Automated data function extraction from textual requirements by leveraging semi-supervised CRF and language model

Abstract

Function Point Analysis (FPA) provides an objective, comparative measure for size estimation in the early stage of software development. When practicing FPA, analysts typically abide by the following steps: data function (DF) extraction, transactional function extraction, function type classification and adjustment factor determination. However, due to lack of approach and tool support, these steps are usually conduct by human efforts in practice. Related approaches can hardly be applied in the FPA due to the following three challenges, i.e., FPA rule-driven extraction, domain-specific parsing, and expensive labeled resources.