Agile methodologies have long been the standard for development, but with today’s product life cycles moving at unprecedented speeds, the right workflow is just the start. Next is a mindset that allows for deep productivity gains and a truly transformed product experience, one that is prepared to build value from the outside in and think broadly of products as platforms for innovation in well-crafted ecosystems. In short, agile products need agile developers.
In this three part article we will discuss:
I. Expediting Production Exponentially. Spotlight on the developer experience for productivity gains of 30-40 percent
II. Ridiculously Easy Platforms. Take on a platform-driven, container-based approach to find increasing value
III. Machine Learning the Agile Way. Create a step change in process improvement and product performance with machine learning techniques
Expedite Production Exponentially.
Machine learning and automation are becoming par for the course for a more efficient software development:
- Use natural interfaces for people to develop, test, debug, deploy and maintain software. The wrong requirements means unmet expectations so its critical to accurately translate and extract good and concrete requirements from documents, meeting notes and spoken dialog
- Generate code by translating human and machine intent into code. Here software code can be created by expanding on lower fidelity wire-frames or code can be generated based upon a requirements model. Finally, open source code repositories provide a well of repeatable building blocks and API's
- Predictive work schedules and logistics. Once requirements have been specified, the next step is to create a task breakdown based on kills or some sort of model. There are predictive tools to schedule and deliver tasks to developers. For example, interpret what kind of problems a developer is better at solving helps improve work scheduling. Here developers can also be assisted with recommendations from domain knowledge captured in artificial intelligence networks