Agile Software Development Effort Estimation based on Product Backlog Items

Abstract

In all methods of Agile Software Development methods, an estimation process which determines the size of all Product Backlog Items (PBI) is essentially important. One of those methods is Planning Poker. This method is familiar among developers in the globe and one of the famous analogy-based estimation techniques known. In Planning Poker, the goal is to have a consensus vote on how big a product backlog should be. This consensus is reached via open discussion. As discussion is involved, many aspects of human errors could cause problems such as: anchoring effect and overconfidence which often leads to underestimation. To comprehend this, we offer an AI method that can be applied alongside the discussion to minimize this human error factors. We will be using NLP and ANN to classify a Product Backlog Item based on previous projects. The aim is individual estimate given by the AI that is free from influence from human to give better estimation. The result is expected at above 90% accuracy to give the best performance.