Measuring the Quality of Machine Learning and Optimization Frameworks

Software frameworks are daily and extensively used in research, both for fundamental studies and applications. Researchers usually trust in the quality of these frameworks without any evidence that they are correctly build, indeed they could contain some defects that potentially could affect to thousands of already published and future papers. Considering the important role of these frameworks in the current state-of-the-art in research, their quality should be quantified to show the weaknesses and strengths of each software package.

[1]  Jesús Alcalá-Fdez,et al.  KEEL Data-Mining Software Tool: Data Set Repository, Integration of Algorithms and Experimental Analysis Framework , 2011, J. Multiple Valued Log. Soft Comput..

[2]  Wynne Hsu,et al.  Integrating Classification and Association Rule Mining , 1998, KDD.

[3]  Yann-Gaël Guéhéneuc,et al.  P-MARt : Pattern-like Micro Architecture Repository , 2007 .

[4]  Sebastián Lozano,et al.  Metaheuristic optimization frameworks: a survey and benchmarking , 2011, Soft Computing.

[5]  Stefan Wagner,et al.  Software Product Quality Control , 2013, Springer Berlin Heidelberg.

[6]  Ralph Johnson,et al.  design patterns elements of reusable object oriented software , 2019 .

[7]  Jian Pei,et al.  CMAR: accurate and efficient classification based on multiple class-association rules , 2001, Proceedings 2001 IEEE International Conference on Data Mining.

[8]  Antonio J. Nebro,et al.  jMetal: A Java framework for multi-objective optimization , 2011, Adv. Eng. Softw..

[9]  Patrick M. Reed,et al.  Borg: An Auto-Adaptive Many-Objective Evolutionary Computing Framework , 2013, Evolutionary Computation.

[10]  Saeed Jalili,et al.  Source code and design conformance, design pattern detection from source code by classification approach , 2015, Appl. Soft Comput..

[11]  Peter A. Whigham,et al.  Grammar-based Genetic Programming: a survey , 2010, Genetic Programming and Evolvable Machines.

[12]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.

[13]  Alexander Chatzigeorgiou,et al.  Design Pattern Detection Using Similarity Scoring , 2006, IEEE Transactions on Software Engineering.

[14]  Fabio Stella,et al.  On applying machine learning techniques for design pattern detection , 2015, J. Syst. Softw..

[15]  Francesca Arcelli Fontana,et al.  DPB: A Benchmark for Design Pattern Detection Tools , 2012, 2012 16th European Conference on Software Maintenance and Reengineering.

[16]  Nadia Bouassida,et al.  Using metric-based filtering to improve design pattern detection approaches , 2014, Innovations in Systems and Software Engineering.

[17]  César Hervás-Martínez,et al.  JCLEC: a Java framework for evolutionary computation , 2007, Soft Comput..

[18]  Itay Maman,et al.  Micro patterns in Java code , 2005, OOPSLA '05.

[19]  Sebastián Ventura,et al.  Design and behavior study of a grammar-guided genetic programming algorithm for mining association rules , 2011, Knowledge and Information Systems.