| \begin{table}[t] |
| \centering |
| \small |
| \begin{tabular}{lcc} |
| \toprule |
| \textbf{Metric} & \textbf{LintEval (Full)} & \textbf{LintEval-Lite} \\ |
| \midrule |
| Instances & 13061 & 498 \\ |
| Total lint alerts & 171,662 & 6,302 \\ |
| Unique problems & 3999 & 426 \\ |
| Avg.\ LOC & 61.0 & 62.5 \\ |
| Avg.\ alerts / instance & 13.1 & 12.7 \\ |
| cpp instances & 6165/13061 (47.2\%) & 244/498 (49.0\%) \\ |
| py3 instances & 4221/13061 (32.3\%) & 203/498 (40.8\%) \\ |
| java instances & 2675/13061 (20.5\%) & 51/498 (10.2\%) \\ |
| & & \\ |
| \multicolumn{3}{c}{\textbf{Alert category distribution}}\\ & & \\ |
| error-prone & 18434/171662 (10.7\%) & 839/6302 (13.3\%) \\ |
| performance & 17776/171662 (10.4\%) & 681/6302 (10.8\%) \\ |
| refactor & 17977/171662 (10.5\%) & 419/6302 (6.6\%) \\ |
| style & 115458/171662 (67.3\%) & 4256/6302 (67.5\%) \\ |
| other & 2017/171662 (1.2\%) & 107/6302 (1.7\%) \\ |
| & & \\ |
| \multicolumn{3}{c}{\textbf{Alert-density bins}}\\ & & \\ |
| 1 alert & 777/13061 (5.9\%) & 39/498 (7.8\%) \\ |
| 2--5 alerts & 4285/13061 (32.8\%) & 173/498 (34.7\%) \\ |
| 6--10 alerts & 3013/13061 (23.1\%) & 120/498 (24.1\%) \\ |
| $>$10 alerts & 4986/13061 (38.2\%) & 166/498 (33.3\%) \\ |
| \bottomrule |
| \end{tabular} |
| \caption{Representativeness of \textbf{LintEval-Lite} (498 instances) against the full \textbf{LintEval} benchmark (13,061 instances). The Lite split preserves the language, alert-category, and alert-density distributions of the full benchmark. All expensive model evaluations in the paper are conducted on Lite.} |
| \label{tab:lite_representativeness} |
| \end{table} |
|
|
