A New Design Defects Classification: Marrying Detection and Correction

Mahouachi, Rim; Kessentini, Marouane; Ghédira, Khaled

doi:10.1007/978-3-642-28872-2_31

Cited by 9 publications

(5 citation statements)

References 18 publications

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“…They compared the outcomes with other conventional computation techniques and prove the potential of their approach. Kessentini et al [ 32 , 33 , 34 , 35 , 36 , 37 ] stated that due to code smells, the cost of maintenance has increased. They have also described that code smells can be prioritized on the basis of risk.…”

Section: Related Work and Motivationmentioning

confidence: 99%

Software Code Smell Prediction Model Using Shannon, Rényi and Tsallis Entropies

Gupta¹,

Suri

Kumar³

et al. 2018

Entropy

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Abstract:The current era demands high quality software in a limited time period to achieve new goals and heights. To meet user requirements, the source codes undergo frequent modifications which can generate the bad smells in software that deteriorate the quality and reliability of software. Source code of the open source software is easily accessible by any developer, thus frequently modifiable. In this paper, we have proposed a mathematical model to predict the bad smells using the concept of entropy as defined by the Information Theory. Open-source software Apache Abdera is taken into consideration for calculating the bad smells. Bad smells are collected using a detection tool from sub components of the Apache Abdera project, and different measures of entropy (Shannon, Rényi and Tsallis entropy). By applying non-linear regression techniques, the bad smells that can arise in the future versions of software are predicted based on the observed bad smells and entropy measures. The proposed model has been validated using goodness of fit parameters (prediction error, bias, variation, and Root Mean Squared Prediction Error (RMSPE)). The values of model performance statistics (R 2 , adjusted R 2 , Mean Square Error (MSE) and standard error) also justify the proposed model. We have compared the results of the prediction model with the observed results on real data. The results of the model might be helpful for software development industries and future researchers.

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Section: Related Work and Motivationmentioning

confidence: 99%

Software Code Smell Prediction Model Using Shannon, Rényi and Tsallis Entropies

Gupta¹,

Suri

Kumar³

et al. 2018

Entropy

View full text Add to dashboard Cite

show abstract

“…It was also empirically evaluated using Ant 1.6.0 and the results obtained shows the technique can efficiently merge code clone. "A new design defects classification: marrying detection and correcti on": Mahouachi et al [13] proposed a new approach to classify code defects using possible corrections. Unlike other approaches where defect detection and correction steps were addressed separately, this approach introduce a new classification rules that combines code defects identificat ion and correction steps generated using genetic programming.…”

Section: Structural and Semantic Information-based Approachmentioning

confidence: 99%

“…With the combination, each defect group has almost the same refactoring operations that can be applied to fix it. Mahouachi et al [13] claimed th is approach can be used to identify all types of refactorings operations. The approach inputs are set of code smells with the appropriate refactorings to correct them and takes set of quality metrics as well as a comprehensive list of refactoring operations to produce output in the form of rule set.…”

Section: Structural and Semantic Information-based Approachmentioning

confidence: 99%

“…In this case, ru les set that best identify defects set and the corresponding refactoring operations stands the solution to the defect identification and correction problem. Mahouachi et al [13] performed an empirical studies on 6 open-source Java systems and the results showed a high precision and recall correction scores on the different systems used.…”

Section: Structural and Semantic Information-based Approachmentioning

confidence: 99%

“…To this end, co mprehending and maintaining the software systems becomes a difficult, if not impossible tasks. This constitute the reason why software maintenance is considered to be the costliest phases of software develop ment since a considerable cost of development is highly expended on maintenance [13].…”

Section: Introductionmentioning

confidence: 99%

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Analysis of Metric-Based Object-Oriented Code Refactoring Opportunities Identification Approaches

Isong¹,

Dladlu²,

Duke³

et al. 2017

IJITCS

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Abstract-Refactoring is used to improve deteriorated software design, code and their maintainability. In objectoriented (OO) code, before refactoring is performed, its opportunities must be identified and s everal approaches exist this regard. Among the approaches is the software metric-based approach where quality software met rics are used. Therefore, this paper provide analysis of existing emp irical studies that utilized software metrics to identify refactoring opportunities in OO software systems. We performed a co mprehensive analysis on 16 studies to identify the state-of-the-practice. The focal point was on the workings, refactoring activ ities, the programming language and the impact on software quality. The results obtained shows approaches were not unique, each was designed either for a single refactoring activ ity or couple of them, move method and extract class dominated th e refactorings activities, and most approaches were fu lly automated while few were semi-auto mated. Moreover, OO met rics p layed acritical role in both opportunities detection and factoring decisions. Based on the results, it would be beneficial if generic refactoring approach is developed that is capable of identifying needs for all refactoring activities.

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