# Machine Learning for Software Engineering: A Tertiary Study

arXiv: https://arxiv.org/abs/2211.09425 · HTML: https://mlsanity.com/paper/2211.09425/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 218 references that are in the map.
- First posted on arXiv 17 Nov 2022.

## Builds on (most cited first, 15 of 15)

- [A Survey of Machine Learning for Big Code and Naturalness](https://mlsanity.com/paper/1709.06182/) (2017)
- [Software Engineering for AI-Based Systems: A Survey](https://mlsanity.com/paper/2105.01984/) (2021)
- [Deep Learning for Source Code Modeling and Generation: Models, Applications and Challenges](https://mlsanity.com/paper/2002.05442/) (2020)
- [A Survey on Deep Learning for Software Engineering](https://mlsanity.com/paper/2011.14597/) (2020)
- [A Systematic Literature Review on the Use of Deep Learning in Software Engineering Research](https://mlsanity.com/paper/2009.06520/) (2020)
- [Learning Software Configuration Spaces: A Systematic Literature Review](https://mlsanity.com/paper/1906.03018/) (2019)
- [Google Scholar, Web of Science, and Scopus: a systematic comparison of citations in 252 subject categories](https://mlsanity.com/paper/1808.05053/) (2018)
- [Systematic Literature Reviews in Software Engineering -- Enhancement of the Study Selection Process using Cohen's Kappa Statistic](https://mlsanity.com/paper/2005.10890/) (2020)
- [Predictive Models in Software Engineering: Challenges and Opportunities](https://mlsanity.com/paper/2008.03656/) (2020)
- [Software Effort Estimation Accuracy Prediction of Machine Learning Techniques: A Systematic Performance Evaluation](https://mlsanity.com/paper/2101.10658/) (2021)
- [Guidelines for the Search Strategy to Update Systematic Literature Reviews in Software Engineering](https://mlsanity.com/paper/2006.05542/) (2020)
- [Machine Learning for Detecting Data Exfiltration: A Review](https://mlsanity.com/paper/2012.09344/) (2020)
- [How Tertiary Studies perform Quality Assessment of Secondary Studies in Software Engineering](https://mlsanity.com/paper/2110.03820/) (2021)
- [Predicting Software Effort from Use Case Points: A Systematic Review](https://mlsanity.com/paper/2012.07005/) (2020)
- [Standing on Shoulders or Feet? An Extended Study on the Usage of the MSR Data Papers](https://mlsanity.com/paper/2111.14583/) (2021)

## Built on by (most cited first, 7 of 7)

- [Empirical Evaluation of ChatGPT on Requirements Information Retrieval Under Zero-Shot Setting](https://mlsanity.com/paper/2304.12562/) (2023)
- [A Survey of Source Code Representations for Machine Learning-Based Cybersecurity Tasks](https://mlsanity.com/paper/2403.10646/) (2024)
- [AI Techniques in the Microservices Life-Cycle: A Systematic Mapping Study](https://mlsanity.com/paper/2305.16092/) (2023)
- [Good Tools are Half the Work: Tool Usage in Deep Learning Projects](https://mlsanity.com/paper/2310.19124/) (2023)
- [A Systematic Literature Review on the Use of Machine Learning in Software Engineering](https://mlsanity.com/paper/2406.13877/) (2024)
- [Perspective of Software Engineering Researchers on Machine Learning Practices Regarding Research, Review, and Education](https://mlsanity.com/paper/2411.19304/) (2024)
- [A Tertiary Review of Large Language Model-Based Code Generating Tasks: Trends, Challenges, and Future Directions](https://mlsanity.com/paper/2605.25536/) (2026)

## Abstract (arXiv)

Machine learning (ML) techniques increase the effectiveness of software engineering (SE) lifecycle activities. We systematically collected, quality-assessed, summarized, and categorized 83 reviews in ML for SE published between 2009-2022, covering 6,117 primary studies. The SE areas most tackled with ML are software quality and testing, while human-centered areas appear more challenging for ML. We propose a number of ML for SE research challenges and actions including: conducting further empirical validation and industrial studies on ML; reconsidering deficient SE methods; documenting and automating data collection and pipeline processes; reexamining how industrial practitioners distribute their proprietary data; and implementing incremental ML approaches.