I would like to share my experience and journey on how we brought down our Jenkins build pipeline time down from over 90 minutes to under 12 minutes. In the process, I would share specific techniques which helped and also some, which logically made sense, but actually did not help. If your team is trying to optimize their build times, then this session might give you some ideas on how to approach the problem.

Development Impact - For one of our build job, below graph shows how the number of builds in a day have increased over a period of time as the build time has reduced. Frequency of code check-in has increased; Wait time has reduced; failed test case faster to isolate and fix.

Business Impact - More builds leading to quicker feedback and faster story acceptance and less story spill over.

Test automation is extremely crucial in adoption of an agile delivery. However, it can take one for a ride, if the approach is not correct. In this sensational, heart throbbing, experience report, we'll share our story of how we turned around an inefficient, expensive automation style to lean, efficient style. In addition to sharing a real-world example, we'll also share some of the key challenges we faced and how we solved them. If you are convinced about the Testing Pyramid, but are struggling to invert it, then this session is for you.

Business Impact:

  Earlier Defect Detection - Higher test coverage at Unit/Intermediate layers lead to earlier defect detection. Reduced number of issues found on higher test environments/Production. Reduced cost of defect fixing.

  Reduced maintenance cost - UI tests are fragile and costlier to maintain Vs backend tests. No of changes in services layer are comparatively less.

  Reduced test execution time - Backend tests are much faster. Almost 7-10  times faster than UI Tests - improved build certification time.

  Test feedbacks are naturally distributed across layers of application. Test feedbacks are more pin pointed/ granular.

After 3 years of improving our frontend development process we ended up with mixing BDD, DSL and Storyboards for developing spotless clean frontend code. Each approach is already powerful on his own but combining these will make them unbeatable. The characteristics are collaborate, communicate and visualize, BDD is the collaborating process between disciplines, DSL for enabling the communication and Storyboards for visualization.

Let me show you how this works by live coding a frontend application using this mixture of approaches. The process is supported by a homemade tool based on CasperJS and PhantomCSS. And all scripts are written in JavaScript. At the end of the session we can discuss if the created code is spotless clean.

"We've tested this feature thoroughly and it worked really well. But for some weird reason, it's really slow in production today...must be a network issue...or may be the server is having a bad day..."

Do you often hear these kinds of comments in your development team? Let us guess, your application is very data-centric and churns big blocks of data on every user request. And under the hood, your application is most probably heavily dependent on long/complex queries with joins, temp-tables, case-statements, nested queries, etc.

These SQL queries probably started-out very simple. But as your requirements evolved, iteration after iterations, the queries also grew in complexity. And most often, even if you test-drove your newer stories, the performance of these complex queries is not evident until you run them in production. 

Given that our requirements will evolve and so will our database, how do you deal with the above problems?

There are TWO essential parts to evolutionary database design:

1. The art of refactoring your SQL queries.
2. Figuring out the right balance of what processing is done in SQL on the DB sides and what is done on your service side in your App/Web Server.

Join us as we take a tour of how we refactored our complex, non-performant queries and overall DB without hurting our time-to-market.