Intelligent Traceability

Over the past 15 years, Software Traceability researchers have investigated a wide variety of information retrieval and machine learning techniques to automated the traceability process. For the most part, these techniques hit a glass ceiling, achieving high recall at relatively low precision. At the same time, human domain experts are well capable of reasoning about whether two artifacts are related or not.

This work is our quest to mimic the human reasoning process during the trace creation process. To do so we need the ability to dynamically build knowledge bases of related concepts for highly-technical systems engineering domains (in itself a daunting task), and then reasoning over the facts in the knowledge base to determine when a trace link should be created. Recent publications include:
  • Jin Guo, Natawut Monaikul, Cody Plepel, Jane Cleland-Huang: Towards an intelligent domain-specific traceability solution. ASE 2014: 755-766
  • Jin Guo, Jane Cleland-Huang, Brian Berenbach: Foundations for an expert system in domain-specific traceability. RE 2013: 42-51
  • Jin Guo, Natawut Monaikul, Jane Cleland-Huang: Trace links explained: An automated approach for generating rationales. RE 2015: 202-207

Current Researchers

  • Jin Guo, PhD student, Topic: Intelligent Domain-Specific Traceability
  • Natawut Monaikul (MS Researcher at SAREC, now at UIC)
  • Dr. Jane Cleland-Huang (PI),

Related Funding

  • $529,383 SHF: Small: RUI: Generating High Quality Trace Links through Intelligent Composition of Tracing Features, 08/01/13