research-article

A meta-learning framework for algorithm recommendation in software fault prediction

Authors:

Silvia N. das Dôres,

Duncan D. Ruiz,

Rodrigo C. BarrosAuthors Info & Claims

SAC '16: Proceedings of the 31st Annual ACM Symposium on Applied Computing

Pages 1486 - 1491

https://doi.org/10.1145/2851613.2851788

Published: 04 April 2016 Publication History

Abstract

Software fault prediction is a significant part of software quality assurance and it is commonly used to detect faulty software modules based on software measurement data. Several machine learning based approaches have been proposed for generating predictive models from collected data, although none has become standard given the specificities of each software project. Hence, we believe that recommending the best algorithm for each project is much more important and useful than developing a single algorithm for being used in any project. For achieving that goal, we propose in this paper a novel framework for recommending machine learning algorithms that is capable of automatically identifying the most suitable algorithm according to the software project that is being considered. Our solution, namely SFP-MLF, makes use of the meta-learning paradigm in order to learn the best learner for a particular project. Results show that the SFP-MLF framework provides both the best single algorithm recommendation and also the best ranking recommendation for the software fault prediction problem.

References

[1]

M. Basu and T. K. Ho. Data Complexity in Pattern Recognition. Springer London, www.springer.com, 2006.

[2]

P. Brazdil, C. G. Carrier, C. Soares, and R. Vilalta. Metalearning: Applications to Data Mining. Springer, Berlin, 2009 edition edition, Nov. 2008.

[3]

P. B. Brazdil and R. J. Henery. Machine learning, neural and statistical classification. pages 175--212. Ellis Horwood, Upper Saddle River, NJ, USA, 1994.

Digital Library

[4]

P. B. Brazdil, C. Soares, and J. P. d. Costa. Ranking learning algorithms: Using IBL and meta-learning on accuracy and time results. Machine Learning, 50(3):251--277, Mar. 2003.

Digital Library

[5]

L. Breiman. Random forests. Machine Learning, 45(1):5--32, 2001.

Digital Library

[6]

K. El Emam, S. Benlarbi, N. Goel, and S. N. Rai. Comparing case-based reasoning classifiers for predicting high risk software components. Journal of Systems and Software, 55(3):301--320, 2001.

Digital Library

[7]

B. Feres de Souza, C. Soares, and A. C. de Carvalho. Meta-learning approach to gene expression data classification. International Journal of Intelligent Computing and Cybernetics, 2(2):285--303, 2009.

[8]

M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I. H. Witten. The weka data mining software: An update. SIGKDD Explor. Newsl., 11(1):10--18, Nov. 2009.

Digital Library

[9]

T. K. Ho and M. Basu. Complexity measures of supervised classification problems. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(3):356--370, Mrço 2002.

Digital Library

[10]

A. Kalousis. Algorithm Selection via Meta-Learning. PhD thesis, Geneva, 2002.

[11]

S. Kanmani, V. R. Uthariaraj, V. Sankaranarayanan, and P. Thambidurai. Object-oriented software fault prediction using neural networks. Information and software technology, 49(5):483--492, 2007.

Digital Library

[12]

T. A. M. Khoshgoftaar and N. Seliya. Tree-based software quality estimation models for fault prediction. In Software Metrics, 2002. Proceedings. Eighth IEEE Symposium on, pages 203--214. IEEE, 2002.

Digital Library

[13]

T. M. Khoshgoftaar and E. B. Allen. Logistic regression modeling of software quality. International Journal of Reliability, Quality and Safety Engineering, 6(04):303--317, 1999.

[14]

S. Lessmann, B. Baesens, C. Mues, and S. Pietsch. Benchmarking classification models for software defect prediction: A proposed framework and novel findings. IEEE Transactions on Software Engineering, 34(4):485--496, July 2008.

Digital Library

[15]

R. Malhotra. A systematic review of machine learning techniques for software fault prediction. Applied Soft Computing, 27(Complete):504--518, 2015.

Digital Library

[16]

R. G. Mantovani, A. L. Rossiy, J. Vanchorenz, B. Bischlx, and A. C. De Carvalho. To tune or not to tune: recommending when to adjust SVM hyper-parameters via Meta-learning. IEEE, 2015.

[17]

T. Menzies, B. Caglayan, Z. He, E. Kocaguneli, J. Krall, F. Peters, and B. Turhan. The promise repository of empirical software engineering data, June 2012.

[18]

T. Menzies, J. Greenwald, and A. Frank. Data mining static code attributes to learn defect predictors. IEEE Transactions on Software Engineering, 33(1):2--13, Jan. 2007.

[19]

Q. Song, Z. Jia, M. Shepperd, S. Ying, and J. Liu. A general software defect-proneness prediction framework. IEEE Transactions on Software Engineering, 37(3):356--370, Maio--Junho 2011.

Digital Library

Cited By

Emond B(2024)Verification and Validation of Adaptive Instructional Systems: A Text Mining ReviewAdaptive Instructional Systems10.1007/978-3-031-60609-0_3(25-43)Online publication date: 29-Jun-2024
https://dl.acm.org/doi/10.1007/978-3-031-60609-0_3
Garouani MAhmad ABouneffa MHamlich M(2023)Autoencoder-kNN meta-model based data characterization approach for an automated selection of AI algorithmsJournal of Big Data10.1186/s40537-023-00687-710:1Online publication date: 3-Feb-2023
https://doi.org/10.1186/s40537-023-00687-7
Santos MAbreu PJapkowicz NFernández ASantos J(2023)A unifying view of class overlap and imbalance: Key concepts, multi-view panorama, and open avenues for researchInformation Fusion10.1016/j.inffus.2022.08.01789(228-253)Online publication date: Jan-2023
https://doi.org/10.1016/j.inffus.2022.08.017
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Index Terms

A meta-learning framework for algorithm recommendation in software fault prediction
1. Software and its engineering
  1. Software creation and management
    1. Software verification and validation
      1. Software defect analysis

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cover image ACM Conferences

SAC '16: Proceedings of the 31st Annual ACM Symposium on Applied Computing

April 2016

2360 pages

ISBN:9781450337397

DOI:10.1145/2851613

Conference Chair:
Sascha Ossowski
University Rey Juan Carlos, Spain

Copyright © 2016 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 04 April 2016

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior

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SAC 2016

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SIGAPP

SAC 2016: Symposium on Applied Computing

April 4 - 8, 2016

Pisa, Italy

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SAC '16 Paper Acceptance Rate 252 of 1,047 submissions, 24%;

Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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Cited By

Emond B(2024)Verification and Validation of Adaptive Instructional Systems: A Text Mining ReviewAdaptive Instructional Systems10.1007/978-3-031-60609-0_3(25-43)Online publication date: 29-Jun-2024
https://dl.acm.org/doi/10.1007/978-3-031-60609-0_3
Garouani MAhmad ABouneffa MHamlich M(2023)Autoencoder-kNN meta-model based data characterization approach for an automated selection of AI algorithmsJournal of Big Data10.1186/s40537-023-00687-710:1Online publication date: 3-Feb-2023
https://doi.org/10.1186/s40537-023-00687-7
Santos MAbreu PJapkowicz NFernández ASantos J(2023)A unifying view of class overlap and imbalance: Key concepts, multi-view panorama, and open avenues for researchInformation Fusion10.1016/j.inffus.2022.08.01789(228-253)Online publication date: Jan-2023
https://doi.org/10.1016/j.inffus.2022.08.017
Özakıncı RKolukısa Tarhan A(2022)A decision analysis approach for selecting software defect prediction method in the early phasesSoftware Quality Journal10.1007/s11219-022-09595-031:1(121-177)Online publication date: 6-Sep-2022
https://doi.org/10.1007/s11219-022-09595-0
Matloob FAftab SAhmad MAdnan Khan MFatima AIqbal MMohsen Alruwaili WSabri Elmitwally N(2021)Software Defect Prediction Using Supervised Machine Learning Techniques: A Systematic Literature ReviewIntelligent Automation & Soft Computing10.32604/iasc.2021.01756229:2(403-421)Online publication date: 2021
https://doi.org/10.32604/iasc.2021.017562
Stefana EPaltrinieri N(2021)ProMetaUS: A proactive meta-learning uncertainty-based framework to select models for Dynamic Risk ManagementSafety Science10.1016/j.ssci.2021.105238138(105238)Online publication date: Jun-2021
https://doi.org/10.1016/j.ssci.2021.105238
Cohen-Shapira NRokach L(2021)Automatic selection of clustering algorithms using supervised graph embeddingInformation Sciences: an International Journal10.1016/j.ins.2021.08.028577:C(824-851)Online publication date: 1-Oct-2021
https://dl.acm.org/doi/10.1016/j.ins.2021.08.028
Ficel HHaddad MBaazaoui Zghal H(2021)A graph-based recommendation approach for highly interactive platformsExpert Systems with Applications: An International Journal10.1016/j.eswa.2021.115555185:COnline publication date: 15-Dec-2021
https://dl.acm.org/doi/10.1016/j.eswa.2021.115555
Souza MSabourin RCavalcanti GCruz R(2020)Multi-label learning for dynamic model type recommendation2020 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN48605.2020.9207644(1-10)Online publication date: Jul-2020
https://doi.org/10.1109/IJCNN48605.2020.9207644
Lorena AGarcia LLehmann JSouto MHo T(2019)How Complex Is Your Classification Problem?ACM Computing Surveys10.1145/334771152:5(1-34)Online publication date: 13-Sep-2019
https://dl.acm.org/doi/10.1145/3347711
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