Does Socio-Technical Congruence Have an Effect on Software Build Success? A Study of Coordination in a Software Project
Why this work is in the frame
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Bibliographic record
Abstract
Socio-technical congruence is an approach that measures coordination by examining the alignment between the technical dependencies and the social coordination in the project. We conduct a case study of coordination in the IBM Rational Team Concert project, which consists of 151 developers over seven geographically distributed sites, and expect that high congruence leads to a high probability of successful builds. We examine this relationship by applying two congruence measurements: an unweighted congruence measure from previous literature, and a weighted measure that overcomes limitations of the existing measure. We discover that there is a relationship between socio-technical congruence and build success probability, but only for certain build types, and observe that in some situations, higher congruence actually leads to lower build success rates. We also observe that a large proportion of zero-congruence builds are successful, and that socio-technical gaps in successful builds are larger than gaps in failed builds. Analysis of the social and technical aspects in IBM Rational Team Concert allows us to discuss the effects of congruence on build success. Our findings provide implications with respect to the limits of applicability of socio-technical congruence and suggest further improvements of socio-technical congruence to study coordination.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it