# Predicting Defective Lines Using a Model-Agnostic Technique

arXiv: https://arxiv.org/abs/2009.03612 · HTML: https://mlsanity.com/paper/2009.03612/

## Key facts

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

## Built on by (most cited first, 14 of 14)

- [Deep Learning for Android Malware Defenses: a Systematic Literature Review](https://mlsanity.com/paper/2103.05292/) (2021)
- [DeepCVA: Automated Commit-level Vulnerability Assessment with Deep Multi-task Learning](https://mlsanity.com/paper/2108.08041/) (2021)
- [RovoDev Code Reviewer: A Large-Scale Online Evaluation of LLM-based Code Review Automation at Atlassian](https://mlsanity.com/paper/2601.01129/) (2026)
- [On the Use of Fine-grained Vulnerable Code Statements for Software Vulnerability Assessment Models](https://mlsanity.com/paper/2203.08417/) (2022)
- [HalluJudge: A Reference-Free Hallucination Detection for Context Misalignment in Code Review Automation](https://mlsanity.com/paper/2601.19072/) (2026)
- [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)
- [AI for DevSecOps: A Landscape and Future Opportunities](https://mlsanity.com/paper/2404.04839/) (2024)
- [Data and Context Matter: Towards Generalizing AI-based Software Vulnerability Detection](https://mlsanity.com/paper/2508.16625/) (2025)
- [A Systematic Literature Review on Explainability for Machine/Deep Learning-based Software Engineering Research](https://mlsanity.com/paper/2401.14617/) (2024)
- [Predicting Line-Level Defects by Capturing Code Contexts with Hierarchical Transformers](https://mlsanity.com/paper/2312.11889/) (2023)
- [Beyond Fidelity: Explaining Vulnerability Localization of Learning-based Detectors](https://mlsanity.com/paper/2401.02686/) (2024)
- [MulVuln: Enhancing Pre-trained LMs with Shared and Language-Specific Knowledge for Multilingual Vulnerability Detection](https://mlsanity.com/paper/2510.04397/) (2025)
- [Large Language Models for Code Generation from Multilingual Prompts: A Curated Benchmark and a Study on Code Quality](https://mlsanity.com/paper/2607.14816/) (2026)

## Abstract (arXiv)

Defect prediction models are proposed to help a team prioritize source code areas files that need Software QualityAssurance (SQA) based on the likelihood of having defects. However, developers may waste their unnecessary effort on the whole filewhile only a small fraction of its source code lines are defective. Indeed, we find that as little as 1%-3% of lines of a file are defective. Hence, in this work, we propose a novel framework (called LINE-DP) to identify defective lines using a model-agnostic technique, i.e., an Explainable AI technique that provides information why the model makes such a prediction. Broadly speaking, our LINE-DP first builds a file-level defect model using code token features. Then, our LINE-DP uses a state-of-the-art model-agnostic technique (i.e.,LIME) to identify risky tokens, i.e., code tokens that lead the file-level defect model to predict that the file will be defective. Then, the lines that contain risky tokens are predicted as defective lines. Through a case study of 32 releases of nine Java open source systems, our evaluation results show that our LINE-DP achieves an average recall of 0.61, a false alarm rate of 0.47, a top 20%LOC recall of0.27, and an initial false alarm of 16, which are statistically better than six baseline approaches. Our evaluation shows that our LINE-DP requires an average computation time of 10 seconds including model construction and defective line identification time. In addition, we find that 63% of defective lines that can be identified by our LINE-DP are related to common defects (e.g., argument change, condition change). These results suggest that our LINE-DP can effectively identify defective lines that contain common defectswhile requiring a smaller amount of inspection effort and a manageable computation cost.