Context MapReduce is a processing model used in Big Data to facilitate the analysis of large
data under a distributed architecture. Objective The aim of this study is to identify and …

Big Data is reforming many industrial domains by providing decision support through
analyzing large data volumes. Big Data testing aims to ensure that Big Data systems run …

Over the last few years, there has been a rising trend towards the field of distributed testing
where the implementation under test (IUT) has physical distributed ports. However, running …

New processing models are being adopted in Big Data engineering to overcome the
limitations of traditional technology. Among them, MapReduce stands out by allowing for the …

MapReduce is a parallel data processing paradigm oriented to process large volumes of
information in data-intensive applications, such as Big Data environments. A characteristic of …

Big Data programs are those that process large data exceeding the capabilities of traditional
technologies. Among newly proposed processing models, MapReduce stands out as it …

Among the current technologies to analyse large data, the MapReduce processing model
stands out in Big Data. MapReduce is implemented in frameworks such as Hadoop, Spark …

This paper proposes transmut‐Spark for automating mutation testing of big data processing
code within Spark programs. Apache Spark is an engine for big data analytics/processing …

Programs that process a large volume of data generally run in a distributed and parallel
architecture, such as the programs implemented in the processing model MapReduce. In …

We propose TRANSMUT-Spark, a tool that automates the mutation testing process of Big
Data processing code within Spark programs. Apache Spark is an engine for Big Data …