Automatic Traceability Maintenance via Machine Learning Classification

TL;DR

This paper introduces TRAIL, a machine learning-based method for maintaining software traceability links, which outperforms traditional IR techniques in accuracy across multiple datasets.

Contribution

The paper presents a novel machine learning approach called TRAIL for automatic maintenance of traceability links in software systems, leveraging historical knowledge.

Findings

TRAIL outperforms IR techniques in precision, recall, and F-score.

Evaluated on 11 datasets from six systems, demonstrating robustness.

Machine learning effectively maintains traceability links over time.

Abstract

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As the project undergoes maintenance and evolution, new artifacts are added and existing ones are changed, resulting in outdated traceability information.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.