Kwabena Ebo Bennin

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To prioritize software quality assurance efforts, fault prediction models have been proposed to distinguish faulty modules from clean modules. The performances of such models are often biased and irrelevant due to the skewness or class imbalance of the datasets considered. To improve the prediction performance of these models, sampling techniques have been(More)
Background: Over the last two decades, numerous software design patterns have been introduced and cataloged on the basis of developer's interest and skills. Motivation: In software design phase, inexperienced designers are mostly concerned on how to select an appropriate design pattern from the catalog of relevant patterns in order to solve a design(More)
In the domain of software fault prediction, class membership probability of a selected classifier and the factors related to its estimation can be considered as necessary information for tester to take informed decisions about software quality issues. The objective of this study is to empirically investigate the class membership probability estimation(More)
Software Testing Effort (STE), which contributes about 25-40% of the total development effort, plays a significant role in software development. In addressing the issues faced by companies in finding relevant datasets for STE estimation modeling prior to development, cross-company modeling could be leveraged. The study aims at assessing the effectiveness of(More)
To prioritize quality assurance efforts, various fault prediction models have been proposed. However, the best performing fault prediction model is unknown due to three major drawbacks: (1) comparison of few fault prediction models considering small number of data sets, (2) use of evaluation measures that ignore testing efforts and (3) use of n-fold(More)
In object-oriented software development, a plethora of studies have been carried out to present the application of machine learning algorithms for fault prediction. Furthermore, it has been empirically validated that an ensemble method can improve classification performance as compared to a single classifier. But, due to the inherent differences among(More)
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