Using digital traces to analyze software work: skills, careers and programming languages
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866911577635028992 |
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| author | Feng, Xiangnan Wachs, Johannes Daniotti, Simone Neffke, Frank |
| author_facet | Feng, Xiangnan Wachs, Johannes Daniotti, Simone Neffke, Frank |
| contents | Recent waves of technological transformation are reshaping work in uncertain and hard-to-predict ways. However, jobs at the forefront of the digitizing economy offer an early glimpse of these changes and leave rich activity traces. We exploit such traces in tens of millions of Question and Answer posts on Stack Overflow for the creation of a fine-grained taxonomy of software skills to analyze human capital in the global software industry. Constructing a software skill space that maps relations among these skills reveals that real-world software jobs demand highly coherent skill sets and that programmers learn through a process of related diversification. The latter process often leads to the acquisition of lower-value skills. However, when programmers use Python they preferentially target higher-value skills, offering a potential explanation for Python's successful rise as a dominant general purpose language. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_03581 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Using digital traces to analyze software work: skills, careers and programming languages Feng, Xiangnan Wachs, Johannes Daniotti, Simone Neffke, Frank General Economics Economics Computers and Society Recent waves of technological transformation are reshaping work in uncertain and hard-to-predict ways. However, jobs at the forefront of the digitizing economy offer an early glimpse of these changes and leave rich activity traces. We exploit such traces in tens of millions of Question and Answer posts on Stack Overflow for the creation of a fine-grained taxonomy of software skills to analyze human capital in the global software industry. Constructing a software skill space that maps relations among these skills reveals that real-world software jobs demand highly coherent skill sets and that programmers learn through a process of related diversification. The latter process often leads to the acquisition of lower-value skills. However, when programmers use Python they preferentially target higher-value skills, offering a potential explanation for Python's successful rise as a dominant general purpose language. |
| title | Using digital traces to analyze software work: skills, careers and programming languages |
| topic | General Economics Economics Computers and Society |
| url | https://arxiv.org/abs/2504.03581 |