Journal ArticleDOI
Quantifying the Effect of Code Smells on Maintenance Effort
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TLDR
To reduce maintenance effort, a focus on reducing code size and the work practices that limit the number of changes may be more beneficial than refactoring code smells.Abstract:
Context: Code smells are assumed to indicate bad design that leads to less maintainable code. However, this assumption has not been investigated in controlled studies with professional software developers. Aim: This paper investigates the relationship between code smells and maintenance effort. Method: Six developers were hired to perform three maintenance tasks each on four functionally equivalent Java systems originally implemented by different companies. Each developer spent three to four weeks. In total, they modified 298 Java files in the four systems. An Eclipse IDE plug-in measured the exact amount of time a developer spent maintaining each file. Regression analysis was used to explain the effort using file properties, including the number of smells. Result: None of the 12 investigated smells was significantly associated with increased effort after we adjusted for file size and the number of changes; Refused Bequest was significantly associated with decreased effort. File size and the number of changes explained almost all of the modeled variation in effort. Conclusion: The effects of the 12 smells on maintenance effort were limited. To reduce maintenance effort, a focus on reducing code size and the work practices that limit the number of changes may be more beneficial than refactoring code smells.read more
Citations
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Journal ArticleDOI
When and Why Your Code Starts to Smell Bad (and Whether the Smells Go Away)
Michele Tufano,Fabio Palomba,Gabriele Bavota,Rocco Oliveto,Massimiliano Di Penta,Andrea De Lucia,Denys Poshyvanyk +6 more
TL;DR: The findings mostly contradict common wisdom stating that smells are being introduced during evolutionary tasks, and call for the need to develop a new generation of recommendation systems aimed at properly planning smell refactoring activities.
Journal ArticleDOI
Comparing and experimenting machine learning techniques for code smell detection
TL;DR: The largest experiment of applying machine learning algorithms to code smells to the best of the authors' knowledge concludes that the application of machine learning to the detection of these code smells can provide high accuracy (>96 %), and only a hundred training examples are needed to reach at least 95 % accuracy.
Proceedings ArticleDOI
When and why your code starts to smell bad
Michele Tufano,Fabio Palomba,Gabriele Bavota,Rocco Oliveto,Massimiliano Di Penta,Andrea De Lucia,Denys Poshyvanyk +6 more
TL;DR: The findings mostly contradict common wisdom, showing that most of the smell instances are introduced when an artifact is created and not as a result of its evolution, and at the same time, 80 percent of smells survive in the system.
Journal ArticleDOI
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
TL;DR: 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.
Journal ArticleDOI
Evolution of software in automated production systems
TL;DR: In this article, the authors provide an interdisciplinary survey on challenges and state-of-the-art in evolution of automated production systems, and summarize future research directions to address the challenges of evolution in automated production system.
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