Enhancing High-Quality User Stories with AQUSA: An Overview Study of Data Cleaning Process

Abstract

Agile software development has ended up profoundly prevalent over the final two decades. In conjunction with the increment of these methodology, amount of scientific research on this topic has also increased. This research concentrates on one component from Agile software development which is User Stories (US). In a recent paper, quality framework for User Stories was proposed together with a tool implementing which is still minimal finding to achieve high quality of user story document. It is closely related to the data requirement as part of the success factor in development project. The main goal is to analyze US requirement written and identification of potential errors in datasets that bring the effectiveness in forecasting the quality of User Stories for monitoring purpose. One of the steps require to identify the quality User Stories is by the route passing through data cleaning process. The research analysis considers performing data cleaning and pre-processing towards existing dataset of user story requirement from open-source agile software projects. Results of analyzing the User Stories will reveal the possibility step of data cleaning as initial step to extend the AQUSA tools and forecast the quality of User Stories. Thus, it can be related to the requirements quality based on the number of issues report in the dataset where highest number of issues report can be categorized as poor requirement statement.