# Predictive Models in Software Engineering: Challenges and Opportunities

arXiv: https://arxiv.org/abs/2008.03656 · HTML: https://mlsanity.com/paper/2008.03656/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Aug 2020.

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

- [Machine Learning for Software Engineering: A Tertiary Study](https://mlsanity.com/paper/2211.09425/) (2022)
- [SoK: Machine Learning for Continuous Integration](https://mlsanity.com/paper/2304.02829/) (2023)
- [Predicting the First Response Latency of Maintainers and Contributors in Pull Requests](https://mlsanity.com/paper/2311.07786/) (2023)
- [Automatic Detection of LLM-Generated Code: A Comparative Case Study of Contemporary Models Across Function and Class Granularities](https://mlsanity.com/paper/2409.01382/) (2024)
- [ArchISMiner: A Framework for Automatic Mining of Architectural Issue-Solution Pairs from Online Developer Communities](https://mlsanity.com/paper/2510.21966/) (2025)

## Abstract (arXiv)

Predictive models are one of the most important techniques that are widely applied in many areas of software engineering. There have been a large number of primary studies that apply predictive models and that present well-preformed studies and well-desigeworks in various research domains, including software requirements, software design and development, testing and debugging and software maintenance. This paper is a first attempt to systematically organize knowledge in this area by surveying a body of 139 papers on predictive models. We describe the key models and approaches used, classify the different models, summarize the range of key application areas, and analyze research results. Based on our findings, we also propose a set of current challenges that still need to be addressed in future work and provide a proposed research road map for these opportunities.