How Effective Is Automated Trace Link Recovery in Model-Driven Development?

Randell Rasiman, Fabiano Dalpiaz*, Sergio España

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Abstract

[Context and Motivation] Requirements Traceability (RT) aims to follow and describe the lifecycle of a requirement. RT is employed either because it is mandated, or because the product team perceives benefits. [Problem] RT practices such as the establishment and maintenance of trace links are generally carried out manually, thereby being prone to mistakes, vulnerable to changes, time-consuming, and difficult to maintain. Automated tracing tools have been proposed; yet, their adoption is low, often because of the limited evidence of their effectiveness. We focus on vertical traceability that links artifacts having different levels of abstraction. [Results] We design an automated tool for recovering traces between JIRA issues (user stories and bugs) and revisions in a model-driven development (MDD) context. Based on existing literature that uses process and text-based data, we created 123 features to train a machine learning classifier. This classifier was validated via three MDD industry datasets. For a trace recommendation scenario, we obtained an average F 2 -score of 69% with the best tested configuration. For an automated trace maintenance scenario, we obtained an F 0.5 -score of 76%. [Contribution] Our findings provide insights on the effectiveness of state-of-the-art trace link recovery techniques in an MDD context by using real-world data from a large company in the field of low-code development.

Original languageEnglish
Title of host publicationRequirements Engineering: Foundation for Software Quality
Subtitle of host publication28th International Working Conference, REFSQ 2022, Birmingham, UK, March 21–24, 2022, Proceedings
EditorsVincenzo Gervasi, Andreas Vogelsang
Place of PublicationCham
PublisherSpringer
Pages35–51
Number of pages17
Edition1
ISBN (Electronic)978-3-030-98464-9
ISBN (Print)978-3-030-98463-2
DOIs
Publication statusPublished - 9 Mar 2022

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume13216
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Keywords

  • Requirement traceability
  • Trace link recovery
  • Model-driven development
  • Low-code development
  • Machine learning

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