A Survey on Co-occurrences of Code and Test Smells
Bibliographic record
Abstract
The co-occurrence of code and test smells can have detrimental effects on software systems, causing developers more difficulty in understanding and changing code in which the smells appear. Researchers have explored code smell co-occurrences and analyzed which smells occur together, the languages and domains of the software systems in which bad smells occurred, and the effects of co-occurring code smells. This paper presents the results of a systematic mapping study we conducted to summarize the research on the subject of code smell co-occurrences. We reviewed 14 papers, identifying various aspects, including the code smells that researchers investigated, the programming languages of the software systems containing the smells, the code and test smells that often occurred together, the tools used to detect smells, the techniques employed to determine co-occurrences, as well as the similarities and differences between studies on the topic. Our results present an overview of the code smells phenomenon, including information such as which code and test smells occur with others the most, as well as how frequently code and test smells are mentioned across the studies. The following implications can be derived from our study: identifying programming languages and code smell categories that have not yet been fully explored, determining standards for code and test smells based on attributes and descriptions of smells, and prioritizing certain code and test smell co-occurrences based on their frequency across the studies we surveyed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.028 | 0.029 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".