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An Exploratory Study for Predicting Maintenance Effort using Hybridized Techniques

Published: 05 February 2017 Publication History

Abstract

Software maintenance effort prediction is one of the very costly and challenging affair in the process of software development. Early detection of changes in software are also necessary as it helps the software developers and project managers to allocate resources in an efficient manner. It is very critical for the project managers and software developers to detect changes in software in the earlier phases of software development so that the portions of software that are more prone to changes can be restructured and redesigned. Various statistical and machine learning based models are available for maintenance effort prediction of these objects oriented systems. In this paper, we propose a novel approach for maintenance effort prediction using hybridized (i.e., combining search-based techniques with machine learning alternatives) techniques. Specifically, we will address these research issues: (i) low repeatability of empirical studies involving maintainability models, (ii) less usage of statistical tests for comparing the effectiveness of different models, and (iii) non-assessment of predictive performance of hybridized techniques. The models are constructed using object-oriented metrics and the results of this research are validated using two commercial datasets. Based on the experiments conducted it has been proved that hybridized techniques have capability for predicting maintenance effort.

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Cited By

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  • (2021)Software Enhancement Effort Prediction Using Machine-Learning Techniques: A Systematic Mapping StudySN Computer Science10.1007/s42979-021-00872-62:6Online publication date: 22-Sep-2021
  • (2021)Handling class imbalance problem in software maintainability prediction: an empirical investigationFrontiers of Computer Science10.1007/s11704-021-0127-016:4Online publication date: 3-Dec-2021
  • (2020)Using Ensembles for Class Imbalance Problem to Predict Maintainability of Open Source SoftwareInternational Journal of Reliability, Quality and Safety Engineering10.1142/S0218539320400112Online publication date: 3-Feb-2020
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Published In

cover image ACM Other conferences
ISEC '17: Proceedings of the 10th Innovations in Software Engineering Conference
February 2017
235 pages
ISBN:9781450348560
DOI:10.1145/3021460
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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Publication History

Published: 05 February 2017

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Author Tags

  1. Empirical Validation
  2. Object-oriented metrics
  3. Search-based techniques
  4. Software maintainability
  5. Software maintenance effort prediction

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  • Research
  • Refereed limited

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ISEC '17

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ISEC '17 Paper Acceptance Rate 25 of 81 submissions, 31%;
Overall Acceptance Rate 76 of 315 submissions, 24%

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Cited By

View all
  • (2021)Software Enhancement Effort Prediction Using Machine-Learning Techniques: A Systematic Mapping StudySN Computer Science10.1007/s42979-021-00872-62:6Online publication date: 22-Sep-2021
  • (2021)Handling class imbalance problem in software maintainability prediction: an empirical investigationFrontiers of Computer Science10.1007/s11704-021-0127-016:4Online publication date: 3-Dec-2021
  • (2020)Using Ensembles for Class Imbalance Problem to Predict Maintainability of Open Source SoftwareInternational Journal of Reliability, Quality and Safety Engineering10.1142/S0218539320400112Online publication date: 3-Feb-2020
  • (2020)Software Defect Categorization based on Maintenance Effort and Change Impact using Multinomial Naïve Bayes Algorithm2020 8th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO)10.1109/ICRITO48877.2020.9198037(1068-1073)Online publication date: Jun-2020
  • (2020)Using Hybridized techniques for Prediction of Software Maintainability using Imbalanced data2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence)10.1109/Confluence47617.2020.9058197(787-792)Online publication date: Jan-2020
  • (2020)F88An Empirical Study to Investigate the Impact of Data Resampling Techniques on the Performance of Class Maintainability Prediction ModelsNeurocomputing10.1016/j.neucom.2020.01.120Online publication date: Aug-2020
  • (2020)An empirical study on predictability of software maintainability using imbalanced dataSoftware Quality Journal10.1007/s11219-020-09525-yOnline publication date: 5-Aug-2020
  • (2020)A systematic literature review on empirical studies towards prediction of software maintainabilitySoft Computing10.1007/s00500-020-05005-4Online publication date: 28-May-2020
  • (2018)On the Application of Cross-Project Validation for Predicting Maintainability of Open Source Software using Machine Learning Techniques2018 7th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO)10.1109/ICRITO.2018.8748749(175-181)Online publication date: Aug-2018

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