On the diffuseness and the impact on maintainability of code smells: a large scale empirical investigation
Fabio Palomba,Gabriele Bavota,Massimiliano Di Penta,Fausto Fasano,Rocco Oliveto,Andrea De Lucia +5 more
TLDR
The results show that smells characterized by long and/or complex code (e.g., Complex Class) are highly diffused, and that smelly classes have a higher change- and fault-proneness than smell-free classes.Abstract:
Code smells are symptoms of poor design and implementation choices that may hinder code comprehensibility and maintainability. Despite the effort devoted by the research community in studying code smells, the extent to which code smells in software systems affect software maintainability remains still unclear. In this paper we present a large scale empirical investigation on the diffuseness of code smells and their impact on code change- and fault-proneness. The study was conducted across a total of 395 releases of 30 open source projects and considering 17,350 manually validated instances of 13 different code smell kinds. The results show that smells characterized by long and/or complex code (e.g., Complex Class) are highly diffused, and that smelly classes have a higher change- and fault-proneness than smell-free classes.read more
Citations
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Practical Nonparametric Statistics (2nd ed.)
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A GQM-based Method and a Bayesian Approach for the Detection of Code and Design Smells
TL;DR: In this paper, a probabilistic model is proposed to detect occurrences of the Blob antipattern in code and design smells in programs, which can be calibrated using machine learning techniques to offer an improved, context-specific detection.
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Machine learning techniques for code smell detection: A systematic literature review and meta-analysis
TL;DR: There is still room for the improvement of machine learning techniques in the context of code smell detection and it is argued that JRip and Random Forest are the most effective classifiers in terms of performance.
Proceedings ArticleDOI
On the Relation of Test Smells to Software Code Quality
TL;DR: Key results of the study include: tests with smells are more change-and defect-prone, "Indirect Testing", "Eager Test", and "Assertion Roulette" are the most significant smells for change-proneness and, production code is more defect- prone when tested by smelly tests.
Journal ArticleDOI
Fine-grained just-in-time defect prediction
TL;DR: This paper investigates to what extent commits are partially defective; then, a novel fine-grained just-in-time defect prediction model is proposed to predict the specific files, contained in a commit, that are defective; and the extent to which it decreases the effort required to diagnose a defect is evaluated.
References
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