Object‐oriented software development antecedents that influence product bug density
Bibliographic record
Abstract
Purpose Presents the results of a study aimed at investigating the antecedent software development factors that affect quality of final products. By monitoring those causal factors from the early phases of development, one can have a final product of enhanced quality and reduced costs. Design/methodology/approach The study considered an unprecedentedly large number of 30 C++ object‐oriented systems of varied size and application domains, a comprehensive suite of large number of predictive software design or code measures in one study, and compared their results on a common platform. Findings It was found that many of the software design or code measures have a significant positive or negative relationship with quality. Originality/value The value of the paper lies in the fact that it addresses some of the major problems from which most of the studies conducted in this research domain suffer. The objective and justification of this paper are to address these deficiencies, in addition to validating some of the results obtained in earlier studies. Another important value of the paper lies in the fact that, based on the results of the study, the paper enlists useful lessons learned that can provide some practical insight for practitioners and quality managers.
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 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 | 0.003 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".