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INDIRECT: Intent-driven Requirements-to-code Traceability

Hey, Tobias

Traceability information is important for software maintenance, change impact analysis, software reusability, and other software engineering tasks. However, manually generating this information is costly. State-of-the-art automation approaches suffer from their imprecision and domain dependence. I propose INDIRECT, an intent-driven approach to automated requirements-to-code traceability. It combines natural language understanding and program analysis to generate intent models for both requirements and source code. Then INDIRECT learns a mapping between the two intent models. I expect that using the two intent models as base for the mapping poses a more precise and general approach. The intent models contain information such as the semantics of the statements, underlying concepts, and relations between them. The generation of the requirements intent model is divided into smaller subtasks by using an iterative natural language understanding. Likewise, the intent model for source code is built iteratively by identifying and understanding semantically related source code chunks.

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DOI: 10.1109/ICSE-Companion.2019.00078
Zugehörige Institution(en) am KIT Institut für Programmstrukturen und Datenorganisation (IPD)
Publikationstyp Proceedingsbeitrag
Publikationsjahr 2019
Sprache Englisch
Identifikator KITopen-ID: 1000095737
Erschienen in Proceedings of the 41st International Conference on Software Engineering: Companion Proceedings (ICSE '19), Montreal, Quebec, Canada — May 25 - 31, 2019
Veranstaltung 41st International Conference on Software Engineering (ICSE 2019), Montreal, Kanada, 25.05.2019 – 31.05.2019
Verlag IEEE Press, Piscataway (NJ)
Seiten 190–191
Schlagwörter natural language understanding, program analysis, requirements traceability, traceability link recovery
Nachgewiesen in Scopus
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