Title: Named entity recognition for function point descriptions in software cost estimation processes

Authors: Boyan Zhao; Xiaofei Zou; Shijie Xin; Di Liu

Addresses: Jilin Institute of Electronic Information Products Inspection, No. 1381 Xinmin Street, Chaoyang District, Changchun City, Jilin Province, China ' Jilin Institute of Electronic Information Products Inspection, No. 1381 Xinmin Street, Chaoyang District, Changchun City, Jilin Province, China ' Jilin Institute of Electronic Information Products Inspection, No. 1381 Xinmin Street, Chaoyang District, Changchun City, Jilin Province, China ' Jilin Institute of Electronic Information Products Inspection, No. 1381 Xinmin Street, Chaoyang District, Changchun City, Jilin Province, China

Abstract: With the advancement of software technology, the industry's informatisation level has improved, but the growing size and complexity of software have raised costs. Consequently, assessing software project costs early is crucial. Function point analysis, the primary method for cost evaluation, quantifies functional elements like external data inputs and outputs to measure software size from the user's perspective. However, it heavily relies on manual effort, especially in extracting function point descriptions, leading to errors and inefficiency. This paper proposes an entity recognition model to address these challenges, integrating a BiLSTM-CRF framework with CNN layers and hierarchical learning. A domain-specific dictionary is developed to enhance the model's performance. Experimental results show that the proposed method outperforms BERT by improving accuracy by 0.42% and recall by 1.04%. The method achieves 95.37% accuracy in entity recognition for a sensor data system, demonstrating its effectiveness and reliability in software cost evaluation.

Keywords: software cost evaluation; named entity recognition; convolutional neural network; CNN; bidirectional long-short memory network; hierarchical learning.

DOI: 10.1504/IJSNET.2025.148196

International Journal of Sensor Networks, 2025 Vol.48 No.4, pp.255 - 267

Received: 06 Feb 2025
Accepted: 16 Feb 2025
Published online: 29 Aug 2025 *

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