Investigating the Accuracy of Test Code Size Prediction using Use Case Metrics and Machine Learning Algorithms
Bibliographic record
Abstract
Software testing plays a crucial role in software quality assurance. It is, however, a time and resource consuming process. It is, therefore, important to predict as soon as possible the effort required to test software, so that activities can be planned and resources can be optimally allocated. Test code size, in terms of Test Lines Of Code (TLOC), is an important testing effort indicator used in many empirical studies. In this paper, we investigate empirically the early prediction of TLOC for object-oriented software using use case metrics. We used different machine learning algorithms (linear regression, k-NN, Naïve Bayes, C4.5, Random Forest, and Multilayer Perceptron) to build the prediction models. We performed an empirical study using data collected from five Java projects. The use case metrics have been compared to the well-known Use Case Points (UCP) method. Results show that the use case metrics-based approach gives a more accurate prediction of TLOC than the UCP 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 | 0.016 | 0.111 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".