Semantic Textual Similarity in Requirement Specification and Use Case Description based on Sentence Transformer Model

MEIZAN ARTHUR ALFIANTO

Informasi Umum

Kode

23.04.3490

Klasifikasi

006.35 - Natural Language Processing, Computer Science

Jenis

Karya Ilmiah - Skripsi (S1) - Reference

Subjek

Natural Language Processing, Natural Science, Transformers,

Dilihat

312 kali

Informasi Lainnya

Abstraksi

<p>The compatibility between the Use Case Description (UCD) and the Functional Requirements (FR) is essential for the successful development of software. Nevertheless, discrepancies may occur if the UCD does not precisely reflect the intended functionalities specified in the FR. This paper uses a Sentence Transformer Model to evaluate the alignment between the UCD and FR, both written in natural language. The study aims to identify potential discrepancies and ambiguities in the UCD and suggest modifications to better their correspondence with the FR. The Sentence Transformer Model quantifies the degree of alignment between the UCD and FR by analyzing semantic similarity. According to the findings, modifications to the UCD, such as refining terminology, elucidating definitions, and correcting writing errors, can substantially increase semantic similarity with the FR. The Pearson correlation coefficient of 0.70 indicates the correlation between the predicted and the ground truth of semantic similarity is linearly positive. The Spearman rank correlation coefficient value of 0.715 suggests a positive monotonic relationship, with the two text types maintaining their rank of semantic similarity. The low mean squared error (MSE) value of 0.024 demonstrates the model's predictive accuracy for semantic similarity.</p>