The use of data envelopment analysis in the measurement of software development team performance: A quality focused approach
Bibliographic record
Abstract
The linear-programming tool Data Envelopment Analysis (DEA) is applied to the measurement of software development efficiency. New DEA models are constructed and applied to project data provided by a large Canadian Financial Institution (FI). One of the main contributions to the literature is the use of quality metrics, namely client satisfaction survey scores measuring software team performance. And uniquely, size metrics were left out of the analysis. For much of its existence the software industry has been plagued by shortcomings in its ability to consistently develop effective products in an efficient manner. This has resulted in budget and schedule overruns, unmet user needs, unusable applications, and ultimately a loss of business. The technical and scale efficiencies of the software projects are analyzed using two separate DEA models. Characteristics of efficient projects are analyzed, and relationships among quality, efficiency, and other projects factors are investigated.
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How this classification was reachedexpand
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 | 0.010 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.005 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".