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Benchmarking Causal Study to Interpret Large Language Models for Source Code
We illustrate the insights of our benchmarking strategy by conducting a case study on the performance of ChatGPT under distinct prompt engineering methods.
Toward a Theory of Causation for Interpreting Neural Code Models
To demonstrate the practical benefit of $do_{code}$, we illustrate the insights that our framework can provide by performing a case study on two popular deep learning architectures and ten NCMs.
Improving the Effectiveness of Traceability Link Recovery using Hierarchical Bayesian Networks
To this end, we design and implement a HierarchiCal PrObabilistic Model for SoftwarE Traceability (Comet) that is able to infer candidate trace links.
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