# Redefining Developer Assistance: Through Large Language Models in Software Ecosystem

arXiv: https://arxiv.org/abs/2312.05626 · HTML: https://mlsanity.com/paper/2312.05626/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 29 references that are in the map.
- First posted on arXiv 9 Dec 2023.

## Builds on (most cited first, 17 of 17)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Teaching Large Language Models to Self-Debug](https://mlsanity.com/paper/2304.05128/) (2023)
- [An Empirical Evaluation of Using Large Language Models for Automated Unit Test Generation](https://mlsanity.com/paper/2302.06527/) (2023)
- [GitHub Copilot AI pair programmer: Asset or Liability?](https://mlsanity.com/paper/2206.15331/) (2022)
- [The Programmer's Assistant: Conversational Interaction with a Large Language Model for Software Development](https://mlsanity.com/paper/2302.07080/) (2023)
- [UniversalNER: Targeted Distillation from Large Language Models for Open Named Entity Recognition](https://mlsanity.com/paper/2308.03279/) (2023)
- [LLM-Assisted Content Analysis: Using Large Language Models to Support Deductive Coding](https://mlsanity.com/paper/2306.14924/) (2023)
- [EcoAssistant: Using LLM Assistant More Affordably and Accurately](https://mlsanity.com/paper/2310.03046/) (2023)
- [Code and Named Entity Recognition in StackOverflow](https://mlsanity.com/paper/2005.01634/) (2020)
- [PUnifiedNER: A Prompting-based Unified NER System for Diverse Datasets](https://mlsanity.com/paper/2211.14838/) (2022)
- [Can Machines Read Coding Manuals Yet? -- A Benchmark for Building Better Language Models for Code Understanding](https://mlsanity.com/paper/2109.07452/) (2021)
- [Duplicate Question Retrieval and Confirmation Time Prediction in Software Communities](https://mlsanity.com/paper/2309.05035/) (2023)
- [Software Entity Recognition with Noise-Robust Learning](https://mlsanity.com/paper/2308.10564/) (2023)
- [Evaluating the Ebb and Flow: An In-depth Analysis of Question-Answering Trends across Diverse Platforms](https://mlsanity.com/paper/2309.05961/) (2023)

## Abstract (arXiv)

In this paper, we delve into the advancement of domain-specific Large Language Models (LLMs) with a focus on their application in software development. We introduce DevAssistLlama, a model developed through instruction tuning, to assist developers in processing software-related natural language queries. This model, a variant of instruction tuned LLM, is particularly adept at handling intricate technical documentation, enhancing developer capability in software specific tasks. The creation of DevAssistLlama involved constructing an extensive instruction dataset from various software systems, enabling effective handling of Named Entity Recognition (NER), Relation Extraction (RE), and Link Prediction (LP). Our results demonstrate DevAssistLlama's superior capabilities in these tasks, in comparison with other models including ChatGPT. This research not only highlights the potential of specialized LLMs in software development also the pioneer LLM for this domain.