Kwabena Ebo Bennin

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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)
Background: In the plethora of studies, the object-orientedmetrics have been empirically validated to assess the design properties and quantify the high-level quality attributes such as fault-proneness, either at the method or class granularity levels of software. Motivation: A more precise value of an object-oriented metric can be used as an indicator for(More)
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 due to the skewness or class imbalance of the datasets considered. To improve the prediction performance of these models, sampling techniques have been employed to(More)
Trend of software development is changing rapidly most of the software development organizations are trying to globalize their activities throughout the world. This trend leads towards a phenomenon called Global Software Development (GSD). The main reason behind the software globalization is its various benefits. Besides these benefits, software(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)
Effort-inconsistency is a situation where historical software project data used for software effort estimation (SEE) are contaminated by many project cases with similar characteristics but are completed with significantly different amount of effort. Using these data for SEE generally produces inaccurate results; however, an effective technique for its(More)
Information retrieval based automatic bug localization techniques provide developers a ranked list of suspicious buggy source entities to aid locate the ones needed to be modified and to fix the bug. However, it is unavoidable that some buggy entities are ranked low in the result list using these automatic techniques. We assume a bug localization process to(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)
—Programmers sometimes leave incomplete, temporary workarounds and buggy codes that require rework. This phenomenon in software development is referred to as Self-admitted Technical Debt (SATD). The challenge therefore is for software engineering researchers and practitioners to resolve the SATD problem to improve the software quality. We performed an(More)