Source and Test Code Size Prediction - A Comparison between Use Case Metrics and Objective Class Points
Bibliographic record
Abstract
Source code size, in terms of SLOC (Source Lines of Code), is an important parameter of many parametric software development effort estimation methods. Moreover, test code size, in terms of TLOC (Test Lines of Code), has been used in many studies to indicate the effort involved in testing. This paper aims at comparing empirically the Use Case Metrics (UCM) method, a use case model based method that we proposed in previous work, and the Objective Class Points (OCP) method in terms of early prediction of SLOC and TLOC for object-oriented software. We used both simple and multiple linear regression methods to build the prediction models. An empirical comparison, using data collected from four open source Java projects, is reported in the paper. Overall, results provide evidence that the multiple linear regression model, based on the combination of the use case metrics, is more accurate in terms of early prediction of SLOC and TLOC than: (1) the simple linear regression models based on each use case metric, and (2) the simple linear regression model based on the OCP method.
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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 | 0.008 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".