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.
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Emma Effa Bella, Stephen Creff, Marie-Pierre Gervais, Reda Bendraou. ATLaS: A Framework for Traceability Links Recovery Combining Information Retrieval and Semi-supervised Techniques. 23RD IEEE INTERNATIONAL EDOC CONFERENCE - THE ENTERPRISE COMPUTING CONFERENCE, Oct 2019, Paris, France. ⟨hal-02201469⟩

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