Contrastive Learning for API Aspect Analysis

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/shahariar-shibli/Contrastive-Learning-for-API-Aspect-AnalysisComment: Accepted in the 38th IEEE/ACM International Conference on Automated Software Engineering (ASE2023

AutoML from Software Engineering Perspective: Landscapes and Challenges

Machine learning (ML) has been widely adopted in modern software, but the manual configuration of ML (e.g., hyper-parameter configuration) poses a significant challenge to software developers. Therefore, automated ML (AutoML), which seeks the optimal configuration of ML automatically, has received increasing attention from the software engineering community. However, to date, there is no comprehensive understanding of how AutoML is used by developers and what challenges developers encounter in using AutoML for software development. To fill this knowledge gap, we conduct the first study on understanding the use and challenges of AutoML from software developers’ perspective. We collect and analyze 1,554 AutoML downstream repositories, 769 AutoML-related Stack Overflow questions, and 1,437 relevant GitHub issues. The results suggest the increasing popularity of AutoML in a wide range of topics, but also the lack of relevant expertise. We manually identify specific challenges faced by developers for AutoML-enabled software. Based on the results, we derive a series of implications for AutoML framework selection, framework development, and research