# A Survey on the Evaluation of Clone Detection Performance and Benchmarking

arXiv: https://arxiv.org/abs/2006.15682 · HTML: https://mlsanity.com/paper/2006.15682/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jun 2020.

## Built on by (most cited first, 3 of 3)

- [Unifying the Perspectives of NLP and Software Engineering: A Survey on Language Models for Code](https://mlsanity.com/paper/2311.07989/) (2023)
- [Evaluation of Contrastive Learning with Various Code Representations for Code Clone Detection](https://mlsanity.com/paper/2206.08726/) (2022)
- [Simulation Code Generation for Fluid Systems using Large Language Models: Benchmarking Models and Prompting Strategies](https://mlsanity.com/paper/2607.29389/) (2026)

## Abstract (arXiv)

There are a great many clone detection tools proposed in the literature. In this paper, we investigate the state of clone detection tool evaluation. We begin by surveying the clone detection benchmarks, and performing a multi-faceted evaluation and comparison of their features and capabilities. We then survey the existing clone detection tool and technique publications, and evaluate how the authors of these works evaluate their own tools/techniques. We rank the individual works by how well they measure recall, precision, execution time and scalability. We select the works the best evaluate all four metrics as exemplars that should be considered by future researchers publishing clone detection tools/techniques when designing the empirical evaluation of their tool/technique. We measure statistics on tool evaluation by the authors, and find that evaluation is poor amongst the authors. We finish our investigation into clone detection evaluation by surveying the existing tool comparison studies, including both the qualitative and quantitative studies.