During the Code Beam Lite conference you might have had questions that did not get answered, this is your opportunity to get them answered by our expert panel group

During the conference you might have had questions that did not get answered, this is your opportunity to get them answered by our expert panel group

Hike is a mobile-first, messaging platform that is used by 100 million users to exchange 40 billion messages/month. Hike app is available on Android, iOS and Windows phone. On the back-end, we’ve 100+ macro-services in Java, Python, Ruby, Go and Elixir. While setting up a Continuous Delivery pipeline, we ran into a series of technical challenges. However it was more important to address the organisational/behavioural challenges to ensure a sustainable culture shift in the company.

In this talk, I’ll explain how we went about:

• Setup a trunk-based development model
• Decentralised our build & test environments using Docker and Jenkins
• Segregated and containerised our macro-services
• Refactored the mobile apps to be more container friendly
• Setup a mobile device farm using STF
• Improved the quality of code-reviews using PRBuilder & PRRiskAdvisor
• Created different kinds of automated tests to align with our CI Pipeline and get rapid feedback
• Finally how we used C3 to visualise the health of our code-base

There has been a lot of concerns about the black-box nature of AI. People have been asking for a sensible AI guideline with the weight of Law behind it. In April 2019, the EU released it's Ethics guidelines for trustworthy AI. Before that during the Obama administration, the National Science and Technology Council came up with its own set of broad guidelines called "Preparing for the Future of Artificial Intelligence."

Most of these cover an impressive amount of ground in several major categories:

• Transparency: Any time an AI system makes decisions on a user's behalf, that person should be aware of it. The reasoning behind decisions should be easily explainable.
• Safety: AI systems should be designed to withstand attempted hijacking and other attacks performed by hackers.
• Fairness: Decisions made by AI systems should not be influenced by gender, race or other personal identifiers. They should be as impartial as possible and not reflect human biases.
• Environmental stewardship: Not all the stakeholders in AI development are human. The development of these platforms and the implications of their decision-making and sustainability should take into account the needs of the larger environment and other forms of life.
• And so on...

At this conference, we would like to bring our experts together to hear their views/concerns on this topic.

Welcome to Agile India 2019

A resilient organizational can not only adapt and respond to incremental change but more importantly, can respond to sudden disruptions and also, be the source of disruption in order to prosper and flourish.

The traditional risk management approach focuses too much on defensive (stopping bad things happen) thinking versus a more progressive (making good things happen) thinking. Being defensive requires consistency across the organization and this is where methodologies like Plan-Do-Check-Act (PDCA) come in. However, PDCA approach does not bake in the required progressive thinking and flexibility required for a fast company organization which operates in a volatile environment.

Professor David Denyer of Cranfield University has recently published a very interesting research report on Organizational Resilience. He has identified the following four quadrants across to help us think about organizational resilience:

• preventative control (defensive consistency)
• mindful action (defensive flexibility)
• performance optimization (progressive consistency)
• adaptive innovation (progressive flexibility)

In this talk, I'll share my personal experience of using this thinking to help an organization to scale their product to Millions of users. I've dive deep into how we structured our organization for Structural Agility and how we set-up a very lightweight governance model using OKRs to drive the necessary flexible and progressive thinking.

Looking to move to Continuous Delivery? Worried about the quality of your the code? Helping your developers understand clean-code practices and getting the right testing strategy in place can take a while. What should you do to control the quality of the incoming code till then? This talk shares our experience of using PRRiskAdvisor to gradually educate and influence developers to write better code and also help the code reviewer to be more effective at their reviews.

Every time a developer raises a pull-request, PRRiskAdvisor analyzes the files that were changed and publishes a report on the pull request itself with the overall risk associated with this pull request and also risk associated with each file. It also runs static code analysis using SonarQube and publishes the configured violations as comments on the pull request. This way the reviewer just has to look at the pull request to get a decent idea of what it means to review this pull request. If there are too many violations, then PRRiskAdvisor can also automatically reject the pull request.

By doing this, we saw our developers starting paying more attention to clean code practices and hence the overall quality of the incoming code improved, while we worked on putting the right engineering practices and testing strategy in place.

On Agile teams, collaboration is the way of life. Our leaders want their team members to work closely with each other, have shared goals and even think as one entity. Why? Because we believe that collaboration leads to happier, more productive teams that can build innovative products/services.

It's strange that companies use the word collaboration very tightly with innovation. Collaboration is based on consensus building, which rarely leads to visionary or revolutionary products/services. Innovative/disruptive concepts require people to independently test out divergent ideas without getting caught up in collaborative boardroom meetings.

In this presentation, Naresh Jain explores the scary, unspoken side of collaboration and explains in what context, collaboration can be extremely important; and when it can get in the way or be a total waste of time.

Through the conference, we would have heard different speaker's perspective and experience with Data Science and AI. In this closing panel, we want to step back and look at any unanswered questions that the audience would have.

Looking to move to Continuous Delivery? Worried about the quality of your the code? Helping your developers understand clean-code practices and getting the right testing strategy in place can take a while. What should you do to control the quality of the incoming code till then? This talk shares our experience of using PRRiskAdvisor to gradually educate and influence developers to write better code and also help the code reviewer to be more effective at their reviews.

Every time a developer raises a pull-request, PRRiskAdvisor analyzes the files that were changed and publishes a report on the pull request itself with the overall risk associated with this pull request and also risk associated with each file. It also runs static code analysis using SonarQube and publishes the configured violations as comments on the pull request. This way the reviewer just has to look at the pull request to get a decent idea of what it means to review this pull request. If there are too many violations, then PRRiskAdvisor can also automatically reject the pull request.

By doing this, we saw our developers starting paying more attention to clean code practices and hence the overall quality of the incoming code improved, while we worked on putting the right engineering practices and testing strategy in place.

Looking to move to Continuous Delivery? Worried about the quality of your the code? Helping your developers understand clean-code practices and getting the right testing strategy in place can take a while. What should you do to control the quality of the incoming code till then? This talk shares our experience of using PRRiskAdvisor to gradually educate and influence developers to write better code and also help the code reviewer to be more effective at their reviews.

Every time a developer raises a pull-request, PRRiskAdvisor analyzes the files that were changed and publishes a report on the pull request itself with the overall risk associated with this pull request and also risk associated with each file. It also runs static code analysis using SonarQube and publishes the configured violations as comments on the pull request. This way the reviewer just has to look at the pull request to get a decent idea of what it means to review this pull request. If there are too many violations, then PRRiskAdvisor can also automatically reject the pull request.

By doing this, we saw our developers starting paying more attention to clean code practices and hence the overall quality of the incoming code improved, while we worked on putting the right engineering practices and testing strategy in place.

By working with some of the most successful tech-product companies, I realised that code is NOT an asset, it's a liability. We should strive hard to minimise code. In 2011, when I started to hack on ConfEngine, I questioned my belief in TDD. I had also started playing around with APL style Array-Programming and Functional Programming. I felt, may be, I was getting a bit too dogmatic about TDD and automated tests in-general. As a thought experiment, I decided to build ConfEngine without ANY automated test. At first, it was really scary not to have the safety-net of automated test (something I took for granted for almost a decade.)

As I scaled ConfEngine without any automated tests, I had certain interesting realisations:

• How to embrace Simplicity and Minimalism WITHOUT automated tests
• Why Throwing Away Code frequently helps you achieve a better decoupled-design and how this helps in better experimentation
• Fear of Refactoring WITHOUT Tests is over-rated (Good IDE and safe-refactoring techniques can take you a long way)

ConfEngine grew from a pet-project to a 8 member product team. It has over 60K users and has done financial transactions worth over half-million USD. And we continue to push forward without ANY automated tests. Its not perfect, but it has certainly helped me challenge my dogma around TDD.

Background: In 2001, I stumbled upon the Test Infected paper. For the next 2 years, I struggled to really apply the test-first concept on real projects. Finally in 2003, I felt that I had fully internalised TDD and was able to apply on almost all projects. Then I started playing around with FIT and FitNesse, using ATDD on some of the projects. In 2006 I published "Avatars of TDD" paper in which I explained various styles of TDD and its design implications. Until 2011, I was a very big advocate of TDD, ATDD and BDD. I still like those practices, however I would not recommend it in all projects.

By working with some of the most successful tech-product companies, I realised that code is NOT an asset, it's a liability. We should strive hard to minimise code. In 2011, when I started to hack on ConfEngine, I questioned my belief in TDD. I had also started playing around with APL style Array-Programming and Functional Programming. I felt, may be, I was getting a bit too dogmatic about TDD and automated tests in-general. As a thought experiment, I decided to build ConfEngine without ANY automated test. At first, it was really scary not to have the safety-net of automated test (something I took for granted for almost a decade.)

As I scaled ConfEngine without any automated tests, I had certain interesting realisations:

• How to embrace Simplicity and Minimalism WITHOUT automated tests
• Why Throwing Away Code frequently helps you achieve a better decoupled-design and how this helps in better experimentation
• Fear of Refactoring WITHOUT Tests is over-rated (Good IDE and safe-refactoring techniques can take you a long way)

ConfEngine grew from a pet-project to a 8 member product team. It has over 60K users and has done financial transactions worth over half-million USD. And we continue to push forward without ANY automated tests. Its not perfect, but it has certainly helped me challenge my dogma around TDD.

Background: In 2001, I stumbled upon the Test Infected paper. For the next 2 years, I struggled to really apply the test-first concept on real projects. Finally in 2003, I felt that I had fully internalised TDD and was able to apply on almost all projects. Then I started playing around with FIT and FitNesse, using ATDD on some of the projects. In 2006 I published "Avatars of TDD" paper in which I explained various styles of TDD and its design implications. Until 2011, I was a very big advocate of TDD, ATDD and BDD. I still like those practices, however I would not recommend it in all projects.

A resilient organizational can not only adapt and respond to incremental change but more importantly, can respond to sudden disruptions and also, be the source of disruption in order to prosper and flourish.

The traditional risk management approach focuses too much on defensive (stopping bad things happen) thinking versus a more progressive (making good things happen) thinking. Being defensive requires consistency across the organization and this is where methodologies like Plan-Do-Check-Act (PDCA) come in. However, PDCA approach does not bake in the required progressive thinking and flexibility required for a fast company organization which operates in a volatile environment.

Professor David Denyer of Cranfield University has recently published a very interesting research report on Organizational Resilience. He has identified the following four quadrants across to help us think about organizational resilience:

• preventative control (defensive consistency)
• mindful action (defensive flexibility)
• performance optimization (progressive consistency)
• adaptive innovation (progressive flexibility)

In this talk, I'll share my personal experience of using this thinking to help an organization to scale their product to Millions of users. I've dive deep into how we structured our organization for Structural Agility and how we set-up a very lightweight governance model using OKRs to drive the necessary flexible and progressive thinking.

A resilient organizational can not only adapt and respond to incremental change but more importantly, can respond to sudden disruptions and also, be the source of disruption in order to prosper and flourish.

The traditional risk management approach focuses too much on defensive (stopping bad things happen) thinking versus a more progressive (making good things happen) thinking. Being defensive requires consistency across the organization and this is where methodologies like Plan-Do-Check-Act (PDCA) come in. However, PDCA approach does not bake in the required progressive thinking and flexibility required for a fast company organization which operates in a volatile environment.

Professor David Denyer of Cranfield University has recently published a very interesting research report on Organizational Resilience. He has identified the following four quadrants across to help us think about organizational resilience:

• preventative control (defensive consistency)
• mindful action (defensive flexibility)
• performance optimization (progressive consistency)
• adaptive innovation (progressive flexibility)

In this talk, I'll share my personal experience of using this thinking to help an organization to scale their product to Millions of users. I've dive deep into how we structured our organization for Structural Agility and how we set-up a very lightweight governance model using OKRs to drive the necessary flexible and progressive thinking.

All transformations have to start from within. This session was an attempt to create awareness on Diversity and Inclusion.

"Shipping is not success; let it sail" has been my mantra. Many organizations fall into the trap of project-based thinking and for them launching a product/service is success. We should step back and ask, "by launching this product/service, has it created any value (economic or otherwise) for the business and/or customers?" If the answer is no/not-sure, then it's premature to celebrate. Organizations want to achieve certain goals, expressed via measurable outcomes. Unless we see quantitative progress on those outcomes, our job is not done.

In this session, Naresh will share his experience helping organizations shift from project/output based thinking to product/outcome-based thinking. Not just at one team level, but at the entire organization level. Once we have the thinking in place, then how do we create a governance model which brings transparency and encourages collective ownership.

With Functions as a Service (FaaS), the smallest billable unit of computation is a single function execution. It's a beautifully simple idea, rejecting the metaphor of a server, and freeing developers to design smaller and simpler services. We can iterate on our code with a high degree of isolation, without fear of affecting other parts of the system; deploy systems in seconds with zero downtime, and always be available regardless of load.

However, building serverless apps is relatively new and filled with complexity:

• Configuration tooling was designed for the last generation of computing metaphors (and often lags behind the releases of new functionality)
• AWS is massive and overwhelming with many similar, but not the same, products
• The web console is confusing, with divergent interfaces between interlocking services
• Deep proprietary knowledge is required to configure and maintain common infrastructure primitives

In this talk, Naresh will walk you through a new approach to architecting applications with plain text using arc.codes to create apps in minutes and subsequently deploy in seconds with zero downtime and unprecedented availability.

This talk is an inspiration from Brian LeRoux's presentation Architecture as Text: Setup AWS Lambda, API Gateway, SNS, and DynamoDB on Easy Mode at YOW! 2017 Conference.

The ability to get the latest greatest version of your product into the hands of users, safely and quickly in a sustainable way, a.k.a Continuous Delivery is the need of the hour for every enterprise out there. Over the years, we've got very good about Continuous Delivery for server-side, web-applications. However, it's not as streamlined for Mobile or Desktop Apps.

This talk will cover the typical aspects we need to address while setting up a Continuous Delivery culture in the context of a large-scale mobile app:

• SCM/Version Control: moving from Feature Branches to a trunk-based development model
• Build/Test Environments: Decentralising the build & test environments using Containers(Docker) and CI Server(Jenkins)
• Microservices Architecture: Segregating and containerize the micro-services. Also refactoring the mobile apps to be more container friendly.
• Device Farm: Setting up a mobile device farm (using STF or ADF)
• Code-reviews: Improving the quality of code-reviews (using SonarQube, PRBuilder & PRRiskAdvisor)
• Test Pyramid: Building the right Test Pyramid to get rapid feedback by creating different kinds of automated tests to align with the CI Pipeline
• Code Quality: Visualising the health of our code-base (using C3)