Machine learning techniques for code smell detection: A systematic literature review and meta-analysis

MI Azeem, F Palomba, L Shi, Q Wang - Information and Software …, 2019 - Elsevier
Background: Code smells indicate suboptimal design or implementation choices in the
source code that often lead it to be more change-and fault-prone. Researchers defined …

Detecting code smells using machine learning techniques: Are we there yet?

D Di Nucci, F Palomba, DA Tamburri… - 2018 ieee 25th …, 2018 - ieeexplore.ieee.org
Code smells are symptoms of poor design and implementation choices weighing heavily on
the quality of produced source code. During the last decades several code smell detection …

On the diffuseness and the impact on maintainability of code smells: a large scale empirical investigation

F Palomba, G Bavota, M Di Penta, F Fasano… - Proceedings of the 40th …, 2018 - dl.acm.org
Code smells were defined as symptoms of poor design choices applied by programmers
during the development of a software project [2]. They might hinder the comprehensibility …

A survey on software smells

T Sharma, D Spinellis - Journal of Systems and Software, 2018 - Elsevier
Context Smells in software systems impair software quality and make them hard to maintain
and evolve. The software engineering community has explored various dimensions …

Code smell detection by deep direct-learning and transfer-learning

T Sharma, V Efstathiou, P Louridas… - Journal of Systems and …, 2021 - Elsevier
Context: An excessive number of code smells make a software system hard to evolve and
maintain. Machine learning methods, in addition to metric-based and heuristic-based …

When and why your code starts to smell bad (and whether the smells go away)

M Tufano, F Palomba, G Bavota… - IEEE Transactions …, 2017 - ieeexplore.ieee.org
Technical debt is a metaphor introduced by Cunningham to indicate “not quite right code
which we postpone making it right”. One noticeable symptom of technical debt is …

Deeplinedp: Towards a deep learning approach for line-level defect prediction

C Pornprasit… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Defect prediction is proposed to assist practitioners effectively prioritize limited Software
Quality Assurance (SQA) resources on the most risky files that are likely to have post-release …

An experimental investigation on the innate relationship between quality and refactoring

G Bavota, A De Lucia, M Di Penta, R Oliveto… - Journal of Systems and …, 2015 - Elsevier
Previous studies have investigated the reasons behind refactoring operations performed by
developers, and proposed methods and tools to recommend refactorings based on quality …

Deep learning based code smell detection

H Liu, J Jin, Z Xu, Y Zou, Y Bu… - IEEE transactions on …, 2019 - ieeexplore.ieee.org
Code smells are structures in the source code that suggest the possibility of refactorings.
Consequently, developers may identify refactoring opportunities by detecting code smells …

On the relation of test smells to software code quality

D Spadini, F Palomba, A Zaidman… - 2018 IEEE …, 2018 - ieeexplore.ieee.org
Test smells are sub-optimal design choices in the implementation of test code. As reported
by recent studies, their presence might not only negatively affect the comprehension of test …