Context: We revisit our review of data quality within the context of empirical software
engineering eight years on from our PROMISE 2008 article. Objective: To assess the extent …

Data is a cornerstone of empirical software engineering (ESE) research and practice. Data
underpin numerous process and project management activities, including the estimation of …

Context Data collected during software engineering experiments might contain quality
problems, leading to wrong experimental conclusions. Objective We present a data quality …

Research based on datasets needs to determine the quality of the data that form the basis of
the results. To facilitate this type of research, the meaning of the data needs to be interpreted …

Data Quality (DQ) is a key element in any Data Science project to guarantee that its results
provide consistent and reliable information. Both process mining and data mining, as part of …

The socio-technical system supporting an organization's daily operations is becoming more
complex, with distributed infrastructures integrating heterogeneous technologies enacting …

Organizational data science projects provide organizations with evidence-based business
intelligence to improve their business processes (BPs). They require methodological …

Data classification is a data mining task that consists of an algorithm adjusted by a training
dataset that is used to predict an object's class (unclassified) on analysis. A significant part of …

Since its inception as a recognized sub-discipline, empirical software engineering (ESE) has
been plagued with data quality issues, and in recent years this has led to an increasing …

IN the last decade, a lot of attention worldwide has been brought by researchers,
organizations, and funders on the realization of Data Infrastructures (DIs), namely systems …