# Contrastive Learning for API Aspect Analysis

arXiv: https://arxiv.org/abs/2307.16878 · HTML: https://mlsanity.com/paper/2307.16878/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 23 of its 43 references that are in the map.
- First posted on arXiv 31 Jul 2023.

## Builds on (most cited first, 23 of 23)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [SimCSE: Simple Contrastive Learning of Sentence Embeddings](https://mlsanity.com/paper/2104.08821/) (2021)
- [Supervised Learning of Universal Sentence Representations from Natural Language Inference Data](https://mlsanity.com/paper/1705.02364/) (2017)
- [Contrastive Code Representation Learning](https://mlsanity.com/paper/2007.04973/) (2020)
- [SynCoBERT: Syntax-Guided Multi-Modal Contrastive Pre-Training for Code Representation](https://mlsanity.com/paper/2108.04556/) (2021)
- [PyTorch Metric Learning](https://mlsanity.com/paper/2008.09164/) (2020)
- [Self-Supervised Contrastive Learning for Code Retrieval and Summarization via Semantic-Preserving Transformations](https://mlsanity.com/paper/2009.02731/) (2020)
- [Code and Named Entity Recognition in StackOverflow](https://mlsanity.com/paper/2005.01634/) (2020)
- [CODE-MVP: Learning to Represent Source Code from Multiple Views with Contrastive Pre-Training](https://mlsanity.com/paper/2205.02029/) (2022)
- [VarCLR: Variable Semantic Representation Pre-training via Contrastive Learning](https://mlsanity.com/paper/2112.02650/) (2021)
- [Understanding How and Why Developers Seek and Analyze API-related Opinions](https://mlsanity.com/paper/2102.08495/) (2021)
- [Aspect-Based API Review Classification: How Far Can Pre-Trained Transformer Model Go?](https://mlsanity.com/paper/2201.11327/) (2022)
- [HELoC: Hierarchical Contrastive Learning of Source Code Representation](https://mlsanity.com/paper/2203.14285/) (2022)
- [Evaluating few shot and Contrastive learning Methods for Code Clone Detection](https://mlsanity.com/paper/2204.07501/) (2022)
- [Contrastive Learning for Multi-Modal Automatic Code Review](https://mlsanity.com/paper/2205.14289/) (2022)
- [CLeBPI: Contrastive Learning for Bug Priority Inference](https://mlsanity.com/paper/2212.01011/) (2022)
- [Can Transformer Models Effectively Detect Software Aspects in StackOverflow Discussion?](https://mlsanity.com/paper/2209.12065/) (2022)

## Abstract (arXiv)

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