A comparison of machine learning algorithms on design smell detection using balanced and imbalanced dataset: A study of God class

[1]  Raed Shatnawi,et al.  Deriving metrics thresholds using log transformation , 2015, J. Softw. Evol. Process..

[2]  Satwinder Singh,et al.  Investigating the Role of Code Smells in Preventive Maintenance , 2018 .

[3]  Jochen Kreimer,et al.  Adaptive Detection of Design Flaws , 2005, LDTA@ETAPS.

[4]  Foutse Khomh,et al.  BDTEX: A GQM-based Bayesian approach for the detection of antipatterns , 2011, J. Syst. Softw..

[5]  B. Matthews.  Comparison of the predicted and observed secondary structure of T4 phage lysozyme. , 1975, Biochimica et biophysica acta.

[6]  Mika Mäntylä,et al.  Comparing and experimenting machine learning techniques for code smell detection , 2015, Empirical Software Engineering.

[7]  Mohammad Alshayeb,et al.  Bad Smell Detection Using Machine Learning Techniques: A Systematic Literature Review , 2020 .

[8]  Yann-Gaël Guéhéneuc,et al.  SMURF: A SVM-based Incremental Anti-pattern Detection Approach , 2012, 2012 19th Working Conference on Reverse Engineering.

[9]  Yann-Gaël Guéhéneuc,et al.  Fingerprinting design patterns , 2004, 11th Working Conference on Reverse Engineering.

[10]  Per Runeson,et al.  Guidelines for conducting and reporting case study research in software engineering , 2009, Empirical Software Engineering.

[11]  Senén Barro,et al.  Do we need hundreds of classifiers to solve real world classification problems? , 2014, J. Mach. Learn. Res..

[12]  M.J. Munro,et al.  Product Metrics for Automatic Identification of "Bad Smell" Design Problems in Java Source-Code , 2005, 11th IEEE International Software Metrics Symposium (METRICS'05).

[13]  S. Counsell,et al.  Size and Frequency of Class Change from a Refactoring Perspective , 2007, Third International IEEE Workshop on Software Evolvability 2007.

[14]  Foutse Khomh,et al.  IDS: An Immune-Inspired Approach for the Detection of Software Design Smells , 2010, 2010 Seventh International Conference on the Quality of Information and Communications Technology.

[15]  Andrew P. Bradley,et al.  The use of the area under the ROC curve in the evaluation of machine learning algorithms , 1997, Pattern Recognit..

[16]  Francesca Arcelli Fontana,et al.  Automatic detection of bad smells in code: An experimental assessment , 2012, J. Object Technol..

[17]  Yann-Gaël Guéhéneuc,et al.  Support vector machines for anti-pattern detection , 2012, 2012 Proceedings of the 27th IEEE/ACM International Conference on Automated Software Engineering.

[18]  Yoshikazu Ueda,et al.  Detecting Defects in Object Oriented Designs Using Design Metrics , 2006, JCKBSE.

[19]  Yann-Gaël Guéhéneuc,et al.  Decor: a tool for the detection of design defects , 2007, ASE.

[20]  Ladan Tahvildari,et al.  Journal of Software Maintenance and Evolution: Research and Practice Improving Design Quality Using Meta-pattern Transformations: a Metric-based Approach , 2022 .

[21]  Nadia Bouassida,et al.  A Metric-Based Approach for Anti-pattern Detection in UML Designs , 2011 .

[22]  Akshi Kumar,et al.  Machine Learning from Theory to Algorithms: An Overview , 2018, Journal of Physics: Conference Series.

[23]  José Amancio M. Santos,et al.  An exploratory study to investigate the impact of conceptualization in god class detection , 2013, EASE '13.

[24]  Ghulam Rasool,et al.  A review of code smell mining techniques , 2015, J. Softw. Evol. Process..

[25]  Cristina Marinescu,et al.  iPlasma: An Integrated Platform for Quality Assessment of Object-Oriented Design , 2005, ICSM.

[26]  Raed Shatnawi,et al.  An empirical study of the bad smells and class error probability in the post-release object-oriented system evolution , 2007, J. Syst. Softw..

[27]  Nakarin Maneerat,et al.  Bad-smell prediction from software design model using machine learning techniques , 2011, 2011 Eighth International Joint Conference on Computer Science and Software Engineering (JCSSE).

[28]  Nitesh V. Chawla,et al.  SMOTE: Synthetic Minority Over-sampling Technique , 2002, J. Artif. Intell. Res..

[29]  Lin Shi,et al.  Machine learning techniques for code smell detection: A systematic literature review and meta-analysis , 2019, Inf. Softw. Technol..

[30]  Dimitrios I. Fotiadis,et al.  Machine learning applications in cancer prognosis and prediction , 2014, Computational and structural biotechnology journal.

[31]  Alexander Chatzigeorgiou,et al.  JDeodorant: Identification and Removal of Type-Checking Bad Smells , 2008, 2008 12th European Conference on Software Maintenance and Reengineering.

[32]  José A. Taboada,et al.  Exploratory study of the impact of project domain and size category on the detection of the God class design smell , 2021, Software Quality Journal.

[33]  Esperanza Manso,et al.  Software Design Smell Detection: a systematic mapping study , 2018, Software Quality Journal.

[34]  Andrea De Lucia,et al.  Detecting code smells using machine learning techniques: Are we there yet? , 2018, 2018 IEEE 25th International Conference on Software Analysis, Evolution and Reengineering (SANER).

[35]  Andrea De Lucia,et al.  Comparing Heuristic and Machine Learning Approaches for Metric-Based Code Smell Detection , 2019, 2019 IEEE/ACM 27th International Conference on Program Comprehension (ICPC).

[36]  Francesca Arcelli Fontana,et al.  Inter-smell relations in industrial and open source systems: A replication and comparative analysis , 2015, 2015 IEEE International Conference on Software Maintenance and Evolution (ICSME).

[37]  A. Yamashita,et al.  Exploring the impact of inter-smell relations on software maintainability: An empirical study , 2013, 2013 35th International Conference on Software Engineering (ICSE).

[38]  Tracy Hall,et al.  Developing Fault-Prediction Models: What the Research Can Show Industry , 2011, IEEE Software.

[39]  José Manuel Cotos,et al.  Assessing the Influence of Size Category of the Project in God Class Detection, an Experimental Approach based on Machine Learning , 2019, SEKE.

[40]  Nitesh V. Chawla,et al.  Data Mining for Imbalanced Datasets: An Overview , 2005, The Data Mining and Knowledge Discovery Handbook.

[41]  Andrea De Lucia,et al.  A large empirical assessment of the role of data balancing in machine-learning-based code smell detection , 2020, J. Syst. Softw..

[42]  Yann-Gaël Guéhéneuc,et al.  DECOR: A Method for the Specification and Detection of Code and Design Smells , 2010, IEEE Transactions on Software Engineering.

[43]  Mika Mäntylä,et al.  Code Smell Detection: Towards a Machine Learning-Based Approach , 2013, 2013 IEEE International Conference on Software Maintenance.

[44]  James H. Hill,et al.  Towards detecting software performance anti-patterns using classification techniques , 2014, SOEN.

[45]  J. Koval,et al.  Interval estimation for Cohen's kappa as a measure of agreement. , 2000, Statistics in medicine.