters and trying to see the difference in results exploration of a model</li><li>As the model can provide random results each time you run – brace the uncertainty, try and understand if it&#8217;s still OK or a complete anomaly&nbsp;</li><li><a href="https://testsigma.com/blog/exploratory-testing/" target="_blank" rel="noreferrer noopener">Exploratory test </a>the model(s), and it should help understand the system better and you&nbsp;</li></ol>



<h2 id="h-conclusion">Conclusion</h2>



As AI continues to revolutionize industries, the role of testing becomes increasingly vital. By implementing robust testing strategies and embracing best practices, organizations can unleash the full potential of AI while ensuring its reliability, security, and ethical use.&nbsp;



The journey into the era of AI is an exciting one, and with meticulous testing, we can navigate this frontier with confidence, unlocking unprecedented opportunities for innovation and advancement. Embrace the power of AI, test diligently, and pave the way for a future where intelligent systems redefine what&#8217;s possible.

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