JR
J.M. Rosenberg
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Requirements Engineering for Machine Learning
A Study in Behavior-Driven Development
Machine Learning (ML) systems are increasingly used in high-stakes, socially impactful domains, requiring attention to improve explainability and trust. However, current Requirements Engineering (RE) techniques often fail to address these human-centered qualities. This research investigates how Behavior-Driven Development (BDD) and Goal-Oriented Requirements Engineering (GORE) can improve the identification and visualization of requirements for explainability and trust. We conducted expert interviews and a survey to see how different stakeholders rate certain BDD scenarios and what they think of conceptual GORE models. Our results show that participants value concise, human-readable BDD scenarios and particularly like the GORE framework of i* to understand stakeholder relationships and system behaviors. The other GORE framework, GR4ML, was found to align more with business goals and addresses other stakeholder perspectives less. We conclude that BDD and GORE can improve explainability in ML system development. Future work should refine modeling tools to better integrate ethical and fairness considerations.
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Machine Learning (ML) systems are increasingly used in high-stakes, socially impactful domains, requiring attention to improve explainability and trust. However, current Requirements Engineering (RE) techniques often fail to address these human-centered qualities. This research investigates how Behavior-Driven Development (BDD) and Goal-Oriented Requirements Engineering (GORE) can improve the identification and visualization of requirements for explainability and trust. We conducted expert interviews and a survey to see how different stakeholders rate certain BDD scenarios and what they think of conceptual GORE models. Our results show that participants value concise, human-readable BDD scenarios and particularly like the GORE framework of i* to understand stakeholder relationships and system behaviors. The other GORE framework, GR4ML, was found to align more with business goals and addresses other stakeholder perspectives less. We conclude that BDD and GORE can improve explainability in ML system development. Future work should refine modeling tools to better integrate ethical and fairness considerations.
The purpose of this research is to reduce food waste by monitoring the ripening process of strawberries in order to optimize the harvesting time. To improve the moment of harvest, we need to know the ripeness of a strawberry. Using data from different color ranges and spaces we should be able to predict the ripeness of a strawberry on a 1-10 scale. We want to answer the question whether data from multiple color spaces can improve such a prediction model.
The prediction is performed on strawberry segments, using linear regression. The regression is performed based on the ripeness and the red and green pixel values in a segment. Each color space uses a slightly different metric.
We are able to show that the YCbCr and CIELab color space outperform RGB in such a linear regression. This is likely to come from the fact that these color spaces separate the luminance and chrominance. For the near-infrared range however, we do not have enough data to make such a conclusion as the available data only has ripeness levels 7-9.
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The prediction is performed on strawberry segments, using linear regression. The regression is performed based on the ripeness and the red and green pixel values in a segment. Each color space uses a slightly different metric.
We are able to show that the YCbCr and CIELab color space outperform RGB in such a linear regression. This is likely to come from the fact that these color spaces separate the luminance and chrominance. For the near-infrared range however, we do not have enough data to make such a conclusion as the available data only has ripeness levels 7-9.
...
The purpose of this research is to reduce food waste by monitoring the ripening process of strawberries in order to optimize the harvesting time. To improve the moment of harvest, we need to know the ripeness of a strawberry. Using data from different color ranges and spaces we should be able to predict the ripeness of a strawberry on a 1-10 scale. We want to answer the question whether data from multiple color spaces can improve such a prediction model.
The prediction is performed on strawberry segments, using linear regression. The regression is performed based on the ripeness and the red and green pixel values in a segment. Each color space uses a slightly different metric.
We are able to show that the YCbCr and CIELab color space outperform RGB in such a linear regression. This is likely to come from the fact that these color spaces separate the luminance and chrominance. For the near-infrared range however, we do not have enough data to make such a conclusion as the available data only has ripeness levels 7-9.
The prediction is performed on strawberry segments, using linear regression. The regression is performed based on the ripeness and the red and green pixel values in a segment. Each color space uses a slightly different metric.
We are able to show that the YCbCr and CIELab color space outperform RGB in such a linear regression. This is likely to come from the fact that these color spaces separate the luminance and chrominance. For the near-infrared range however, we do not have enough data to make such a conclusion as the available data only has ripeness levels 7-9.