Can Transformer Models Effectively Detect Software Aspects in StackOverflow Discussion?

TL;DR

This study evaluates the effectiveness of Transformer models in detecting software aspects from StackOverflow discussions, showing improvements over baseline models but with limitations in certain aspects and architecture sizes.

Contribution

It provides an empirical comparison of Transformer models versus SVM for software aspect detection in developer discussions, highlighting their strengths and weaknesses.

Findings

Transformer models outperform SVM for most software aspects.

XLNet is less effective than smaller architectures like DistilBERT.

Some aspects like 'Community' and 'Potability' are difficult for models to detect.

Abstract

Dozens of new tools and technologies are being incorporated to help developers, which is becoming a source of consternation as they struggle to choose one over the others. For example, there are at least ten frameworks available to developers for developing web applications, posing a conundrum in selecting the best one that meets their needs. As a result, developers are continuously searching for all of the benefits and drawbacks of each API, framework, tool, and so on. One of the typical approaches is to examine all of the features through official documentation and discussion. This approach is time-consuming, often makes it difficult to determine which aspects are the most important to a particular developer and whether a particular aspect is important to the community at large. In this paper, we have used a benchmark API aspects dataset (Opiner) collected from StackOverflow posts and observed how Transformer models (BERT, RoBERTa, DistilBERT, and XLNet) perform in detecting software aspects in textual developer discussion with respect to the baseline Support Vector Machine (SVM) model. Through extensive experimentation, we have found that transformer models improve the performance of baseline SVM for most of the aspects, i.e., `Performance', `Security', `Usability', `Documentation', `Bug', `Legal', `OnlySentiment', and `Others'. However, the models fail to apprehend some of the aspects (e.g., `Community' and `Potability') and their performance varies depending on the aspects. Also, larger architectures like XLNet are ineffective in interpreting software aspects compared to smaller architectures like DistilBERT.