1 citations · 1 across the 4 of their papers we have counts for
4 papers
LibEvoBench: Probing Temporal Knowledge Stratification in Code Generation Models
Daniele Cipollone, Sergey Titov, Maliheh Izadi +2
Large software projects often depend on older versions of libraries, even as APIs continue to evolve across releases. This creates a challenge for LLMs: they must maintain knowledg…
Counterfactual Training: Teaching Models Plausible and Actionable Explanations
Patrick Altmeyer, Aleksander Buszydlik, Arie van Deursen +1
We propose a novel training regime termed counterfactual training that leverages counterfactual explanations to increase the explanatory capacity of models. Counterfactual explanat…
Prepared for the Unknown: Adapting AIOps Capacity Forecasting Models to Data Changes
Lorena Poenaru-Olaru, Wouter van 't Hof, Adrian Stando +5
Capacity management is critical for software organizations to allocate resources effectively and meet operational demands. An important step in capacity management is predicting fu…
Sustainable Machine Learning Retraining: Optimizing Energy Efficiency Without Compromising Accuracy
Lorena Poenaru-Olaru, June Sallou, Luis Cruz +2
The reliability of machine learning (ML) software systems is heavily influenced by changes in data over time. For that reason, ML systems require regular maintenance, typically bas…