Software smell detection techniques: A systematic literature review. Issue 3 (15th October 2020)

• Record Type: Journal Article
• Authors: AbuHassan, Amjad; Alshayeb, Mohammad; Ghouti, Lahouari
• Is Part Of: Journal of software. Volume 33:Issue 3(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-10-15
• Subjects: antipatterns -- code refactoring -- code smells -- design smells -- smell detection -- software smells; Software engineering -- Periodicals; Computer software -- Development -- Periodicals; Software maintenance -- Periodicals; 005.1
• DOI: 10.1002/smr.2320
• Languages: English
• ISSNs: 2047-7473

Abstract

Abstract: Software smells indicate design or code issues that might degrade the evolution and maintenance of software systems. Detecting and identifying these issues are challenging tasks. This paper explores, identifies, and analyzes the existing software smell detection techniques at design and code levels. We carried out a systematic literature review (SLR) to identify and collect 145 primary studies related to smell detection in software design and code. Based on these studies, we address several questions related to the analysis of the existing smell detection techniques in terms of abstraction level (design or code), targeted smells, used metrics, implementation, and validation. Our analysis identified several detection techniques categories. We observed that 57% of the studies did not use any performance measures, 41% of them omitted details on the targeted programing language, and the detection techniques were not validated in 14% of these studies. With respect to the abstraction level, only 18% of the studies addressed bad smell detection at the design level. This low coverage urges for more focus on bad smell detection at the design level to handle them at early stages. Finally, our SLR brings to the attention of the research community several opportunities for future research. Abstract : Identified and collected 145 primary studies (PS) related to smell detection in software design and code. Identified several detection techniques categories. Observed that 57% of the studies did not use any performace measures. 41% of the PSs omitted details on the targeted programing language. Detection techniques were not validated in 14% of the PSs. Only 18% of the studies addressed bad smell detection at the design level. Identified a number of open issues.