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As technological advancements reshape employment patterns in IT, real-time job market monitoring is essential to address emerging skills demands and shifts in workforce trends. Amid growing global efforts toward environmental sustainability, Green Software Engineering has gained significant importance. The principles of GSE suggest the use of sustainable technologies and acquiring relevant knowledge. Hence, identifying key green skills and skillsets is critical for shaping education, labour policies and industry strategies, ensuring an equitable and sustainable future in software. In this paper, to identify important green skills and skillsets, we introduce H-TURF, an expansion of the Total Unduplicated Reach and Frequency Analysis algorithm using Hierarchical Cumulative Voting. The suggested algorithm is employed in a dataset of Software Engineering job postings. Our findings reveal the growing integration of green skills with software development, emphasizing their significance in shaping the future workforce. These insights can guide policymakers, educators, and industry leaders in adapting curricula, designing targeted training programs, and refining recruitment strategies to meet evolving market needs.

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H-TURF: Detecting Optimal Green Software Engineering Skillsets Using TURF Analysis and Hierarchical Cumulative Voting

  • 会议论文

  • 发表日期：

    2026年

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  • Vasileios Ntaoulas,
  • Konstantinos Georgiou,
  • Nikolaos Mittas,
  • Lefteris Angelis

摘要

As technological advancements reshape employment patterns in IT, real-time job market monitoring is essential to address emerging skills demands and shifts in workforce trends. Amid growing global efforts toward environmental sustainability, Green Software Engineering has gained significant importance. The principles of GSE suggest the use of sustainable technologies and acquiring relevant knowledge. Hence, identifying key green skills and skillsets is critical for shaping education, labour policies and industry strategies, ensuring an equitable and sustainable future in software. In this paper, to identify important green skills and skillsets, we introduce H-TURF, an expansion of the Total Unduplicated Reach and Frequency Analysis algorithm using Hierarchical Cumulative Voting. The suggested algorithm is employed in a dataset of Software Engineering job postings. Our findings reveal the growing integration of green skills with software development, emphasizing their significance in shaping the future workforce. These insights can guide policymakers, educators, and industry leaders in adapting curricula, designing targeted training programs, and refining recruitment strategies to meet evolving market needs.