# Automatic Traceability Maintenance via Machine Learning Classification

arXiv: https://arxiv.org/abs/1807.06684 · HTML: https://mlsanity.com/paper/1807.06684/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jul 2018.

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

- [Zero-Shot Learning for Requirements Classification: An Exploratory Study](https://mlsanity.com/paper/2302.04723/) (2023)
- [A Machine Learning Approach for Hierarchical Classification of Software Requirements](https://mlsanity.com/paper/2302.12599/) (2023)
- [Evaluating the Use of LLMs for Documentation to Code Traceability](https://mlsanity.com/paper/2506.16440/) (2025)
- [LinkAnchor: An Autonomous LLM-Based Agent for Issue-to-Commit Link Recovery](https://mlsanity.com/paper/2508.12232/) (2025)
- [A Systematic Literature Review on the Use of Machine Learning in Software Engineering](https://mlsanity.com/paper/2406.13877/) (2024)

## Abstract (arXiv)

Previous studies have shown that software traceability, the ability to link together related artifacts from different sources within a project (e.g., source code, use cases, documentation, etc.), improves project outcomes by assisting developers and other stakeholders with common tasks such as impact analysis, concept location, etc. Establishing traceability links in a software system is an important and costly task, but only half the struggle. As the project undergoes maintenance and evolution, new artifacts are added and existing ones are changed, resulting in outdated traceability information. Therefore, specific steps need to be taken to make sure that traceability links are maintained in tandem with the rest of the project. In this paper we address this problem and propose a novel approach called TRAIL for maintaining traceability information in a system. The novelty of TRAIL stands in the fact that it leverages previously captured knowledge about project traceability to train a machine learning classifier which can then be used to derive new traceability links and update existing ones. We evaluated TRAIL on 11 commonly used traceability datasets from six software systems and compared it to seven popular information Retrieval (IR) techniques including the most common approaches used in previous work. The results indicate that TRAIL outperforms all IR approaches in terms of precision, recall, and F-score.