Requirement Tracing using Term Extraction

TL;DR

This paper presents a novel approach for requirements traceability in software development by applying statistical term extraction metrics to generate candidate links, validated on multiple datasets, outperforming traditional TF-IDF methods.

Contribution

The paper introduces a new method using statistical term extraction metrics for requirements tracing, demonstrating improved results over traditional TF-IDF techniques.

Findings

The proposed method outperforms TF-IDF in generating traceability links.

Validation on two datasets confirms the effectiveness of the approach.

Different filters impact the quality of candidate link generation.

Abstract

Requirements traceability is an essential step in ensuring the quality of software during the early stages of its development life cycle. Requirements tracing usually consists of document parsing, candidate link generation and evaluation and traceability analysis. This paper demonstrates the applicability of Statistical Term Extraction metrics to generate candidate links. It is applied and validated using two data sets and four types of filters two for each data set, 0.2 and 0.25 for MODIS, 0 and 0.05 for CM1. This method generates requirements traceability matrices between textual requirements artifacts (such as high-level requirements traced to low-level requirements). The proposed method includes ten word frequency metrics divided into three main groups for calculating the frequency of terms. The results show that the proposed method gives better result when compared with the traditional TF-IDF method.