Agile Effort Estimation Improved by Feature Selection and Model Explainability

Agile methodologies are widely adopted in the industry, with iterative development being a common practice. However, this approach introduces certain risks in controlling and managing the planned scope for delivery at the end of each iteration. Previous studies have proposed machine learning methods to predict the likelihood of meeting this committed scope, using models trained on features extracted from prior iterations and their associated tasks. A crucial aspect of any predictive model is user trust, which depends on the model’s explainability. However, an excessive number of features can complicate interpretation. In this work, we propose feature subset selection methods to reduce the number of features without compromising model performance.To ensure interpretability, we leverage state-of-the-art explainability techniques to analyze the key features driving model predictions. Our evaluation, conducted on five large open-source projects from prior studies, demonstrates successful feature subset selection, reducing the feature set to 10% of its original size without any loss in predictive performance. Using explainability tools, we provide a synthesis of the features with the most significant impact on iteration performance predictions across agile projects.