# A systematic literature review on the code smells datasets and validation mechanisms

arXiv: https://arxiv.org/abs/2306.01377 · HTML: https://mlsanity.com/paper/2306.01377/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 150 references that are in the map.
- First posted on arXiv 2 Jun 2023.

## Builds on (most cited first, 9 of 9)

- [Kitsune: An Ensemble of Autoencoders for Online Network Intrusion Detection](https://mlsanity.com/paper/1802.09089/) (2018)
- [N-BaIoT: Network-based Detection of IoT Botnet Attacks Using Deep Autoencoders](https://mlsanity.com/paper/1805.03409/) (2018)
- [Code Smells and Refactoring: A Tertiary Systematic Review of Challenges and Observations](https://mlsanity.com/paper/2004.10777/) (2020)
- [Code Smell Detection using Multilabel Classification Approach](https://mlsanity.com/paper/1902.03222/) (2019)
- [The Technical Debt Dataset](https://mlsanity.com/paper/1908.00827/) (2019)
- [RefDiff: Detecting Refactorings in Version Histories](https://mlsanity.com/paper/1704.01544/) (2017)
- [An ensemble meta-estimator to predict source code testability](https://mlsanity.com/paper/2208.09614/) (2022)
- [A Machine-learning Based Ensemble Method For Anti-patterns Detection](https://mlsanity.com/paper/1903.01899/) (2019)
- [Learning to predict test effectiveness](https://mlsanity.com/paper/2208.09623/) (2022)

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

- [A systematic literature review on source code similarity measurement and clone detection: techniques, applications, and challenges](https://mlsanity.com/paper/2306.16171/) (2023)
- [How Propense Are Large Language Models at Producing Code Smells? A Benchmarking Study](https://mlsanity.com/paper/2412.18989/) (2024)
- [Defending Code Language Models against Backdoor Attacks with Deceptive Cross-Entropy Loss](https://mlsanity.com/paper/2407.08956/) (2024)

## Abstract (arXiv)

The accuracy reported for code smell-detecting tools varies depending on the dataset used to evaluate the tools. Our survey of 45 existing datasets reveals that the adequacy of a dataset for detecting smells highly depends on relevant properties such as the size, severity level, project types, number of each type of smell, number of smells, and the ratio of smelly to non-smelly samples in the dataset. Most existing datasets support God Class, Long Method, and Feature Envy while six smells in Fowler and Beck's catalog are not supported by any datasets. We conclude that existing datasets suffer from imbalanced samples, lack of supporting severity level, and restriction to Java language.