# Exploring Web Search Engines to Find Architectural Knowledge

arXiv: https://arxiv.org/abs/2103.11705 · HTML: https://mlsanity.com/paper/2103.11705/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Mar 2021.

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

- [ARLO: A Tailorable Approach for Transforming Natural Language Software Requirements into Architecture using LLMs](https://mlsanity.com/paper/2504.06143/) (2025)
- [ArchISMiner: A Framework for Automatic Mining of Architectural Issue-Solution Pairs from Online Developer Communities](https://mlsanity.com/paper/2510.21966/) (2025)

## Abstract (arXiv)

Software engineers need relevant and up-to-date architectural knowledge (AK), in order to make well-founded design decisions. However, finding such AK is quite challenging. One pragmatic approach is to search for AK on the web using traditional search engines (e.g. Google); this is common practice among software engineers. Still, we know very little about what AK is retrieved, from where, and how useful it is. In this paper, we conduct an empirical study with 53 software engineers, who used Google to make design decisions using the Attribute-Driven-Design method. Based on how the subjects assessed the nature and relevance of the retrieved results, we determined how effective web search engines are to find relevant architectural information. Moreover, we identified the different sources of AK on the web and their associated AK concepts.