Software Product Satisfaction Classification

Somchai Prakancharoen, Thavatchai Ngamsantivong, Nattapong Luangnaruedom, Suchada Ratanakongnate

Abstract


There are many software product quality attributes which are concerned by both software development companies and customers.  This research tried to seek for the most significant attributes then use them on software satisfaction level prediction by classification tree. The samples were collected from thirty complete developed software projects which all of them are coded for customer’s satisfaction on that software product. Multivariate linear regression and Discriminant analysis are used to filter the significant attributes. Decision tree with these significant attributes are used to construct the classification rule of software product satisfaction classification. The accuracy of classification was about 60%. The effective attributes on classification are functional suitability, reliability, and portability.


Full Text:

PDF

References


ISO 25000 Portal,Softare quality model,2022. https://iso25000.com/index.php/en/iso-25000-standards/iso-25010

International Association for SoftwareArchitects, Quality Attributes, 2022. Available on https://itabok.iasaglobal.org/quality-attributes/

Syndicode, software architecture quality attributes, San Francisco, CA 94102. Available on https://syndicode.com/blog/12-software-architecture-quality-attributes/

Huberty, C. J. and Olejnik, S. (2006). Applied MANOVA and Discriminant Analysis, Second Edition. Hoboken, New Jersey: John Wiley and Sons, Inc.

Lund Research, Linear Regression Analysis using SPSS Statistics, Laerd Statistics, 2020. Available on https://statistics.laerd.com/spss-tutorials/linear-regression-using-spss-statistics.phpK. Elissa, “Title of paper if known,” unpublished.

Prashant Gupta, Decision Trees in Machine Learning, Towards Data Science, 2017. Available on https://towardsdatascience.com/decision-trees-in-machine-learning-641b9c4e8052


Refbacks

  • There are currently no refbacks.