The video-game industry has recently grown from focused markets to mainstream. The advancements the industry has been enjoying motivated researches to propose techniques and tools to support the activities across the different phases of the game development lifecycle. Game testing is one of the crucial activities within the game development process. Due to the nature of game testing, many automated game testing techniques have been proposed in the literature. However, there is no framework that could be used to aid practitioners in selecting appropriate techniques suitable for their particular development efforts. In this paper we present an attribute-based framework to classify and compare these techniques and provide such aid to practitioners. The framework is also meant to guide researchers interested in proposing new game testing techniques. The paper discusses a number of prominent techniques against the framework. Analysis of the discussion reveals gaps and suggests open points for future research.

Protein–protein interaction networks are receiving increased attention due to their importance in understanding life at the cellular level. A major challenge in systems biology is to understand the modular structure of such biological networks. Although clustering techniques have been proposed for clustering protein–protein interaction networks, those techniques suffer from some drawbacks. The application of earlier clustering techniques to protein–protein interaction networks in order to predict protein complexes within the networks does not yield good results due to the small-world and power-law properties of these networks. In this paper, we construct a new clustering algorithm for predicting protein complexes through the use of genetic algorithms. We design an objective function for exclusive clustering and overlapping clustering. We assess the quality of our proposed clustering algorithm using two gold-standard data sets. Our algorithm can identify protein complexes that are significantly enriched in the gold-standard data sets. Furthermore, our method surpasses three competing methods: MCL, ClusterOne, and MCODE in terms of the quality of the predicted complexes. The source code and accompanying examples are freely available at http://faculty.kfupm.edu.sa/ics/eramadan/GACluster.zip .