# Explainable AI for Software Engineering

arXiv: https://arxiv.org/abs/2012.01614 · HTML: https://mlsanity.com/paper/2012.01614/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Dec 2020.

## Built on by (most cited first, 8 of 8)

- [Deep Learning for Android Malware Defenses: a Systematic Literature Review](https://mlsanity.com/paper/2103.05292/) (2021)
- [Pitfalls in Language Models for Code Intelligence: A Taxonomy and Survey](https://mlsanity.com/paper/2310.17903/) (2023)
- [RAGVA: Engineering Retrieval Augmented Generation-based Virtual Assistants in Practice](https://mlsanity.com/paper/2502.14930/) (2025)
- [SQAPlanner: Generating Data-Informed Software Quality Improvement Plans](https://mlsanity.com/paper/2102.09687/) (2021)
- [Studying the explanations for the automated prediction of bug and non-bug issues using LIME and SHAP](https://mlsanity.com/paper/2209.07623/) (2022)
- [Will It Survive? Deciphering the Fate of AI-Generated Code in Open Source](https://mlsanity.com/paper/2601.16809/) (2026)
- [A Methodology and Software Architecture to Support Explainability-by-Design](https://mlsanity.com/paper/2206.06251/) (2022)
- [Practitioners' Challenges and Perceptions of CI Build Failure Predictions at Atlassian](https://mlsanity.com/paper/2402.09651/) (2024)

## Abstract (arXiv)

Artificial Intelligence/Machine Learning techniques have been widely used in software engineering to improve developer productivity, the quality of software systems, and decision-making. However, such AI/ML models for software engineering are still impractical, not explainable, and not actionable. These concerns often hinder the adoption of AI/ML models in software engineering practices. In this article, we first highlight the need for explainable AI in software engineering. Then, we summarize three successful case studies on how explainable AI techniques can be used to address the aforementioned challenges by making software defect prediction models more practical, explainable, and actionable.