Cohesion as changeability indicator in object-oriented systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The assessment of the changeability of software systems is of major concern for buyers of large systems found in fast-moving domains such as telecommunications. One way of approaching this problem is to investigate the dependency between the changeability of the software and its design, with the goal of finding design properties that can be used as changeability indicators. In the realm of object oriented systems, experiments have been conducted showing that coupling between classes is such an indicator. However, class cohesion has not been quantitatively studied in respect to changeability. In the research presented, we set out to investigate whether cohesion is correlated with changeability. As cohesion metrics, LCC and LCOM were adopted, and for measuring changeability, a change impact model was used. The data collected on three test systems of industrial size indicate no such correlation. Manual investigation of classes supposed to be weakly cohesive showed that the metrics used do not capture all the facets of class cohesion. We conclude that cohesion metrics such as LCC and LCOM should not be used as changeability indicators.
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it