TRANSMUT-SPARK: Transformation Mutation for Apache Spark

TL;DR

TRANSMUT-Spark is a tool that automates mutation testing for Apache Spark programs, helping developers identify faults in complex Big Data processing code to improve reliability and correctness.

Contribution

The paper introduces TRANSMUT-Spark, a novel tool that automates mutation testing specifically for Spark programs, streamlining fault detection in Big Data applications.

Findings

TRANSMUT-Spark successfully automates mutant generation and testing phases.

Experimental results validate the tool's effectiveness and scope.

The approach improves fault detection in Spark Big Data processing code.

Abstract

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 Processing. It hides the complexity inherent to Big Data parallel and distributed programming and processing through built-in functions, underlying parallel processes, and data management strategies. Nonetheless, programmers must cleverly combine these functions within programs and guide the engine to use the right data management strategies to exploit the large number of computational resources required by Big Data processing and avoid substantial production losses. Many programming details in data processing code within Spark programs are prone to false statements that need to be correctly and automatically tested. This paper explores the application of mutation testing in Spark programs, a fault-based testing technique that relies on fault simulation to evaluate and design test sets. The paper introduces the TRANSMUT-Spark solution for testing Spark programs. TRANSMUT-Spark automates the most laborious steps of the process and fully executes the mutation testing process. The paper describes how the tool automates the mutants generation, test execution, and adequacy analysis phases of mutation testing with TRANSMUT-Spark. It also discusses the results of experiments that were carried out to validate the tool to argue its scope and limitations.