# Towards a question answering assistant for software development using a transformer-based language model

arXiv: https://arxiv.org/abs/2103.09423 · HTML: https://mlsanity.com/paper/2103.09423/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Mar 2021.

## Built on by (most cited first, 2 of 2)

- [FACOS: Finding API Relevant Contents on Stack Overflow with Semantic and Syntactic Analysis](https://mlsanity.com/paper/2111.07238/) (2021)
- [Generative Language Models Potential for Requirement Engineering Applications: Insights into Current Strengths and Limitations](https://mlsanity.com/paper/2412.00959/) (2024)

## Abstract (arXiv)

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.