# On Using Active Learning and Self-Training when Mining Performance Discussions on Stack Overflow

## TL;DR

This paper explores the combined use of active learning and self-training to improve classification of Stack Overflow posts, highlighting challenges in annotation and demonstrating potential accuracy gains.

## Contribution

It systematically evaluates active learning and self-training for classifying technical discussions, revealing annotation difficulties and proposing mitigation strategies.

## Key findings

- Active learning identifies valuable but difficult-to-annotate examples.
- Self-training can enhance classifier accuracy with limited labeled data.
- Low inter-rater agreement highlights annotation challenges.

## Abstract

Abundant data is the key to successful machine learning. However, supervised learning requires annotated data that are often hard to obtain. In a classification task with limited resources, Active Learning (AL) promises to guide annotators to examples that bring the most value for a classifier. AL can be successfully combined with self-training, i.e., extending a training set with the unlabelled examples for which a classifier is the most certain. We report our experiences on using AL in a systematic manner to train an SVM classifier for Stack Overflow posts discussing performance of software components. We show that the training examples deemed as the most valuable to the classifier are also the most difficult for humans to annotate. Despite carefully evolved annotation criteria, we report low inter-rater agreement, but we also propose mitigation strategies. Finally, based on one annotator's work, we show that self-training can improve the classification accuracy. We conclude the paper by discussing implication for future text miners aspiring to use AL and self-training.