Systematic Mapping Study of Ensemble Effort Estimation
Bibliographic record
Abstract
Ensemble methods have been used recently for prediction in data mining area in order to overcome the weaknesses of single estimation techniques. This approach consists on combining more than one single technique to predict a dependent variable and has attracted the attention of the software development effort estimation (SDEE) community. An ensemble effort estimation (EEE) technique combines several existing single/classical models. In this study, a systematic mapping study was carried out to identify the papers based on EEE techniques published in the period 2000-2015 and classified them according to five classification criteria: research type, research approach, EEE type, single models used to construct EEE techniques, and rule used the combine single estimates into an EEE technique. Publication channels and trends were also identified. Within the 16 studies selected, homogeneous EEE techniques were the most investigated. Furthermore, the machine learning single models were the most frequently employed to construct EEE techniques and two types of combiner (linear and non-linear) have been used to get the prediction value of an ensemble.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".