SpaceX: Exploring metrics with the SPACE model for developer productivity
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909925508120576 |
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| author | Kaul, Sanchit Nhu, Kevin Eissayou, Jason Eser, Ivan Borup, Victor |
| author_facet | Kaul, Sanchit Nhu, Kevin Eissayou, Jason Eser, Ivan Borup, Victor |
| contents | This empirical investigation elucidates the limitations of deterministic, unidimensional productivity heuristics by operationalizing the SPACE framework through extensive repository mining. Utilizing a dataset derived from open-source repositories, the study employs rigorous statistical methodologies including Generalized Linear Mixed Models (GLMM) and RoBERTa-based sentiment classification to synthesize a holistic, multi-faceted productivity metric. Analytical results reveal a statistically significant positive correlation between negative affective states and commit frequency, implying a cycle of iterative remediation driven by frustration. Furthermore, the investigation has demonstrated that analyzing the topology of contributor interactions yields superior fidelity in mapping collaborative dynamics compared to traditional volume-based metrics. Ultimately, this research posits a Composite Productivity Score (CPS) to address the heterogeneity of developer efficacy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_20955 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SpaceX: Exploring metrics with the SPACE model for developer productivity Kaul, Sanchit Nhu, Kevin Eissayou, Jason Eser, Ivan Borup, Victor Software Engineering Artificial Intelligence This empirical investigation elucidates the limitations of deterministic, unidimensional productivity heuristics by operationalizing the SPACE framework through extensive repository mining. Utilizing a dataset derived from open-source repositories, the study employs rigorous statistical methodologies including Generalized Linear Mixed Models (GLMM) and RoBERTa-based sentiment classification to synthesize a holistic, multi-faceted productivity metric. Analytical results reveal a statistically significant positive correlation between negative affective states and commit frequency, implying a cycle of iterative remediation driven by frustration. Furthermore, the investigation has demonstrated that analyzing the topology of contributor interactions yields superior fidelity in mapping collaborative dynamics compared to traditional volume-based metrics. Ultimately, this research posits a Composite Productivity Score (CPS) to address the heterogeneity of developer efficacy. |
| title | SpaceX: Exploring metrics with the SPACE model for developer productivity |
| topic | Software Engineering Artificial Intelligence |
| url | https://arxiv.org/abs/2511.20955 |