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Software quality estimation with limited fault data: a semi-supervised learning perspective

Published: 01 September 2007 Publication History

Abstract

We addresses the important problem of software quality analysis when there is limited software fault or fault-proneness data. A software quality model is typically trained using software measurement and fault data obtained from a previous release or similar project. Such an approach assumes that fault data is available for all the training modules. Various issues in software development may limit the availability of fault-proneness data for all the training modules. Consequently, the available labeled training dataset is such that the trained software quality model may not provide predictions. More specifically, the small set of modules with known fault-proneness labels is not sufficient for capturing the software quality trends of the project. We investigate semi-supervised learning with the Expectation Maximization (EM) algorithm for software quality estimation with limited fault-proneness data. The hypothesis is that knowledge stored in software attributes of the unlabeled program modules will aid in improving software quality estimation. Software data collected from a large NASA software project is used during the semi-supervised learning process. The software quality model is evaluated with multiple test datasets collected from other NASA software projects. Compared to software quality models trained only with the available set of labeled program modules, the EM-based semi-supervised learning scheme improves generalization performance of the software quality models.

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  • (2023)Implications of semi-supervised learning for design pattern selectionSoftware Quality Journal10.1007/s11219-022-09610-431:3(809-842)Online publication date: 5-Jan-2023
  • (2022)A Review on Software Defect Prediction Using Machine LearningProceedings of the 4th International Conference on Information Management & Machine Intelligence10.1145/3590837.3590918(1-10)Online publication date: 23-Dec-2022
  • (2021)An empirical study toward dealing with noise and class imbalance issues in software defect predictionSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-021-06096-325:21(13465-13492)Online publication date: 1-Nov-2021
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Information & Contributors

Information

Published In

cover image Software Quality Journal
Software Quality Journal  Volume 15, Issue 3
September 2007
106 pages

Publisher

Kluwer Academic Publishers

United States

Publication History

Published: 01 September 2007

Author Tags

  1. Expectation maximization
  2. Semi-supervised learning
  3. Software metrics
  4. Software quality estimation
  5. Unlabeled data

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Cited By

View all
  • (2023)Implications of semi-supervised learning for design pattern selectionSoftware Quality Journal10.1007/s11219-022-09610-431:3(809-842)Online publication date: 5-Jan-2023
  • (2022)A Review on Software Defect Prediction Using Machine LearningProceedings of the 4th International Conference on Information Management & Machine Intelligence10.1145/3590837.3590918(1-10)Online publication date: 23-Dec-2022
  • (2021)An empirical study toward dealing with noise and class imbalance issues in software defect predictionSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-021-06096-325:21(13465-13492)Online publication date: 1-Nov-2021
  • (2019)A study on software fault prediction techniquesArtificial Intelligence Review10.1007/s10462-017-9563-551:2(255-327)Online publication date: 1-Feb-2019
  • (2017)Label propagation based semi-supervised learning for software defect predictionAutomated Software Engineering10.1007/s10515-016-0194-x24:1(47-69)Online publication date: 1-Mar-2017
  • (2015)Case consistencyProceedings of the 19th International Conference on Evaluation and Assessment in Software Engineering10.1145/2745802.2745820(1-10)Online publication date: 27-Apr-2015
  • (2015)A systematic review of machine learning techniques for software fault predictionApplied Soft Computing10.1016/j.asoc.2014.11.02327:C(504-518)Online publication date: 1-Feb-2015
  • (2014)An in-depth study of the potentially confounding effect of class size in fault predictionACM Transactions on Software Engineering and Methodology10.1145/255677723:1(1-51)Online publication date: 20-Feb-2014
  • (2013)Creating Process-Agents incrementally by mining process asset libraryInformation Sciences: an International Journal10.1016/j.ins.2012.12.052233(183-199)Online publication date: 1-Jun-2013
  • (2012)Software defect prediction using semi-supervised learning with dimension reductionProceedings of the 27th IEEE/ACM International Conference on Automated Software Engineering10.1145/2351676.2351734(314-317)Online publication date: 3-Sep-2012
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