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

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.

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Publication Details

Journal
IEEE Software
Published
2024-11-26
DOI
https://doi.org/10.1109/ms.2024.3505544
Citations
2
Primary Topic
Software Engineering Research
Type
article
Field-Weighted Citation Impact
1.56
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article

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

Szymon Stradowski, Lech Madeyski
2 citations
IEEE Software
Software Engineering Research
1.56
article

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

Szymon Stradowski, Lech Madeyski
article en
2 citations

Abstract

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.

IEEE SoftwareVol. 42(3)
Wrocław University of Science and Technology (PL), AGH University of Krakow (PL)
Decent work and economic growth
Openalex Percentile: Top 12%
Software Engineering Research
1.56
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