Java quality assurance by detecting code smells
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
Software inspection is a known technique for improving software quality. It involves carefully examining the code, the design, and the documentation of software and checking these for aspects that are known to be potentially problematic based on past experience. Code smells are a metaphor to describe patterns that are generally associated with bad design and bad programming practices. Originally, code smells are used to find the places in software that could benefit from refactoring. In this paper we investigate how the quality of code can be automatically assessed by checking for the presence of code smells and how this approach can contribute to automatic code inspection. We present an approach for the automatic detection and visualization of code smells and discuss how this approach can be used in the design of a software inspection tool. We illustrate the feasibility of our approach with the development of jCOSMO, a prototype code smell browser that detects and visualizes code smells in JAVA source code. Finally, we show how this tool was applied in a case study.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it