Bibliographic record
Abstract
DACOS - DAtaset of COde Smells The dataset offers annotated code snippets for three code smells— multifaceted abstraction, complex method, and long parameter list. In addition to a manually annotated dataset on potentially subjective snippets, we offer a larger set of snippets containing the snippets that are either definitely benign or smelly. The upload contains three files : DACOSMain.sql - This is the SQL file containing the main DACOS dataset. DACOSExtended.sql - This is the SQL file containing the Extended DACOS dataset. Files.zip - The zip file containing all the source code files. Required Software The dataset is created in MySQL. Hence a local or remote installation of MySQL is needed with privileges to create and modify schemas. Importing the Dataset The dataset is a self-contained SQL file. To import the dataset, run the following command: mysql -u username -p database_name < DACOSMain.sql mysql -u username -p database_name < DACOSExtended.sql Understanding the Datasets Both the datasets differ in architecture. The main dataset contains a table named annotations that contains every annotation collected from users. The sample table contains the samples presented to the user for annotation. The class_metrics and method_metrics contain the tables for class and method metrics respectively. These were used to filter samples that are likely to contain smells and hence can be shown to users. The extended dataset is created by selecting samples that are below or above the selected metric range for each smell. Hence, these samples are definitely smelly or benign. The extended version of the dataset does not contain a table for annotation since they were not presented to user. It instead has an 'entry' table where each sample is classified according to the smell it contains. The codes for identifying smells are as below: Condition smell Id Multifaceted Abstraction Present 1 Multifaceted Abstraction not detected 4 Long Parameter List Present 2 Long Parameter List Absent 5 Complex Method Present 3 Complex Method Absent 6
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 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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.045 | 0.068 |
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".