Fayola Peters

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How can we find data for quality prediction? Early in the life cycle, projects may lack the data needed to build such predictors. Prior work assumed that relevant training data was found nearest to the local project. But is this the best approach? This paper introduces the Peters filter which is based on the following conjecture: When local data is scarce,(More)
The fundamental issue in cross project defect prediction is selecting the most appropriate training data for creating quality defect predictors. Another concern is whether historical data of open-source projects can be used to create quality predictors for proprietary projects from a practical point-of-view. Current studies have proposed statistical(More)
Ideally, we can learn lessons from software projects across multiple organizations. However, a major impediment to such knowledge sharing are the privacy concerns of software development organizations. This paper aims to provide defect data-set owners with an effective means of privatizing their data prior to release. We explore MORPH which understands how(More)
Before a community can learn general principles, it must share individual experiences. Data sharing is the fundamental step of cross project defect prediction, i.e. the process of using data from one project to predict for defects in another. Prior work on secure data sharing allowed data owners to share their data on a single-party basis for defect(More)
Background: Cross-company defect prediction (CCDP) is a field of study where an organization lacking enough local data can use data from other organizations for building defect predictors. To support CCDP, data must be shared. Such shared data must be privatized, but that privatization could severely damage the utility of the data. Aim: To enable effective(More)
This paper augments Boehm-Turner's model of agile and plan-based software development augmented with an AI search algorithm. The AI search finds the key factors that predict for the success of agile or traditional plan-based software developments. According to our simulations and AI search algorithm: (1) in no case did agile methods perform worse than(More)
Target audience: Software practitioners and researchers wanting to understand the state of the art in using data science for software engineering (SE). Content: In the age of big data, data science (the knowledge of deriving meaningful outcomes from data) is an essential skill that should be equipped by software engineers. It can be used to predict useful(More)
Using the tools of quantitative data science, software engineers that can predict useful information on new projects based on past projects. This tutorial reflects on the state-of-the-art in quantitative reasoning in this important field. This tutorial discusses the following: (a) when local data is scarce, we show how to adapt data from other organizations(More)