ATLaS: A Framework for Traceability Links Recovery Combining Information Retrieval and Semi-Supervised Techniques

Abstract

Current Model-Based Systems Engineering (MBSE) practices to design and implement complex systems require modeling and analysis based on many representations: structure, dynamics, safety, security, etc. This induces a large volume of overlapping heterogeneous artefacts which are subject to frequent changes during the project life cycle. In order to verify and validate systems requirements and ensure that models meet user's needs, MBSE techniques shall rely on consistent traceability management. In this paper, we investigate the benefits of Information Retrieval (IR) techniques and the latest advances in Natural Language Processing (NLP) approaches to suggest stakeholders with candidate semantic links generated from the processing of structured and unstructured contents. We illustrate our approach called ATLaS (Aggregation Trace Links Support) through an application on the design and analysis of a mobility service gathering several industrial partners. We provide an empirical evaluation regarding its limitations as part of an industrial MBSE process. Most importantly, we highlight how our method drastically reduces the false positive links generated compared to current IR techniques. The results obtained suggest a good synergy between the presented approach and MBSE techniques.

Applications of Natural Language Processing in Software Traceability: A Systematic Mapping Study

“…Bi-directional traceability (Salih et al, 2021), linking to UML diagrams (Arunthavanathan et al, 2016;Salih and Sahraoui, 2018;Kchaou et al, 2019;Salih et al, 2021;Panichella et al, 2015;Kchaou. et al, 2017;Effa Bella et al, 2018), fuzzy logic (Thommazo et al, 2013), reducing false positives (Effa Bella et al, 2019;Capobianco et al, 2013b), are some examples of how NLP was used during the REQ phase.…”

“…),Wang et al (2018), EffaBella et al (2019),Panichella et al (2015),Csuvik et al (2019a),Pauzi and Capiluppi (2020),Huang et al (2016),Mahmoud and Niu (2015),Arora et al (2015),Khatiwada et al. (2017) 5.…”