Interpretability/Explainability Applied to Machine Learning Software Defect Prediction: An Industrial Perspective

Szymon Stradowski

doi:10.1109/MS.2024.3505544

Interpretability/Explainability Applied to Machine Learning Software Defect Prediction: An Industrial Perspective

2024-01-01

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AI

DOI：10.1109/MS.2024.3505544

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We explore the interpretability and explainability of machine learning software defect prediction models in the context of commercial system-level testing in Nokia 5G. We evaluate how they can gain stakeholders' acceptance, analyze expectations, offer management strategies, and identify business impacts.

Keyword:

Software development management

Predictive models

Stakeholders

Industries

Explainable AI

Knowledge discovery

Context modeling

Companies

Software engineering

Quality assurance

Interpretability/Explainability Applied to Machine Learning Software Defect Prediction: An Industrial Perspective

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