Understanding the Correlation between Code Smells And Software Bugs

Then we examine the impact of using just the number of bugs versus the developers experience as dependent variable in the prediction model.. We measure the impact on prioritization

While we used fine-grained change data in Study 1 to build more accu- rate prediction models in terms of prediction performance, we leverage fine-grained source code changes in

Francesca Arcelli Fontana, Vincenzo Ferme, Alessandro Marino, Bartosz Walter, Pawel Martenka, “Investigating the Impact of Code Smells on System's Quality: An Empirical Study

84 Visualizing and Understanding Code Duplication in Large Software Systems Clone Detection Non- Functional Clone Filtering Filtered Clone Classes Source Code Clone Relation

This paper makes the first, to the best of our knowledge, com- prehensive study of real-world performance bugs based on 109 bugs randomly collected from the bug databases of

In conclusion, the hybrid memory system outperforms cache-based systems because it serves data very efficiently: the strided accesses are served by the LM so the cache hierarchy is

In this paper, we follow previous works on the impact of code smells on development activities [1, 3, 5–7] and revisit the dataset from one particular study [2] to assess the impact

(1) We confirm and complements the findings by Anda [6], by extracting maintainability factors that are important from the software maintainer’s perspective, and (2) Based on