Using Data Envelopment Analysis in software development productivity measurement
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Bibliographic record
Abstract
The ever-increasing size and complexity of software systems make the cost of developing and maintaining software important. Unfortunately, the process of software production has not been particularly well understood. This article helps clarify the relationship between postimplementation function points (FP) and the corresponding development effort for software development projects in a large Canadian bank. Knowledge of this relationship enables evaluations of the productivity of completed projects and, in particular, provides a predictive tool for future projects. The empirical analysis employs a combination of traditional regression models and Data Envelopment Analysis (DEA). The regression analyses show a log-linear relationship between project size and development effort, which is subsequently used in the DEA models. The DEA models identify best performers and use these as benchmarks, but are not limited to the constant returns to scale assumption of the regression analyses and are capable of including the delivery time as a nondiscretionary input. Finally, by including data from the International Software Benchmarking Standards Group (ISBSG) repository in the DEA models, the bank's projects are benchmarked not only against its own best performers but also against what is globally feasible. Copyright © 2006 John Wiley & Sons, Ltd.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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