<abstract xmlns="http://www.w3.org/1999/xhtml"><p>User satisfaction is an important feature of software quality. However, it was rarely studied in software engineering literature. By enhancing earlier research this paper focuses on predicting user satisfaction with machine learning techniques using software development data from an extended ISBSG dataset. This study involved building, evaluating and comparing a total of 15,600 prediction schemes. Each scheme consists of a different combination of its components: manual feature preselection, handling missing values, outlier elimination, value normalization, automated feature selection, and a classifier. The research procedure involved a 10-fold cross-validation and separate testing, both repeated 10 times, to train and to evaluate each prediction scheme. Achieved level of accuracy for best performing schemes expressed by Matthews correlation coefficient was about 0.5 in the cross-validation and about 0.5–0.6 in the testing stage. The study identified the most accurate settings for components of prediction schemes.</p></abstract>

User satisfaction is an important feature of software quality. However, it was rarely studied in software engineering literature. By enhancing earlier research this paper focuses on predicting user satisfaction with machine learning techniques using software development data from an extended ISBSG dataset. This study involved building, evaluating and comparing a total of 15,600 prediction schemes. Each scheme consists of a different combination of its components: manual feature preselection, handling missing values, outlier elimination, value normalization, automated feature selection, and a classifier. The research procedure involved a 10-fold cross-validation and separate testing, both repeated 10 times, to train and to evaluate each prediction scheme. Achieved level of accuracy for best performing schemes expressed by Matthews correlation coefficient was about 0.5 in the cross-validation and about 0.5–0.6 in the testing stage. The study identified the most accurate settings for components of prediction schemes.

Predicting Aggregated User Satisfaction in Software Projects

Faculty of Computer Science and Information Technology

West Pomeranian University of Technology in Szczecin

Foundations of Computing and Decision Sciences

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

{"article-title":"Predicting Aggregated User Satisfaction in Software Projects"}

User satisfaction is an important feature of software quality. However, it was rarely studied in software engineering literature. By enhancing earlier research...

Predicting Aggregated User Satisfaction in Software Projects

Online veröffentlicht: 31. Dez. 2018

Seitenbereich: 335 - 357

Eingereicht: 07. Aug. 2018

Akzeptiert: 19. Okt. 2018

DOI: https://doi.org/10.1515/fcds-2018-0017

Schlüsselwörter
user satisfaction, prediction scheme, software projects, machine learning, ISBSG

© 2018 Łukasz Radliński, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Predicting Aggregated User Satisfaction in Software Projects

Online veröffentlicht: 31. Dez. 2018

Seitenbereich: 335 - 357

Eingereicht: 07. Aug. 2018

Akzeptiert: 19. Okt. 2018

DOI: https://doi.org/10.1515/fcds-2018-0017

Schlüsselwörteruser satisfaction, prediction scheme, software projects, machine learning, ISBSG

© 2018 Łukasz Radliński, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Schlüsselwörter
user satisfaction, prediction scheme, software projects, machine learning, ISBSG