Dynamic and Automatic Feedback-Based Threshold Adaptation for Code Smell Detection

Liu, Hui; Liu, Qiurong; Niu, Zhendong; Yang, Liu

doi:10.1109/tse.2015.2503740

Cited by 32 publications

(26 citation statements)

References 35 publications

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“…Most architectural smell detection techniques use dynamic source code analysis and belong to the paradigm of SOA, relying on DSL that helps generate algorithms along with a service interface, using FraSCAti 11 runtime support for static, dynamic, and lexical analyses. The dynamic source code analysis also used dynamic threshold adaptation instead of fixed thresholds for smell detection . A genetic algorithm along with tuning machine is applied to check the results with inferred settings, default settings, and with a tuning algorithm .…”

Section: Resultsmentioning

confidence: 99%

“…The dynamic source code analysis also used dynamic threshold adaptation instead of fixed thresholds for smell detection . A genetic algorithm along with tuning machine is applied to check the results with inferred settings, default settings, and with a tuning algorithm . We found eight studies that reported dynamic source code analysis for OO.…”

Section: Resultsmentioning

confidence: 99%

“…§ § § https://marketplace.eclipse.org/content/jdeodorant ¶ ¶ ¶ checkstyle.sourceforge.net/ ### https://sourceforge.net/projects/jason/files/jason/version%201.3.10/ |||||| www.eclipse.org/downloads/packages/release/Kepler/SR A genetic algorithm along with tuning machine is applied to check the results with inferred settings, default settings, and with a tuning algorithm. 99 We found eight studies that reported dynamic source code analysis for OO. The complete information about the different techniques implemented using dynamic source code analysis is presented in Table 13.…”

Section: Csmentioning

confidence: 99%

“…Mining the source code through SVN The SrcML ***** toolkit and a MARKOS † † † † † code analyzer are used 99…”

Section: Csmentioning

confidence: 99%

“…Frequency of smell terms in studies ,41,52,53,58,61,63,68,69,72,[88][89][90][91][92][93][94][95][96][97][98][99] …”

mentioning

confidence: 99%

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A systematic literature review on the detection of smells and their evolution in object‐oriented and service‐oriented systems

et al. 2018

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Summary This systematic literature review paper investigates the key techniques employed to identify smells in different paradigms of software engineering from object‐oriented (OO) to service‐oriented (SO). In this review, we want to identify commonalities and differences in the identification of smells in OO and SO systems. Our research method relies on an automatic search from the relevant digital libraries to find the studies published since January 2000 on smells until December 2017. We have conducted a pilot and author‐based search that allows us to select the 78 most relevant studies after applying inclusion and exclusion criteria. We evaluated the studies based on the smell detection techniques and the evolution of different methodologies in OO and SO. Among the 78 relevant studies selected, we have identified six different studies in which linguistic source code analysis received less attention from the researchers as compared to the static source code analysis. Smells like the yo‐yo problem, unnamed coupling, intensive coupling, and interface bloat received considerably less attention in the literature. We also identified a catalog of 30 smells infrequently reported for SO systems and that require further attention. Moreover, a suite of 20 smells reported for SO systems can also be detected using static source code metrics in OO. Finally, our review highlighted three major research trends that are further subdivided into 20 research patterns initiating the detection of smells toward their correction.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Csmentioning

confidence: 99%

“…Mining the source code through SVN The SrcML ***** toolkit and a MARKOS † † † † † code analyzer are used 99…”

Section: Csmentioning

confidence: 99%