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<p>Accurate cost estimation is crucial for the success of agile software development projects. However, it remains a significant challenge due to the lack of cost drivers specific to agile methodologies. This study addresses this gap by identifying and empirically validating the key factors influencing cost estimation in agile development. This research employs a dual-method approach that combines a systematic literature review (SLR) approach with empirical research. The SLR analyzed 25 studies published over the past decade from 4 major data repositories to identify potential cost drivers. As a result, 55 cost drivers were found and classified into four aspects: project, people, process, and product, of which 19 factors had a higher-than-average importance ratio. To verify these factors, this study collected 150 questionnaires from 12 countries and regions. Respondents evaluated these 55 driving factors based on project experience and considered 22 to have a higher-than-average importance ratio. Therefore, this research used the Pearson correlation test and Spearman correlation test to analyze the correlation between the findings of SLR and the empirical study. The test results show that the correlation coefficients were 0.710 and 0.834, respectively, and the P values were both less than 0.001. It can be considered that there was a strong positive correlation between them. Based on the above research, this research analyzed the factors that were above average importance ratio in both approaches, finally obtained 17 key cost drivers, and elaborated on their connotation and understanding. This study fills an important gap by systematically identifying and empirically validating agile development-specific cost drivers. This research makes a significant contribution to cost estimation for agile development and is an indispensable step in using mathematical methods such as optimization algorithms and machine learning algorithms to conduct cost estimation research, and can further improve the accuracy of cost estimation.</p>

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Identification and empirical investigation of factors influencing cost estimation in Agile development

  • 期刊论文

  • 发表日期：

    2026年5月2日

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  • Xiaoyan Zhao,
  • Zulkefli Mansor,
  • Rozilawati Razali,
  • Mohd Zakree Ahmad Nazri,
  • Liangyu Li,
  • Xuwei Guo

摘要

Accurate cost estimation is crucial for the success of agile software development projects. However, it remains a significant challenge due to the lack of cost drivers specific to agile methodologies. This study addresses this gap by identifying and empirically validating the key factors influencing cost estimation in agile development. This research employs a dual-method approach that combines a systematic literature review (SLR) approach with empirical research. The SLR analyzed 25 studies published over the past decade from 4 major data repositories to identify potential cost drivers. As a result, 55 cost drivers were found and classified into four aspects: project, people, process, and product, of which 19 factors had a higher-than-average importance ratio. To verify these factors, this study collected 150 questionnaires from 12 countries and regions. Respondents evaluated these 55 driving factors based on project experience and considered 22 to have a higher-than-average importance ratio. Therefore, this research used the Pearson correlation test and Spearman correlation test to analyze the correlation between the findings of SLR and the empirical study. The test results show that the correlation coefficients were 0.710 and 0.834, respectively, and the P values were both less than 0.001. It can be considered that there was a strong positive correlation between them. Based on the above research, this research analyzed the factors that were above average importance ratio in both approaches, finally obtained 17 key cost drivers, and elaborated on their connotation and understanding. This study fills an important gap by systematically identifying and empirically validating agile development-specific cost drivers. This research makes a significant contribution to cost estimation for agile development and is an indispensable step in using mathematical methods such as optimization algorithms and machine learning algorithms to conduct cost estimation research, and can further improve the accuracy of cost estimation.