Exploring Visual-Based Sprint Evaluation Using Convolutional Neural Networks and Agile Project Metrics

Agile project monitoring commonly relies on numerical metrics and visual artifacts, such as Burndown charts, to assess Sprint progress and identify potential deviations. However, most automated approaches focus on structured data, while the visual patterns contained in agile charts remain underexplored. This paper presents an exploratory study on visual-based Sprint evaluation using convolutional neural networks and agile project metrics. The proposed approach uses Burndown and Completed vs. Uncompleted Work chart (TTvsNT) images to classify Sprint performance into four categories: Poor, Regular, Good, and Excellent. A transfer-learning strategy based on MobileNetV2 was applied, including image preprocessing, Sprint-level data partitioning, two-phase training, and multiclass evaluation. The model achieved an overall accuracy of 70.33% on the evaluation set. Class-level results showed better performance for the Poor and Excellent categories, while the intermediate classes presented greater ambiguity. The main contribution of this study lies in evaluating Sprint monitoring charts as a complementary visual representation to traditional metric-based models. The findings provide preliminary evidence that these images contain useful performance-related patterns; however, the limited dataset size and current accuracy do not support production-level deployment. Further research with larger datasets, additional architectures, and multimodal approaches is required.