Estimation Models for Software Functional Test Effort
Bibliographic record
Abstract
The International Software Benchmarking and Standards Group (ISBSG) data-base was used to build estimation models for estimating software functional test effort. The analysis of the data revealed three test productivity patterns representing economies or diseconomies of scale and these patterns served as a basis for investigating the characteristics of the corresponding projects. Three groups of projects related to the three different productivity patterns, characterized by domain, team size, elapsed time and rigor of verification and validation carried out during development, were found to be statistically significant. Within each project group, the variations in test effort can be explained, in addition to functional size, by 1) the processes executed during development, and 2) the processes adopted for testing. Portfolios of estimation models were built using combinations of the three independent variables. Performance of the estimation models built using the function point method innovated by the Common Software Measurement International Consortium (COSMIC) known as COSMIC Function Points, and the one advocated by the International Function Point Users Group (IFPUG) known as IFPUG Function Points, were compared to evaluate the impact of these respective sizing methods on test effort estimation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.073 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".