Self organizing maps as a tool for software analysis
Bibliographic record
Abstract
Software measures (metrics) are indicators describing complexity of software products and processes. By their very nature, software measures give rise to a number of complex and highly dimensional data (patterns) that attempt to provide some useful insights into the very nature of the software systems. Such findings help to investigate and quantify the key properties of the systems such as their reliability, maintainability, readability, etc. In this study, self-organizing maps (SOMs) are considered as a vehicle for analysis of multidimensional data. From the functional point of view, SOMs are neural networks that map highly dimensional data into low dimensional (usually two-dimensional) space in such a way that the topology of the data is preserved. The construction of a map is realized through a process of unsupervised learning. Owing to the visualization capabilities arising in the two-dimensional space, one can visualize a structure in the original data and identify potential clusters as well as their size (compactness) and mutual distribution in the map. In this study, analysis of software data concerning JAVA classes is being carried out.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".