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Code smells as system-level indicators of maintainability: An empirical study

  • Published:2013-10 Issue:10 Volume:86 Page:2639-2653
  • ISSN:0164-1212
  • Container-title:Journal of Systems and Software
  • language:en
  • Short-container-title:Journal of Systems and Software

Author:

Yamashita Aiko,Counsell Steve

Publisher

Elsevier BV

Subject

Hardware and Architecture,Information Systems,Software

Reference88 articles.

1. An empirical study of the impact of two antipatterns, blob and spaghetti code, on program comprehension;Abbes,2011

2. Analysis of maintenance work categories through measurement;Abran,1991

3. A metric extraction framework based on a high-level description language;Alikacem,2009

4. An empirical validation of object-oriented metrics in two different iterative software processes;Alshayeb;IEEE Transactions on Software Engineering,2003

5. Assessing software system maintainability using structural measures and expert assessments;Anda,2007

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1. CBReT: A Cluster-Based Resampling Technique for dealing with imbalanced data in code smell prediction;Knowledge-Based Systems;2024-02

2. Deep learning approaches for bad smell detection: a systematic literature review;Empirical Software Engineering;2023-05

3. Revisiting Inter-Class Maintainability Indicators;2023 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER);2023-03

4. Code Clone Detection Using Boosting Algorithms;2023 3rd International Conference on Advanced Research in Computing (ICARC);2023-02-23

5. Research Trends, Detection Methods, Practices, and Challenges in Code Smell: SLR;IEEE Access;2023
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