Uncertainty-wise software anti-patterns detection: A possibilistic evolutionary machine learning approach

Boutaib, Sofien; Elarbi, Maha; Bechikh, Slim; Coello, Carlos A. Coello; Saïd, Lamjed Ben

doi:10.1016/j.asoc.2022.109620

Cited by 4 publications

(3 citation statements)

References 103 publications

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“…Boutaib et al [35] have developed a tool named Bilevel Anti-pattern Detection and Identifcation using Possibilistic Optimized k-NNs (BI-ADIPOK) that is capable of detecting and identifying code smells under certain and uncertain environments where uncertainty occurs at the level of class labels. Uncertainty factors are issued by human experts, which may result in a decrease in the quality of the result produced by detectors.…”

Section: Related Workmentioning

confidence: 99%

Intelligent Mining of Association Rules Based on Nanopatterns for Code Smells Detection

Thessalonica

Nehemiah

Sreejith

et al. 2023

Scientific Programming

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Software maintenance is an imperative step in software development. Code smells can arise as a result of poor design as well as frequent code changes due to changing needs. Early detection of code smells during software development can help with software maintenance. This work focuses on identifying code smells on Java software using nanopatterns. Nanopatterns are method-level code structures that reflect the presence of code smells. Nanopatterns are extracted using a command-line interface based on the ASM bytecode analysis. Class labels are extracted using three tools, namely inFusion, JDeodorant, and iPlasma. Rules are extracted from nanopatterns using the Apriori algorithm and mapped with the extracted class labels. Best rules are selected using the Border Collie Optimization (BCO) algorithm with the accuracy of the k-NN classifier as the fitness function. The selected rules are stored in the rule base to detect code smells. The objective is to detect a maximum number of code smells with a minimum number of rules. Experiments are carried out on Java software, namely jEdit, Nutch, Lucene, and Rhino. The proposed work detects code smells, namely data class, blob, spaghetti code, functional decomposition, and feature envy, with 98.78% accuracy for jEdit, 97.45% for Nutch, 95.58% for Lucene, and 96.34% for Rhino. The performance of the proposed work is competitive with other well-known methods of detecting code smells.

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Section: Related Workmentioning

confidence: 99%

Intelligent Mining of Association Rules Based on Nanopatterns for Code Smells Detection

Thessalonica

Nehemiah

Sreejith

et al. 2023

Scientific Programming

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“…Some recently proposed methods for detecting code smells under uncertainty do not use metrics in the detection process (Boutaib et al, 2022). Therefore, we have not discussed the challenges in them.…”

Section: Related Workmentioning

confidence: 99%

“…Also, another research has been conducted in the direction of combining several anti-pattern detection methods and providing an ensemble approach (Dewangan et al, 2022), which will help to improve it by providing a suitable anti-pattern detection method. In the Bi-ADIPOK method to detect code smells under uncertainty (Boutaib et al, 2022), an attempt has been made to reduce the effect of developers' taste in detecting anti-patterns or uncertainty. This method is a bi-level optimization technique including upper and lower levels.…”

Section: Introductionmentioning

confidence: 99%

SADSE: Search-based Anti-pattern Detection Method by Standard Examples

Kalhor,

keyvanpour,

Salajegheh

et al. 2023

Preprint

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Anti-patterns occur when software design principles are not followed in the software design and coding process. Although there may be no errors or bugs in the software implementation, the quality of the software is reduced. To improve the quality of the software, its anti-patterns should be identified and corrected. Various software engineering criteria are defined and they are used to detect anti-patterns. Choosing the correct and limited combination of criteria as a detection rule is the main purpose of new automatic anti-pattern detection methods. Selection of base examples in these methods for their training is a challenge, which is highly dependent on software experts. In our method, the anti-patterns identified in open-source programs presented as a standard are used for the base examples to reduce the influence of experts’ tastes in detection. The second challenge in all metric-based methods is the complexity of choosing correct and limited criteria in detection rules which in our method by separating the rule tree for every anti-pattern, considered criteria are limited as much as possible. Therefore, we present the SADSE method, which is a new approach to create anti-pattern detection rules using standard base examples and reduce the complexity of searching for suitable software metrics. to identify the detection rules, a new fitness function is defined in the genetic programming, and the rule tree is created to identify each anti-pattern separately to reduce the complexity of selecting software criteria. The analysis of our method by SERC Benchmark shows that its precision and recall on average for all considered anti-patterns are 97% and 96.9%, respectively.

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Actionable code smell identification with fusion learning of metrics and semantics

Yu,

Yang,

Chen

et al. 2024

Science of Computer Programming

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Uncertainty-wise software anti-patterns detection: A possibilistic evolutionary machine learning approach

Cited by 4 publications

References 103 publications

Intelligent Mining of Association Rules Based on Nanopatterns for Code Smells Detection

Intelligent Mining of Association Rules Based on Nanopatterns for Code Smells Detection

SADSE: Search-based Anti-pattern Detection Method by Standard Examples

Actionable code smell identification with fusion learning of metrics and semantics

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