  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.
