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XAIport: A Service Framework for the Early Adoption of XAI in AI Model Development
In this study, we propose the early adoption of Explainable AI (XAI) with a focus on three properties: Quality of explanation, the explanation summaries should be consistent across multiple XAI methods; Architectural Compatibility, for effective integration in XAI, the architecture styles of both the XAI methods and the models to be explained must be compatible with the framework; Configurable operations, XAI explanations are operable, akin to machine learning operations.
EnHMM: On the Use of Ensemble HMMs and Stack Traces to Predict the Reassignment of Bug Report Fields
Finally, when comparing EnHMM to Im. ML. KNN, a recent approach in the field, we found that the average F-measure score of EnHMM improves the average F-measure of Im. ML. KNN by 6. 80% and improves the average recall of Im. ML. KNN by 36. 09%.
DepGraph: Localizing Performance Bottlenecks in Multi-Core Applications Using Waiting Dependency Graphs and Software Tracing
In this paper, we use a system level tracing approach to extract a Waiting Dependency Graph that shows the breakdown of a task execution among all the interleaving threads and resources.
Software Engineering
