Combinatorial testing and machine learning for automated test generation

Abstract

In this issue, we are pleased to present two papers that showcase innovative techniques in software testing in two different directions (memory-aware combinatorial test generation and survey about the use of machine learning for automated test generation). The first paper, ‘An investigation of distributed computing for combinatorial testing’ by Edmond La Chance and Sylvain Hallé, proposes the use of distributed computing to reduce the time and memory required for t-way test generation. The authors present a distributed graph colouring method and a distributed hypergraph vertex covering method for generating high-quality test suites. They also demonstrate how to build a distributed IPOG algorithm using these methods. (Recommended by Arnaud Gotlieb) The second paper, ‘The Integration of Machine Learning into Automated Test Generation: A Systematic Mapping Study’ by Afonso Fontes and Gregory Gay, investigates the integration of machine learning (ML) into automated test generation. Through a systematic study of 124 papers, the authors characterize the emerging research in this area and identify the testing practices, researcher goals, ML techniques applied, evaluation metrics, and challenges in integrating ML into testing. The results show that ML is used to generate inputs for different types of testing, improve the performance of existing generation methods, and generate test verdicts and oracles. The authors also identify common ML techniques used in this area, such as supervised and reinforcement learning, and the evaluation metrics used to assess the effectiveness of these techniques. (Recommended by Phil McMinn) The first paper contributes to the literature on combinatorial testing by demonstrating the effectiveness of distributed computing for test generation. The second paper highlights the potential of ML in automated test generation and provides insights into the challenges that researchers face when integrating ML into testing. We hope that these papers will inspire further research in these directions of software testing.