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Predicting Aggregated User Satisfaction in Software Projects

Radliński Ł.

Foundations of Computing and Decision Sciences

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2018

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Vol. 43, No. 4

335--357

EN

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.

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Preliminary evaluation of schemes for predicting use satisfaction with the ability of system to meet stated objectives

Radliński Ł.

Journal of Theoretical and Applied Computer Science

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2015

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Vol. 9, nr 2

32--50

EN

In software engineering literature two most commonly investigated targets for prediction are development effort and software quality. This study follows the methodological advances of these studies but focuses on predicting user satisfaction in software project. Specific outcome variable investigated in prediction is user satisfaction with the ability of system to meet stated objectives (MSO). A total number of 288 prediction schemes have been evaluated in the ability to predict MSO. These schemes have been built as different combinations of their components, i.e. feature pre-selection, elimination of missing values, automated feature selection, and a classifier. Two best performing schemes achieved the accuracy measured as Matthews correlation coefficient of 0.71 in test subset. These schemes involved W-LMT and W-SimpleLogistic classifiers. Significant differences have been observed between different classifiers and selected other components, depending on the dataset (validation or test). Discussed results may serve as guidelines to design a scheme to predict user satisfaction.

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Factors of software quality - analysis of extended ISBSG dataset

Radliński Ł.

Foundations of Computing and Decision Sciences

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2011

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Vol. 36, No. 3-4

293-313

EN

In this paper, we analyze the extended ISBSG dataset, which contains data on a wide range of software projects developed in various companies worldwide. The main aim of this paper is to identify important factors that influence software quality and to investigate the nature of these relationships. This analysis involves using various statistical techniques, both analytical and graphical. We provide a rating for each variable to express the strength of its relationship with software quality. Unlike earlier analyses, we focus on the business perspective and its relationships on software quality. Obtained results may be used do support decision making in software projects, specifically by demonstrating the impact of selected software development practices.