A systematic mapping addressing Hyper-Heuristics within Search-based Software Testing

Abstract

Search-based Software Testing (SBST) is a research field where testing a software product is formulated as an optimization problem . It is an active sub-area of Search-based Software Engineering (SBSE) where many studies have been published and some reviews have been carried out. The majority of studies in SBST has been adopted meta-heuristics while hyper-heuristics have a long way to go. Moreover, there is still a lack of studies to perceive the state-of-the-art of the use of hyper-heuristics within SBST. The objective of this work is to investigate the adoption of hyper-heuristics for Software Testing highlighting the current efforts and identifying new research directions. A Systematic mapping study was carried out with 5 research questions considering papers published up to may/2019, and 4 different bases. The research questions aims to find out, among other things, what are the hyper-heuristics used in the context of Software Testing, for what problems hyper-heuristics have been applied, and what are the objective functions in the scope of Software Testing. A total of 734 studies were found via the search strings and 164 articles were related to Software Testing. However, from these, only 26 papers were actually in accordance with the scope of this research and 3 more papers were considered due to snowballing or expert’s suggestion, totalizing 29 selected papers. Few different problems and application domains where hyper-heuristics have been considered were identified. Differently from other communities (Operational Research, Artificial Intelligence), SBST has little explored the benefits of hyper-heuristics which include generalization and less difficulty in parameterization. Hence, it is important to further investigate this area in order to alleviate the effort of practitioners to use such an approach in their testing activities.