Competitive Coevolutionary Code-Smells Detection

Boussaa, Mohamed; Kessentini, Wael; Kessentini, Marouane; Bechikh, Slim; Chikha, Soukeina Ben

doi:10.1007/978-3-642-39742-4_6

Cited by 41 publications

(25 citation statements)

References 11 publications

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“…Kessentini et al [74] formulated code smell detection as an optimization problem, leading to the usage of search algorithms to solve it [74], [75], [76], [77].…”

Section: Related Literature On Code Smellsmentioning

confidence: 99%

Toward a Smell-Aware Bug Prediction Model

Palomba

Zanoni

Fontana

et al. 2019

IIEEE Trans. Software Eng.

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Code smells are symptoms of poor design and implementation choices. Previous studies empirically assessed the impact of smells on code quality and clearly indicate their negative impact on maintainability, including a higher bug-proneness of components affected by code smells. In this paper, we capture previous findings on bug-proneness to build a specialized bug prediction model for smelly classes. Specifically, we evaluate the contribution of a measure of the severity of code smells (i.e., code smell intensity) by adding it to existing bug prediction models based on both product and process metrics, and comparing the results of the new model against the baseline models. Results indicate that the accuracy of a bug prediction model increases by adding the code smell intensity as predictor. We also compare the results achieved by the proposed model with the ones of an alternative technique which considers metrics about the history of code smells in files, finding that our model works generally better. However, we observed interesting complementarities between the set of buggy and smelly classes correctly classified by the two models. By evaluating the actual information gain provided by the intensity index with respect to the other metrics in the model, we found that the intensity index is a relevant feature for both product and process metrics-based models. At the same time, the metric counting the average number of code smells in previous versions of a class considered by the alternative model is also able to reduce the entropy of the model. On the basis of this result, we devise and evaluate a smell-aware combined bug prediction model that included product, process, and smell-related features.We demonstrate how such model classifies bug-prone code components with an F-Measure at least 13% higher than the existing state-of-the-art models.

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“…Kessentini et al [74] formulated code smell detection as an optimization problem, leading to the usage of search algorithms to solve it [74], [75], [76], [77].…”

Section: Related Literature On Code Smellsmentioning

confidence: 99%

Toward a Smell-Aware Bug Prediction Model

Palomba

Zanoni

Fontana

et al. 2019

IIEEE Trans. Software Eng.

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show abstract

“…This definition entails a strong relationship between refactoring and internal software quality, i.e., refactoring improves software quality (improves the software internal structure). This has motivated research on bad smell and antipattern detection and on the identification of refactoring oppotunities (Bavota et al, 2013a;Boussaa et al, 2013;Fokaefs et al, 2011;Kessentini et al, 2010;Moha et al, 2010;Palomba et al, 2015;Tsantalis and Chatzigeorgiou, 2009). …”

Section: Introductionmentioning

confidence: 99%

An experimental investigation on the innate relationship between quality and refactoring

Bavota

Lucia

Penta

et al. 2015

Journal of Systems and Software

198

203

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“…The research community has been also actively developing approaches and tools for detecting smells [11], [34], [37], [44], [33], and, whenever possible, triggering refactoring operations. Such tools rely on different types of analysis techniques, such as constraint-based reasoning over metric values [33], [34], static code analysis [44], or analysis of software changes [37].…”

Section: Introductionmentioning

confidence: 99%

When and Why Your Code Starts to Smell Bad

Tufano

Palomba

Bavota

et al. 2015

2015 IEEE/ACM 37th IEEE International Conference on Software Engineering

201

143

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Abstract-In past and recent years, the issues related to managing technical debt received significant attention by researchers from both industry and academia. There are several factors that contribute to technical debt. One of these is represented by code bad smells, i.e., symptoms of poor design and implementation choices. While the repercussions of smells on code quality have been empirically assessed, there is still only anecdotal evidence on when and why bad smells are introduced. To fill this gap, we conducted a large empirical study over the change history of 200 open source projects from different software ecosystems and investigated when bad smells are introduced by developers, and the circumstances and reasons behind their introduction. Our study required the development of a strategy to identify smellintroducing commits, the mining of over 0.5M commits, and the manual analysis of 9,164 of them (i.e., those identified as smellintroducing). Our findings mostly contradict common wisdom stating that smells are being introduced during evolutionary tasks. In the light of our results, we also call for the need to develop a new generation of recommendation systems aimed at properly planning smell refactoring activities.

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Competitive Coevolutionary Code-Smells Detection

Cited by 41 publications

References 11 publications

Toward a Smell-Aware Bug Prediction Model

Toward a Smell-Aware Bug Prediction Model

An experimental investigation on the innate relationship between quality and refactoring

When and Why Your Code Starts to Smell Bad

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