research-article

Using CBR and CART to predict maintainability of relational database-driven software applications

Authors:

Muhammad SulaymanAuthors Info & Claims

EASE '13: Proceedings of the 17th International Conference on Evaluation and Assessment in Software Engineering

Pages 132 - 143

https://doi.org/10.1145/2460999.2461019

Published: 14 April 2013 Publication History

Abstract

Background: Relational database-driven software applications have gained significant importance in modern software development. Given that software maintainability is an important quality attribute, predicting these applications' maintainability can provide various benefits to software organizations, such as adopting a defensive design and more informed resource management. Aims: The aim of this paper is to present the results from employing two well-known prediction techniques to estimate the maintainability of relational database-driven applications. Method: Case-based reasoning (CBR) and classification and regression trees (CART) were applied to data gathered on 56 software projects from software companies. The projects concerned development and/or maintenance of relational database-driven applications. Unlike previous studies, all variables (28 independent and 1 dependent) were measured on a 5-point bi-polar scale. Results: Results showed that CBR performed slightly better (at 76.8% correct predictions) in terms of prediction accuracy when compared to CART (67.8%). In addition, the two important predictors identified were documentation quality and understandability of the applications. Conclusions: The results show that CBR can be used by software companies to formalize and improve their process of maintainability prediction. Future work involves gathering more data and also employing other prediction techniques.

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Cited By

Gonzalez-Hemandez SSanchez-Garcia ACortes-Verdin KPerez-Arriaga J(2021)Regression in Estimation of Software Attributes: A Systematic Literature Review2021 9th International Conference in Software Engineering Research and Innovation (CONISOFT)10.1109/CONISOFT52520.2021.00019(54-60)Online publication date: Oct-2021
https://doi.org/10.1109/CONISOFT52520.2021.00019
Malhotra RLata K(2020)A systematic literature review on empirical studies towards prediction of software maintainabilitySoft Computing10.1007/s00500-020-05005-4Online publication date: 28-May-2020
https://doi.org/10.1007/s00500-020-05005-4
Chen WWang X(2014)A Way to Predict and Evaluate of Software Maintainability Based on Machine LearningAdvanced Materials Research10.4028/www.scientific.net/AMR.926-930.2924926-930(2924-2927)Online publication date: May-2014
https://doi.org/10.4028/www.scientific.net/AMR.926-930.2924

Index Terms

Using CBR and CART to predict maintainability of relational database-driven software applications
1. Information systems
  1. Data management systems
    1. Database design and models
      1. Relational database model
    2. Query languages
      1. Relational database query languages
2. Software and its engineering
  1. Software creation and management
    1. Software post-development issues
      1. Documentation

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cover image ACM Other conferences

EASE '13: Proceedings of the 17th International Conference on Evaluation and Assessment in Software Engineering

April 2013

268 pages

ISBN:9781450318488

DOI:10.1145/2460999

General Chair:
Fabio Q. B. da Silva
Universidade Federal de Pernambuco, Brazil
,
Program Chairs:
Natalia Juristo
Universidad Politécnica de Madrid, Spain
,
Guilherme Horta Travassos
Universidade Federal do Rio de Janeiro, Brazil

Copyright © 2013 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]

Sponsors

Centro de Informatica - UFPE: Centro de Informatica - UFPE
SBC: Brazilian Computer Society
CNPq: Conselho Nacional de Desenvolvimento Cientifico e Tecn
CAPES: Brazilian Higher Education Funding Council

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 14 April 2013

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Knowledge Foundation in Sweden

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EASE '13

Sponsor:

Centro de Informatica - UFPE
SBC
CNPq
CAPES

EASE '13: 17th International Conference on Evaluation and Assessment in Software Engineering

April 14 - 16, 2013

Porto de Galinhas, Brazil

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EASE '13 Paper Acceptance Rate 31 of 94 submissions, 33%;

Overall Acceptance Rate 71 of 232 submissions, 31%

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Cited By

Gonzalez-Hemandez SSanchez-Garcia ACortes-Verdin KPerez-Arriaga J(2021)Regression in Estimation of Software Attributes: A Systematic Literature Review2021 9th International Conference in Software Engineering Research and Innovation (CONISOFT)10.1109/CONISOFT52520.2021.00019(54-60)Online publication date: Oct-2021
https://doi.org/10.1109/CONISOFT52520.2021.00019
Malhotra RLata K(2020)A systematic literature review on empirical studies towards prediction of software maintainabilitySoft Computing10.1007/s00500-020-05005-4Online publication date: 28-May-2020
https://doi.org/10.1007/s00500-020-05005-4
Chen WWang X(2014)A Way to Predict and Evaluate of Software Maintainability Based on Machine LearningAdvanced Materials Research10.4028/www.scientific.net/AMR.926-930.2924926-930(2924-2927)Online publication date: May-2014
https://doi.org/10.4028/www.scientific.net/AMR.926-930.2924

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