Supporting the quality assurance of a scientific framework

The quality assurance of scientific software has to deal with special challenges of this type of software, including missing test oracles, the need for high performance computing, and the high priority of non-functional requirements. A scientific framework consists of common code, which provides solutions for several similar mathematical problems. The various possible uses of a scientific framework lead to a large variability in the framework. In addition to the challenges of scientific software, the quality assurance of a scientific framework needs to find a way of dealing with the large variability. In software product line engineering (SPLE), the idea is to develop a software platform and then use mass customization for the creation of a group of similar applications. In this thesis, we show how SPLE, in particular variability modeling, can be applied to support the quality assurance of scientific frameworks. One of the main contributions of this thesis is a process for the creation of reengineering variability models for a scientific framework based on its mathematical requirements. Reengineering means the adjustment of a software system to improve the software quality, mostly without changing the software’s functionality. In our research, the variability models are created for existing software and therefore we call them reengineering variability models. The created variability models are used for a systematic development of system test applications for the framework. Additionally, we developed a model-based method for test case derivation for the system test applications based on the variability models. Furthermore, we contribute a software product line test strategy for scientific frameworks. A test strategy strongly influences the test activities performed. Another main contribution of this thesis is the design of a quality assurance process for scientific frameworks, which combines the test activities of the test strategy with other quality assurance activities. We introduce a list of special characteristics for scientific software, which we use as rationale for the design of this process. We report on a case study, analyzing the feasibility and acceptance by developers for two parts of the design of the quality assurance process: variability model creation and desk-checking, a kind of lightweight review. Using FeatureIDE, an environment for feature-oriented software development as well as an automated test environment, we prototypically demonstrate the applicability of our approach.