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- W2897987981 abstract "Requirements traceability provides critical support throughout all phases of software engineering. Automated tracing based on information retrieval (IR) reduces the effort required to perform a manual trace. Unfortunately, IR-based trace recovery suffers from low precision due to polysemy, which refers to the coexistence of multiple meanings for a term appearing in different requirements. Latent semantic indexing (LSI) has been introduced as a method to tackle polysemy, as well as synonymy. However, little is known about the scope and significance of polysemous terms in requirements tracing. While quantifying the effect, we present a novel method based on artificial neural networks (ANN) to enhance the capability of automatically resolving polysemous terms. The core idea is to build an ANN model which leverages a term's highest-scoring coreferences in different requirements to learn whether this term has the same meaning in those requirements. Experimental results based on 2 benchmark datasets and 6 long-lived open-source software projects show that our approach outperforms LSI on identifying polysemous terms and hence increasing the precision of automated tracing." @default.
- W2897987981 created "2018-10-26" @default.
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- W2897987981 date "2018-08-01" @default.
- W2897987981 modified "2023-10-16" @default.
- W2897987981 title "Enhancing Automated Requirements Traceability by Resolving Polysemy" @default.
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- W2897987981 doi "https://doi.org/10.1109/re.2018.00-53" @default.
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