36 citations · 137 across the 7 of their papers we have counts for
9 papers
Analyzing the Stationarity Process in Software Effort Estimation Datasets
Michael Franklin Bosu, Stephen G. MacDonell, Peter A. Whigham
Software effort estimation models are typically developed based on an underlying assumption that all data points are equally relevant to the prediction of effort for future project…
A Taxonomy of Data Quality Challenges in Empirical Software Engineering
Michael Franklin Bosu, Stephen G. MacDonell
Reliable empirical models such as those used in software effort estimation or defect prediction are inherently dependent on the data from which they are built. As demands for proce…
Investigating the Significance of Bellwether Effect to Improve Software Effort Estimation
Solomon Mensah, Jacky Keung, Stephen G. MacDonell +2
Bellwether effect refers to the existence of exemplary projects (called the Bellwether) within a historical dataset to be used for improved prediction performance. Recent studies h…
Data Quality in Empirical Software Engineering: A Targeted Review
Michael Franklin Bosu, Stephen G. MacDonell
Context: The utility of prediction models in empirical software engineering (ESE) is heavily reliant on the quality of the data used in building those models. Several data quality…
Investigating the Significance of the Bellwether Effect to Improve Software Effort Prediction: Further Empirical Study
Solomon Mensah, Jacky Keung, Stephen G. MacDonell +2
Context: In addressing how best to estimate how much effort is required to develop software, a recent study found that using exemplary and recently completed projects [forming Bell…
On Satisfying the Android OS Community: User Feedback Still Central to Developers' Portfolios
Sherlock A. Licorish, Amjed Tahir, Michael Franklin Bosu +1
End-users play an integral role in identifying requirements, validating software features' usefulness, locating defects, and in software product evolution in general. Their role in…
Experience: Quality Benchmarking of Datasets Used in Software Effort Estimation
Michael F. Bosu, Stephen G. MacDonell
Data is a cornerstone of empirical software engineering (ESE) research and practice. Data underpin numerous process and project management activities, including the estimation of d…
Testing the Stationarity Assumption in Software Effort Estimation Datasets
Michael Franklin Bosu, Stephen G. MacDonell, Peter Whigham
Software effort estimation (SEE) models are typically developed based on an underlying assumption that all data points are equally relevant to the prediction of effort for future p…
Time-Aware Models for Software Effort Estimation
Michael Franklin Bosu, Stephen G. MacDonell, Peter Whigham
It seems logical to assert that the dynamic nature of software engineering practice would mean that software effort estimation (SEE) modelling should take into account project star…