Rubbing salt in the wound? A large-scale investigation into the effects of refactoring on security
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
Abstract Software refactoring is a behavior-preserving activity to improve the source code quality without changing its external behavior. Unfortunately, it is often a manual and error-prone task that may induce regressions in the source code. Researchers have provided initial compelling evidence of the relation between refactoring and defects, yet little is known about how much it may impact software security. This paper bridges this knowledge gap by presenting a large-scale empirical investigation into the effects of refactoring on the security profile of applications. We conduct a three-level mining software repository study to establish the impact of 14 refactoring types on (i) security-related metrics, (ii) security technical debt, and (iii) the introduction of known vulnerabilities. The study covers 39 projects and a total amount of 7,708 refactoring commits. The key results show that refactoring has a limited connection to security. However, Inline Method and Extract Interface statistically contribute to improving some security aspects connected to encapsulating security-critical code components. Extract Superclass and Pull Up Attribute refactoring are commonly found in commits violating specific security best practices for writing secure code. Finally, Extract Superclass and Extract & Move Method refactoring tend to occur more often in commits contributing to the introduction of vulnerabilities. We conclude by distilling lessons learned and recommendations for researchers and practitioners.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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