  We present a novel approach - CLAA - for API aspect detection in API reviews
that utilizes transformer models trained with a supervised contrastive loss
objective function. We evaluate CLAA using performance and impact analysis. For
performance analysis, we utilized a benchmark dataset on developer discussions
collected from Stack Overflow and compare the results to those obtained using
state-of-the-art transformer models. Our experiments show that contrastive
learning can significantly improve the performance of transformer models in
detecting aspects such as Performance, Security, Usability, and Documentation.
For impact analysis, we performed empirical and developer study. On a randomly
selected and manually labeled 200 online reviews, CLAA achieved 92% accuracy
while the SOTA baseline achieved 81.5%. According to our developer study
involving 10 participants, the use of 'Stack Overflow + CLAA' resulted in
increased accuracy and confidence during API selection. Replication package:
https://github.com/disa-lab/Contrastive-Learning-API-Aspect-ASE2023
