Application of ensemble techniques in predicting object-oriented software maintainability

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

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Abstract

While prior object-oriented software maintainability literature acknowledges the role of machine learning techniques as valuable predictors of potential change, the most suitable technique that achieves consistently high accuracy remains undetermined. With the objective of obtaining more consistent results, an ensemble technique is investigated to advance the performance of the individual models and increase their accuracy in predicting software maintainability of the object-oriented system. This paper describes the research plan for predicting object-oriented software maintainability using ensemble techniques. First, we present a brief overview of the main research background and its different components. Second, we explain the research methodology. Third, we provide expected results. Finally, we conclude summary of the current status.
Original languageEnglish
Title of host publicationProceedings of EASE 2019 - Evaluation and Assessment in Software Engineering
Place of PublicationNew York
Pages370-373
Number of pages4
ISBN (Electronic)9781450371452
DOIs
Publication statusPublished - 15 Apr 2019
EventEASE 2019 - Evaluation and Assessment in Software Engineering - Copenhagen, Denmark
Duration: 16 Apr 201917 Apr 2019
Conference number: 23
https://ease2019.org/

Conference

ConferenceEASE 2019 - Evaluation and Assessment in Software Engineering
Abbreviated titleEASE 2019
CountryDenmark
CityCopenhagen
Period16/04/1917/04/19
Internet address

Keywords

  • individual model
  • ensemble model
  • software maintainability
  • object-oriented systems

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