Quality control practice based on design artifacts categories
Bibliographic record
Abstract
The current empirical literature suggests that discrepancies between software design artifacts and implementation seems inevitable. The goal of this empirical study is to understand the nature and impact of these discrepancies by a detailed analysis of the design and code artifacts. The case study is based on an object-oriented software development project based on a traditional plan-driven software development process and proposed by an industrial collaborator. Case study results show that the code written is based on different sources. Software components can be created from scratch, added after design, adapted from the redesign of a reused component or implemented from the modification of an existing component. It is found that code components based on design artifacts are indeed victims of erosion once implementation begins. This erosion can be linked to the evolution of the team members' project understanding during implementation. However, the study concludes that this evolution is mostly opportunistic and does not necessarily warrant an evolution toward amelioration. This study proposes a quality control practice based on the category of the implemented components.
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 | 0.066 | 0.151 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".