# Navigating Complexity in Software Engineering: A Prototype for Comparing GPT-n Solutions

arXiv: https://arxiv.org/abs/2301.12169 · HTML: https://mlsanity.com/paper/2301.12169/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jan 2023.

## Built on by (most cited first, 5 of 5)

- [Summary of ChatGPT-Related Research and Perspective Towards the Future of Large Language Models](https://mlsanity.com/paper/2304.01852/) (2023)
- [Advancements in Generative AI: A Comprehensive Review of GANs, GPT, Autoencoders, Diffusion Model, and Transformers](https://mlsanity.com/paper/2311.10242/) (2023)
- [CodePori: Large-Scale System for Autonomous Software Development Using Multi-Agent Technology](https://mlsanity.com/paper/2402.01411/) (2024)
- [Can Large Language Models Serve as Data Analysts? A Multi-Agent Assisted Approach for Qualitative Data Analysis](https://mlsanity.com/paper/2402.01386/) (2024)
- ["Give me the code" -- Log Analysis of First-Year CS Students' Interactions With GPT](https://mlsanity.com/paper/2411.17855/) (2024)

## Abstract (arXiv)

Navigating the diverse solution spaces of non-trivial software engineering tasks requires a combination of technical knowledge, problem-solving skills, and creativity. With multiple possible solutions available, each with its own set of trade-offs, it is essential for programmers to evaluate the various options and select the one that best suits the specific requirements and constraints of a project. Whether it is choosing from a range of libraries, weighing the pros and cons of different architecture and design solutions, or finding unique ways to fulfill user requirements, the ability to think creatively is crucial for making informed decisions that will result in efficient and effective software. However, the interfaces of current chatbot tools for programmers, such as OpenAI's ChatGPT or GitHub Copilot, are optimized for presenting a single solution, even for complex queries. While other solutions can be requested, they are not displayed by default and are not intuitive to access. In this paper, we present our work-in-progress prototype "GPTCompare", which allows programmers to visually compare multiple source code solutions generated by GPT-n models for the same programming-related query by highlighting their similarities and differences.