  Question answering platforms, such as Stack Overflow, have impacted
substantially how developers search for solutions for their programming
problems. The crowd knowledge content available from such platforms has also
been used to leverage software development tools. The recent advances on
Natural Language Processing, specifically on more powerful language models,
have demonstrated ability to enhance text understanding and generation. In this
context, we aim at investigating the factors that can influence on the
application of such models for understanding source code related data and
produce more interactive and intelligent assistants for software development.
In this preliminary study, we particularly investigate if a how-to question
filter and the level of context in the question may impact the results of a
question answering transformer-based model. We suggest that fine-tuning models
with corpus based on how-to questions can impact positively in the model and
more contextualized questions also induce more objective answers.
