The parallel agile process: Applying parallel processing techniques to software engineering

For the last 4 years, we have been experimenting with the parallel agile (PA) approach. PA achieves significant schedule compression by leveraging parallelism; large teams of developers can independently and concurrently develop scenarios from initial concept through code. This paper summarizes our experience in defining and evolving PA by applying it to four representative emergent‐technology applications: location‐based advertising, picture sharing, bad driver reporting, and a VR/AR game project. In comparison with the mainstream architected agile process that we had been using on similar systems, the PA process has consistently achieved significant speedups in system development while simultaneously reducing defects. PA uses storyboards and prototypes to define both sunny‐day and rainy‐day scenarios, defines requirements for each use case, and decomposes each use case into a conceptual model‐view‐controller (MVC) pattern. PA also uses code generation from UML models to rapidly construct a domain‐driven microservice architecture at the inception of a project. This microservice architecture is then used to enable prototype code to interact with a live database during requirements definition. PA then uses automatic test case generation from the same UML model. The paper summarizes the overall challenge of software schedule compression, identifies managed parallel development as generally the most powerful but least‐practiced strategy for schedule compression, and summarizes the key elements required to support parallelism. It then summarizes the key techniques for scaling up PA, using a previous million‐line command and control project as an example. We have used MS‐degree graduate student projects to gather productivity data because the university environment has afforded us the opportunity to explore massively parallel development over an extended time period. We are now working with a large company to modernize their main legacy system using the PA methods. The prospective looks good as many of the techniques used (eg, domain models, use case analysis, and MVC decomposition) have been proven to be effective in industry on a wide range of projects for multiple decades. The new techniques (eg, executable domain models, and visual modeling of sprint plans) should also serve to make integration and project management work better on large development efforts.