TEMPORAL SOFTWARE CHANGE PREDICTION USING NEURAL NETWORKS
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
Predicting changes in software entities (e.g. source files) that are more likely to change can help in the efficient allocation of the project resources. A powerful change prediction tool can improve maintenance and evolution tasks in software projects in terms of cost and time factors. The vast majority of research works have focused on determining "where" the most change-prone entities are, and "how" the change will be propagated through a system. This article suggests that knowing "when" changes are likely to happen can also provide another consideration for managers and developers to plan their maintenance activities more efficiently. To address this issue, a Neural Network-based Temporal Change Prediction (NNTCP) framework is proposed. This novel framework indicates "where" the changes are likely to happen (i.e. hot spots), and then adds the time dimension to predict "when" it may occur. In proving this concept, the NNTCP framework is applied in two large-scale open source software projects, Mozilla and Eclipse. The results obtained indicate NNTCP can predict the occurrence of several future revisions with reasonable performance.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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