4 Key Focus Areas for Effective Performance Engineering

In this webcast we’ll travel along the path of the development and operation of “PAD” system,
supported by DevOps practices and infrastructure.

This solution tracks the fueling charge of planes and helicopters by the principal oil company,
in all the Argentinian airports.

It’s composed of rugged tablets (front-end) running off-line (on-line when it’s possible), against
a cloud backend, all over the country.

The keys scenarios that our solution cover is:
- Extensive on-field surveys and device integrations
- Gas fueling to planes before international flights
- Flowmeters interconnection
- Corporate identity management
- Assure a 24x7 operation, 99.9% SLA

DevOps is driving incredible results for many companies, but many more are struggling and every team has lots of room to improve. Recent focus on the bigger picture of value streams and flow provides us a much better perspective and model for improvement. In this talk, I'll share some of what's working for leaders, and how you can see improvement with a little investment in the right areas.

I'll share:
- How to tackle the worst bottlenecks
- Where automation really matters
- Remote-friendly mapping for teams
- Strategies for driving alignment
- How to get metrics from mapping

The role of the IT department has changed in recent years and is more aligned with the business than ever. In 2020, as DevOps matures, what does this mean for the role of DevOps and Agile in delivering business outcomes?

Join this panel as DevOps experts explore what opportunities - and challenges - DevOps and Agile can create for the business. Points of discussion will include:

- Are businesses investing enough in DevOps and IT teams to enable them to remain competitive in a landscape of accelerated Digital Transformation?
- How can businesses ensure that they reap the rewards of investments they do make?
- If businesses do it right, what benefits can they expect?

As we look to momentous changes unleashed by technological advances like 5G, AI, Deep Learning/Machine Learning, AI Accelerator Chips, etc., supported by social changes leading to 9-to-5 jobs taking newer meanings with WFH with higher autonomy for business users. DevOps provides an opportunity for enterprises leapfrog to Industry 4.0 by leveraging their internal data, social media data and finding effective ways to analyze, learn, and inferencing the data from the millions of edge devices to give business users the power to take autonomous decisions within real-time and meet the regulatory and risk management requirements.

Traditional App Dev models that needed elaborate software lifecycle and testing, and an extended roll-out plan, will pose severe challenges in Industry 4.0, and it requires real-time updates rolled out to all users. The traditional models that relied on IT development, Implementation and Processes are now blurred as business users rely more on AI to perform many of the tasks being performed by business process organizations.

To achieve Industry 4.0, enterprises need to critically relook at their internal processes and work towards leveraging AI for many of the tasks that are being done manually today, and then push the inferencing from these AI tools to the end business users. Enterprises would need to rapidly push these inferences from AI tools to the end-users and at the same time keep constantly updating their learning, analysis and inferences on real-time basis from the millions of data generators from devices, users, and social media to ensure that the users always have most current AI inference on their devices. All these updates, including Cloud based machine learning, analysis and AI inferencing and all the business users devices (like mobile, laptops, tablets, AR/VR, wearables, etc., ) need to happen in real-time.

“No Time to Test” – an approach to starting the testing journey with people who think module testing is all you have to do.



You are called in by a development manager who is suffering because of crashes in the system causing outages at critical times and significant revenue losses. They know they have to do something about testing but doesn’t know how to get started or obtain buy in. The situation is that the system is huge and complex. To properly scope out a full functional and regression test plan might take years. At the same time the developers are not interested, providing the blame cannot be pinned on them.



The point of this session is to discuss how to present a strategy that will allow you to make a realistic start without promising the undeliverable.

The search for more efficient, quicker and cheaper testing has been ongoing for a long time. In the past test automation was frequently presented as the solution to this problem. These days it has become at least a small part of any modern software testing process. However, in the next four years the test automation market will grow 2.3 fold to over 25 billion USD.
But it’s not only the size of test automation that is growing, it’s also increasing in maturity. It is now better that ever before as the tools are getting to grow more advanced and the practices more mature.
We will share with you how test automation market is changing, what are the current hot trends, and what new opportunities these will bring to us. Some of the most interesting innovations relate to improving scope selection, execution and maintenance of the automated test pack, powered by AI and ML.
This talk will give a high-level, understandable introduction to what’s happening and is about to happen in test automation. The target audience is testers, developers and technical resources.

Over the last 15 years, organizations have had difficulty with the entire software delivery process, but two artifacts in particular became a recurring problem. The problem stems from the Goldilocks principle: one of these artifacts is much too big to overcome efficiently, and one of them is much too small to make a significant impact, so we need to find the one that is just right.

So what artifact is “just right?” As the user, what we really care about are the Features: the stuff we use and interact with all the time to make our daily lives better. However, the systems we have today haven't advanced to the point where software features are the nearest proxy for customer value. In other words, the feature must be at the forefront of the UI.

In this talk, I will share why features are the proxy for value and explore the different levels of abstraction for the “just right” aspect of a Feature in order to shift people's mindset from thinking in terms of Builds or Releases, to thinking in terms of the customer and business value. I will share tactics to address the Goldilocks problem and how to have these discussions at the right level of the software in order to make prioritizations, decisions, and discoveries.

What Attendees Will Learn:

A different perspective about software delivery, which will help them make better decisions about which features to pursue and builds/releases to postpone.

Learn why features are the future and how they are the perfect middle-ground between individual builds and major releases.

How to ultimately break free from the Goldilocks problem of software delivery.

Artificial Intelligence (AI) techniques are enjoying rapid adoption by all sizes of businesses across the world. However, evolving AI from
“Prototyping” activities to “Production” face many challenges, in large part due to the radically different development and deployment profile of
modern Machine Learning (ML) methods, and the range of practical issues that come with real-world use cases.

This webinar will identify the challenges and demonstrate how these challenges could be mitigated with a remarkably scalable, flexible and
extensible ML platform.

Have you or your organisation tried to implement test automation and failed in doing so? Don’t be ashamed, you are far from being alone! The sad truth of the matter is most test automation initiatives fail!

Attend this webinar and learn how to finally achieve the goal of fully functional test automation and reap the many benefits and rewards this will provide.

About this Webinar
2020 Has forced many organisations to revisit the way they do things and seek out ways to gain efficiencies and lower costs. While many development teams are turning to Agile methodologies and DevOps strategies in order to achieve better quality software and faster production releases, in many cases testing and quality assurance is proving to be a bottleneck. Automation is the only way to allow these new development strategies to achieve their full potential. This webinar will be of interest to anyone seeking to understand how to finally eliminate the roadblocks that have hindered so many others in the past from making test automation simply work!

Join us as we take you through the reasons why test automation fails and equip you with the tools and understanding to put you finally on your way to success!

Companies are moving to security automation to protect their software and assets. In this presentation PwC and Uleska will discuss the trend towards DevOps and Digital Transformation, and the risks that introduces for companies. Learn about the current problems with DevOps Security, scalability, and effective regulatory coverage through DevSecOps.

Key takeaways will include:
• How to scale DevSecOps
• How to move from measuring inherent risk with software, to residual risk, automatically, and at scale
• What are the current best practices we see in the industry, and what are the blockers
• What will the future of security automation look like

DevSecOps extends DevOps, in that everyone in the software development life cycle is responsible
for security bringing operations and development together with security functions. DevSecOps
embeds security in every part of the development and operations processes. It is about automating
core security tasks by embedding security controls and processes early in the DevOps workflow
(rather than being bolted on at the end). For example, this could be the case when migrating to
microservices, building out a CI/CD pipeline, compliance automation or simply testing cloud
infrastructure. It is about getting security back into the lifecycle, or as it has been described: ‘shifting security left’.

Integrating security into DevOps to deliver DevSecOps requires new mindsets, processes, and tools.
Starting with continual Risk Assessment, Security and Risk Management leaders need to adhere to
the collaborative, agile nature of DevOps to be seamless and transparent in the development
process, making security as silent, encompassing, and seamless as possible.

How to get a business to approach built-in security, from the beginning and continually:
1. By establishing a security and data privacy strategy – a Security Program spanning people, process, products with Risk Assessment and Policies.
2. Integrating Data Protection, Secure Coding and Testing practices within the engineering
lifecycle.
3. Leveraging automation to integrate security into the DevOps & CI/CD pipeline – from code scanners through to continuous security configuration management and remediation – in Dev, Testing, Staging, Production environments.
4. Finally, staying abreast of practices, regulations, auditing and remaining certified, in true intent and spirit.

Join Prolifics Testing to hear how their Test Automation journey for clients has entered a new phase: an AI-driven, containerized Test Automation platform with inbuilt self-healing scripts - best of all it’s based on Open Source components and free!

If you are interested in “Having it all” and bringing together your strategy for web, desktop, mobile and API Test Automation for Functional, Performance and Security Testing, join this session to hear Faz Ahmad show you the way.

Test (execution) automation has been a goal since the earliest programs were written. The mechanics of automated tests have evolved with the technology used to build software, but the fundamental problems of test automation have not changed. Establishing a consistent environment, creating integral and re-usable test data, handling genuine failures, false negatives (and positives), tear-down, and clean-up. These are well-understood challenges. Developers and testers battle with flaky environments, test frameworks, and buggy software under test much the same way they always did. There is little debate about these technical or logistical matters.

The use of unit test frameworks to test low-level components and integration is well-understood and usually most effective. But where the user interface is graphical and/or where tests of larger, integrated systems are required, test automation is more challenging. These tests tend to be longer, slower, and more complex and consequently, they are harder to write, debug, and maintain. These tests also run relatively slowly. All in all, long-winded, complex tests are flaky and far less efficient and economic. Two models dominate people’s thinking in this area – the four-quadrant model and the test automation pyramid. They have some value, but practitioners and managers need something better to guide their thinking.

This is the “state of automation” and has been for many years.

In this talk, Paul sets out a way of thinking about testing and test automation that helps to answer the strategic questions: What does test automation really do for us? When and how is automation the right choice? How do we justify automation? Can automation replace testers? What new tools and skills do we need to implement automation in the future?

The need for rapid deployment of scalable clusters is creating an opportunity for container solutions such as Kubernetes. But what are the implications of multiple Kubernetes clusters in a broad deployment? What happens if a cluster goes down? What’s the impact on business resiliency? Managing and securing multiple clusters is becoming a key topic and area of debate. Multi-cluster Kubernetes that provides robustness & resilience is rapidly moving from “best practice” to a “must have”.

In this webcast we’ll dive into:
•The history of multi-cluster Kubernetes
•How multi-cluster setups could affect data heavy workloads (such as multiple microservices backed by independent data stores)
•Managing multiple clusters
•Keeping the business functioning if a cluster goes down
•How to prepare for the coming datapocalypse

After you watch the webcast, check out the Q&A blog at: https://bit.ly/2DE91fL

Cloud native technologies, like containers and Kubernetes, enable enterprise agility at scale and without compromises. In this session, Jim Bugwadia, who is a founder and the CEO at Nirmata - the Kubernetes Management Plane for Enterprises, will show how enterprises can warp speed their DevOps initiatives by embracing cloud native technologies. You will learn:

- How containers and Kubernetes impact enterprise DevOps
- What organizational patterns drive successful Kubernetes adoption
- How to approach hybrid and multi-cloud strategies with Kubernetes

Have you automated your entire CI/CD pipeline except for the manual DBA actions? Are untested SQL scripts your main source of deployment issues? You aren't alone. Many shops that embrace DevOps principles realize improvements in almost all areas... except for databases. In this talk, we will explore both methods for bringing Oracle DBA teams into a DevOps mindset, and practices for leveraging the benefits of DevOps automation for Oracle database operations.

Some analysts say that DevOps is starting to reach maturity, and as such there are numerous tools flooding the market. But all good tools only reap true benefits if there is a solid strategy to their use and implementation.

Join this webinar as we take a closer look at how solid DevOps strategies can support emerging tools and foster transformation in mission-critical software environments.

Topics will include:

- Why it’s crucial to build a culture of continuous, collaborative improvement and evolution between dev and ops
- What metrics and approaches can help to forecast the value of DevOps accurately
- Foundational success factors, including management buy-in, the maturity of current processes, and CI/CD tools

Moderator:
- Jeanne Morain - Founder, iSpeak Cloud

Panellists:
- Graham Berry - EMEA Sales Lead - OpenShift, RedHat
- Mark Berry - DevOps Director, 6point6
- TJ Gonen - Head of Cloud Security Programs, Check Point

Have you ever had challenges with unsuitable test data? Have you lost time each cycle due to tracing back data questions to key team members?
Is your data structure so complicated and under-documented that you struggle to know where to even start?

Whether you are contributing to a project using the latest technology trends or an antiquated mainframe system running on punch cards,
having a robust Test Data Management Strategy in place is crucial to the timely and smooth execution of your testing effort.

Join tap|QA's Akila Kavo, Delivery Manager of Emerging Technology, as we’ll explore best practices and real world examples in Test Data Management with a keen focus on how to integrate with your test automation solution.

We’ll dive into the following topics:

1) Why TDM is so important
2) What questions to ask to properly identify your problems
3) Strategies in creating the best TDM environment possible honed from multiple consulting engagements!
4) A technical demonstration of different approaches

Attendees will learn what questions to ask to get to the root source of data management woes. We’ll focus on how to solution a Test Data Management strategy, and plan its implementation.

You’ll learn what metrics to capture and how you can use them to quantify the value you are providing to the business.

Many testing teams today use Selenium for their automation needs and rightfully so, Selenium allows for cross browser and mobile support, it’s free and has a large open source community behind it.

Regardless of its feature set, Selenium and test automation in general has its own list of pain points like test maintenance and test coverage.
These pain points can have a large impact on testing metrics depending on the test architecture used and the design patterns being followed.

To address these pain points a team can utilize areas of Artificial Intelligence (AI) and Machine Learning (ML) via open source libraries.
This enables the tests to find their own elements and attempt every possible combination of test cases, which a tester could then store, rerun, and iterate on.
This approach effectively gives Selenium a brain in the form of models and actions.

Join Mike Wagner, Test Architect and Principal Consultant for tap|QA as he leads this informative session for teams interested in integrating Artificial Intelligence into their existing Selenium framework.

Takeaways: Understanding of the different areas of Machine Learning/AI with an emphasis on:
* Convolutional Neural Networks (How they apply to element selection)
* Reinforcement Learning (How it applies to test coverage)
Approach for implementing Convolutional Neural Networks and Reinforcement Learning into an existing Selenium framework.
* Introduction to top open source libraries related to Machine Learning