# ArchISMiner: A Framework for Automatic Mining of Architectural Issue-Solution Pairs from Online Developer Communities

arXiv: https://arxiv.org/abs/2510.21966 · HTML: https://mlsanity.com/paper/2510.21966/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 39 of its 116 references that are in the map.
- First posted on arXiv 24 Oct 2025.

## Builds on (most cited first, 30 of 39)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
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- [GPT-4o System Card](https://mlsanity.com/paper/2410.21276/) (2024)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [Universal Sentence Encoder](https://mlsanity.com/paper/1803.11175/) (2018)
- [Recent Trends in Deep Learning Based Natural Language Processing](https://mlsanity.com/paper/1708.02709/) (2017)
- [Extractive Summarization as Text Matching](https://mlsanity.com/paper/2004.08795/) (2020)
- [Ranking Sentences for Extractive Summarization with Reinforcement Learning](https://mlsanity.com/paper/1802.08636/) (2018)
- [Fine-tune BERT for Extractive Summarization](https://mlsanity.com/paper/1903.10318/) (2019)
- [Neural Document Summarization by Jointly Learning to Score and Select Sentences](https://mlsanity.com/paper/1807.02305/) (2018)
- [Large Language Model for Vulnerability Detection and Repair: Literature Review and the Road Ahead](https://mlsanity.com/paper/2404.02525/) (2024)
- [Code and Named Entity Recognition in StackOverflow](https://mlsanity.com/paper/2005.01634/) (2020)
- [On Model Stability as a Function of Random Seed](https://mlsanity.com/paper/1909.10447/) (2019)
- [PTM4Tag: Sharpening Tag Recommendation of Stack Overflow Posts with Pre-trained Models](https://mlsanity.com/paper/2203.10965/) (2022)
- [On the validity of pre-trained transformers for natural language processing in the software engineering domain](https://mlsanity.com/paper/2109.04738/) (2021)
- [Recommending Stack Overflow Posts for Fixing Runtime Exceptions using Failure Scenario Matching](https://mlsanity.com/paper/2009.10174/) (2020)
- [Leveraging Unsupervised Learning to Summarize APIs Discussed in Stack Overflow](https://mlsanity.com/paper/2111.13962/) (2021)
- [Predictive Models in Software Engineering: Challenges and Opportunities](https://mlsanity.com/paper/2008.03656/) (2020)
- [Towards Exploring the Code Reuse from Stack Overflow during Software Development](https://mlsanity.com/paper/2204.12711/) (2022)
- [Automated Summarization of Stack Overflow Posts](https://mlsanity.com/paper/2305.16680/) (2023)
- [ChatGPT Incorrectness Detection in Software Reviews](https://mlsanity.com/paper/2403.16347/) (2024)
- [Essential Sentences for Navigating Stack Overflow Answers](https://mlsanity.com/paper/1912.13455/) (2019)
- [Answer Summarization for Technical Queries: Benchmark and New Approach](https://mlsanity.com/paper/2209.10868/) (2022)
- [Mining API Usage Scenarios from Stack Overflow](https://mlsanity.com/paper/2102.08874/) (2021)
- [Technical Q&A Site Answer Recommendation via Question Boosting](https://mlsanity.com/paper/2210.15846/) (2022)
- [Unveiling the Role of ChatGPT in Software Development: Insights from Developer-ChatGPT Interactions on GitHub](https://mlsanity.com/paper/2505.03901/) (2025)
- [Recommending Comprehensive Solutions for Programming Tasks by Mining Crowd Knowledge](https://mlsanity.com/paper/1903.07662/) (2019)
- [Exploring Web Search Engines to Find Architectural Knowledge](https://mlsanity.com/paper/2103.11705/) (2021)
- [Mining Architecture Tactics and Quality Attributes Knowledge in Stack Overflow](https://mlsanity.com/paper/2105.07940/) (2021)

## Abstract (arXiv)

Stack Overflow (SO), a leading online community forum, is a rich source of software development knowledge. However, locating architectural knowledge, such as architectural solutions remains challenging due to the overwhelming volume of unstructured content and fragmented discussions. Developers must manually sift through posts to find relevant architectural insights, which is time-consuming and error-prone. This study introduces ArchISMiner, a framework for mining architectural knowledge from SO. The framework comprises two complementary components: ArchPI and ArchISPE. ArchPI trains and evaluates multiple models, including conventional ML/DL models, Pre-trained Language Models (PLMs), and Large Language Models (LLMs), and selects the best-performing model to automatically identify Architecture-Related Posts (ARPs) among programming-related discussions. ArchISPE employs an indirect supervised approach that leverages diverse features, including BERT embeddings and local TextCNN features, to extract architectural issue-solution pairs. Our evaluation shows that the best model in ArchPI achieves an F1-score of 0.960 in ARP detection, and ArchISPE outperforms baselines in both SE and NLP fields, achieving F1-scores of 0.883 for architectural issues and 0.894 for solutions. A user study further validated the quality (e.g., relevance and usefulness) of the identified ARPs and the extracted issue-solution pairs. Moreover, we applied ArchISMiner to three additional forums, releasing a dataset of over 18K architectural issue-solution pairs. Overall, ArchISMiner can help architects and developers identify ARPs and extract succinct, relevant, and useful architectural knowledge from developer communities more accurately and efficiently. The replication package of this study has been provided at https://github.com/JeanMusenga/ArchISPE