We'll go over important updates and announcements. If you've any questions about the conference, like how do I get the videos, etc. this session will help you answer all those questions. So don't miss it.

Welcome Address and Functional Conf Overview. Here you will get al the important details you need about the conference. So don't miss it.

Closing talk and thank you note!

Welcome Address and Agile India Conf Overview. Here you will get al the important details you need about the conference. So don't miss it.

Q & A with the Appium Core Committers:

• Jonathan Lipps
• Kazuakai Matsuo
• Sai Krishna
• Srinivasan Sekar
• Ayan Khan
• Christopher Hiller
• Mykola Mokhnach

Naresh Jain would moderate this panel.

Does your technical debt backlog look endless? Are you thinking about pausing feature development to resolve tech-debt? Stop. What if I told you that a good chunk of your backlog can simply wait? Tech-Debt can seem overwhelming when we look at it as a loosely organised list. This can lead to several anti-patterns in how we deal with them. Attend this session where I will be sharing strategies we have been leveraging to identify high priority tech-debt items to make sure we are able to continue feature development while improving code health.

Problem Statement: Tech-Debt often accumulates until productivity takes a serious hit and then as a knee jerk reaction we try to clean it up all at once. At this point there is just one large list of issues with a loose sense of priority. Net net it gives an impression that we have a huge backlog. This can lead to several anti-patterns.

• Chicken and Egg Problem - Too much Tech-Debt, so feature development is slow. Since feature development is slow, we cannot set aside time to fix issues.
• Fixing the wrong issues - In the larger scheme of things, it may be counter-productive to fix low priority issues just because they are easy.
• Pausing Feature Development - Approaches such as "Technical Debt Sprints" where we pause features to resolve tech-debt are not be sustainable even if they offer some short term benefits.
• Local Optima - Patchy cleanups which lead to uneven code health across the code base.
• And many more

Solution: Understand the impact of each Technical Debt at Block, Category and Item level to narrow your backlog to those issues which matter. While there are several tools that help us identify tech-debt it is up to us to map our context on those issues. Attend this talk where I will be going over how to VISUALISE, TRIAGE, PRIORITISE and STRATEGISE in order to get a realistic view on your tech-debt. Also I will be sharing my experience about how we have been leveraging time boxes and capacity constraints as a tool to make sure we are only working on the most important tech-debt issues.

Topics that will be covered:

• Tech-debt resolutions strategies - Anti-Patterns
• Tech Debt - Understanding Size vs Impact
• Tech-Debt Manifestations - Matrix view of areas of code and types of problems.
• Visualise - Triage - Prioritise - Strategise
  • Visual techniques to understand your tech-debt backlog with code analysis tools - Examples with popular tools
    • Bubble Charts - Coverage vs LOC, Maintainability vs LOC etc.
    • Git History - Multiple ways of looking at changes to a piece of code
    • Active Code Paths - Mapping usage to issues
    • Mapping Project Management Data - Bugs, Stories that touch a piece of code
  • Triaging the backlog to quickly eliminate tasks that can wait
    • Block level - Leveraging Logical Architecture
    • Category vice - Example: Front-End, Backend, API etc.
    • Item vice elimination
    • Refactor vs Rewrite
  • Prioritising tech-debt with layers of detail such as - Churn, LOC, Coverage, Bugs etc. Hotspot Identification.
  • Strategise - Approach to resolving each tech debt item based on Tech-Debt Manifestation Matrix
• Tech-Debt resolution - Hypothesis based, Data Driven approach
  • A template to capture your hypotheses, experiments and learnings
  • Visual confirmation that the issue is resolved
  • Just-Enough resolution - The uncomfortably short time-box - Imposing constraints to avoid runaway clean-ups
  • Guard rails to avoid a repeat of the same issue
• Incorporating tech-debt resolution into your Iterations, Weeks, Sprints, etc.
  • Identifying the right cadence on how often you fix debt - Hours per Day, Days per Week etc.
  • Tech-Debt back log Grooming
  • Cycling through categories of tech-debt
• Measuring Progress
  • Short-term - Measuring immediate impact on the code
  • Medium-term - Productivity improvements (Readability, Issue resolution time, etc)
  • Long-term - Team Health - Knowledge Silos, New Team Member Onboarding Time

In a complex, interdependent eco-system, where each service is evolving rapidly, we typically end up with an Integration Hell .i.e. the myriad problems that occur when API integrations break down

• Provider and consumer integrations break when their understanding of the API endpoints are out of sync
• Working integrations break when the API developer makes a change without informing the consumer
• Development and testing slow down when the consumer depends on the provider API running in a staging environment:
  • The network may be down
  • The environment hosting the API may be down
  • The staging API may crash, or may not even be started
  • Development can be delayed if the staging API is not kept up-to-date
  • API changes can come in at the last minute, resulting in breakage on the consumer side
  • The provider API may break backward compatibility, resulting in breakage on the consumer

Instead, what if we could make the dependencies between the provider and consumers explicit in the form of executable contracts. These executable contracts provide a common, clear definition of their API endpoints. They give instantaneous feedback about accidental breakage to the teams so that they can work independently. These executable contracts are:

1. Kept up-to-date and acts as a single source of truth
2. Used for service virtualisation, keeping consumers in sync with the contract
3. Run as tests against the provider API to validate it's request and response type definitions
4. Tightly integrated with CI
5. Capable of pinpointing any backwards-incompatible changes to the contract

This is Contract Driven Development, and it heralds the Death of Integration Hell.

Here's a sample contract:

This session will demonstrate all the key points of Contract Driven Development as implemented by the teams using an open-source tool called Specmatic.

In a complex, interdependent eco-system, where each service is evolving rapidly, we typically end up with an Integration Hell .i.e. the myriad problems that occur when API integrations break down

• Provider and consumer integrations break when their understanding of the API endpoints are out of sync
• Working integrations break when the API developer makes a change without informing the consumer
• Development and testing slow down when the consumer depends on the provider API running in a staging environment:
  • The network may be down
  • The environment hosting the API may be down
  • The staging API may crash, or may not even be started
  • Development can be delayed if the staging API is not kept up-to-date
  • API changes can come in at the last minute, resulting in breakage on the consumer side
  • The provider API may break backward compatibility, resulting in breakage on the consumer

Instead, what if we could make the dependencies between the provider and consumers explicit in the form of executable contracts. These executable contracts provide a common, clear definition of their API endpoints. They give instantaneous feedback about accidental breakage to the teams so that they can work independently. These executable contracts are:

1. Kept up-to-date and acts as a single source of truth
2. Used for service virtualisation, keeping consumers in sync with the contract
3. Run as tests against the provider API to validate it's request and response type definitions
4. Tightly integrated with CI
5. Capable of pinpointing any backwards-incompatible changes to the contract

This is Contract Driven Development, and it heralds the Death of Integration Hell.

Here's a sample contract:

This session will demonstrate all the key points of Contract Driven Development as implemented by the teams using an open-source tool called Specmatic.

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.

This talk will help you understand the vision behind ODSC Conference and how it has grown over the years.

Welcome to Agile India 2019

GUI and functional tests determine if the product is working correctly from an end user perspective. With increasing number of automated GUI tests we would want to automate when and where they are executed. Continuous Integration helps in merging code to a centralised repository frequently and find out issues early in development cycle in order to help push quality upstream.

In the talk, you will see examples of how you can setup CI system for Android and IOS native/hybrid apps and how to plugin your Appium tests in the pipeline using Jenkins. We will also talk about the challenges we face while setting it up for Android and IOS applications. We will also talk about how to strengthen your CI pipeline via integrating various tests and Static code analysis tools.

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.