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Abstract-With the fast advance of big data technology and analytics solutions, building high-quality big data computing services in different application domains is becoming a very hot research and application topic among academic and industry communities, and government agencies. Therefore, big data based applications are widely-used currently, such as recommendation, predication, and decision system. Nevertheless, there are increasing quality problems resulting in erroneous testing costs in enterprises and businesses. Current research work seldom discusses how to effectively validate big data applications to assure system quality. This paper focuses on big data system validation and quality assurance, and includes informative discussions about essential quality parameters, primary focuses, and validation process. Moreover, the paper discusses potential testing methods for big data application systems. Furthermore, the primary issues, challenges, and needs in testing big data application are presented.

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Software change impact analysis (CIA) is a key technique to identify unpredicted and potential effects caused by software changes. In this paper, we propose a new CIA technique based on a compact and effective representation for object oriented programs, called lattice of class and method dependence (LoCMD). This novel representation can effectively capture the dependences between classes and methods. Based on the LoCMD, our CIA technique calculates a ranked list of potential impacted methods according to a metric, impact factor, which corresponds to the priority of these methods to be inspected. Initial case study validates the reasonability of our two assumptions, and demonstrates the effectiveness of our technique.