Improving Trace Link Recovery Using Semantic Relation Graphs and Spreading Activation

نویسندگان

چکیده

[Context & Motivation] Trace Link Recovery tries to identify and link related existing requirements with each other support further engineering tasks. Existing approaches are mainly based on algebraic Information Retrieval or machine-learning. [Question/Problem] Machine-learning usually demand reasonably large labeled datasets train. Algebraic like distance between tf-idf scores also work smaller without training but limited in considering the context of semantic statements. [Principal Ideas/Results] In this work, we revise our approach that is an explicit representation content as a relation graph uses Spreading Activation answer trace queries over graph. The generates sorted candidate lists fully automated including NLP pipeline transform unrestricted natural language into does not require any external knowledge bases resources. [Contribution] To improve performance, take detailed look at five common adapt structure search algorithm. Depending selected configuration, predictive power strongly varies. With best tested achieves mean average precision 50%, Lag 30% recall 90%.

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ژورنال

عنوان ژورنال: Lecture Notes in Computer Science

سال: 2021

ISSN: ['1611-3349', '0302-9743']

DOI: https://doi.org/10.1007/978-3-030-73128-1_3