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Found 8 papers, 2 papers with code
Rethinking Software Engineering in the Foundation Model Era: A Curated Catalogue of Challenges in the Development of Trustworthy FMware
Finally, we (i) show how the unique properties of FMArts enabled us to design and develop a complex FMware for a large customer in a timely manner and (ii) discuss the lessons that we learned in doing so.
Keeping Deep Learning Models in Check: A History-Based Approach to Mitigate Overfitting
This classifier is then used to detect if a trained model is overfit.
The Impact of Using Regression Models to Build Defect Classifiers
We find that: i) Random forest based classifiers outperform other classifiers (best AUC) for both classifier building approaches; ii) In contrast to common practice, building a defect classifier using discretized defect counts (i. e., discretized classifiers) does not always lead to better performance.
Impact of Discretization Noise of the Dependent variable on Machine Learning Classifiers in Software Engineering
Researchers usually discretize a continuous dependent variable into two target classes by introducing an artificial discretization threshold (e. g., median).
Towards a consistent interpretation of AIOps models
Artificial Intelligence for IT Operations (AIOps) has been adopted in organizations in various tasks, including interpreting models to identify indicators of service failures.
The impact of feature importance methods on the interpretation of defect classifiers
We further observe that the commonly used defect datasets are rife with feature interactions and these feature interactions impact the computed feature importance ranks of the CS methods (not the CA methods).
Towards Training Reproducible Deep Learning Models
However, DL models are challenging to be reproduced due to issues like randomness in the software (e. g., DL algorithms) and non-determinism in the hardware (e. g., GPU).
Can I use this publicly available dataset to build commercial AI software? -- A Case Study on Publicly Available Image Datasets
Publicly available datasets are one of the key drivers for commercial AI software.
