# Explainable Software Analytics

arXiv: https://arxiv.org/abs/1802.00603 · HTML: https://mlsanity.com/paper/1802.00603/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Feb 2018.

## Built on by (most cited first, 15 of 15)

- [What Do We Want From Explainable Artificial Intelligence (XAI)? -- A Stakeholder Perspective on XAI and a Conceptual Model Guiding Interdisciplinary XAI Research](https://mlsanity.com/paper/2102.07817/) (2021)
- [Explainable Artificial Intelligence: A Survey of Needs, Techniques, Applications, and Future Direction](https://mlsanity.com/paper/2409.00265/) (2024)
- [Stop ordering machine learning algorithms by their explainability! A user-centered investigation of performance and explainability](https://mlsanity.com/paper/2206.10610/) (2022)
- [Adversarial Patch Attacks and Defences in Vision-Based Tasks: A Survey](https://mlsanity.com/paper/2206.08304/) (2022)
- [XAI Handbook: Towards a Unified Framework for Explainable AI](https://mlsanity.com/paper/2105.06677/) (2021)
- [The impact of feature importance methods on the interpretation of defect classifiers](https://mlsanity.com/paper/2202.02389/) (2022)
- [A Systematic Literature Review on Explainability for Machine/Deep Learning-based Software Engineering Research](https://mlsanity.com/paper/2401.14617/) (2024)
- [Requirements for Explainability and Acceptance of Artificial Intelligence in Collaborative Work](https://mlsanity.com/paper/2306.15394/) (2023)
- [Explainable AI for Correct Root Cause Analysis of Product Quality in Injection Moulding](https://mlsanity.com/paper/2505.01445/) (2025)
- [Local Explanations for Clinical Search Engine results](https://mlsanity.com/paper/2110.12891/) (2021)
- [Mining Action Rules for Defect Reduction Planning](https://mlsanity.com/paper/2405.13740/) (2024)
- [Towards Explainable Test Case Prioritisation with Learning-to-Rank Models](https://mlsanity.com/paper/2405.13786/) (2024)
- [Human-centered explanation does not fit all: The interplay of sociotechnical, cognitive, and individual factors in the effect AI explanations in algorithmic decision-making](https://mlsanity.com/paper/2502.12354/) (2025)
- [Benchmarking Zero-Shot Reasoning Approaches for Error Detection in Solidity Smart Contracts](https://mlsanity.com/paper/2603.13239/) (2026)
- [A Preliminary Study on Explaining Risk of Code Changes using LLM-Based Prediction Models](https://mlsanity.com/paper/2607.02782/) (2026)

## Abstract (arXiv)

Software analytics has been the subject of considerable recent attention but is yet to receive significant industry traction. One of the key reasons is that software practitioners are reluctant to trust predictions produced by the analytics machinery without understanding the rationale for those predictions. While complex models such as deep learning and ensemble methods improve predictive performance, they have limited explainability. In this paper, we argue that making software analytics models explainable to software practitioners is as important as achieving accurate predictions. Explainability should therefore be a key measure for evaluating software analytics models. We envision that explainability will be a key driver for developing software analytics models that are useful in practice. We outline a research roadmap for this space, building on social science, explainable artificial intelligence and software engineering.