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Abstract: We introduce a foundational theory, and a practical technique for assessing and improving test oracles by reducing the incidence of both false positives and false negatives. Our technique combines test case generation to reveal false positives and mutation testing to reveal false negatives. We applied the decision support tool that implements our oracle improvement technique to five real-world subjects. The experimental results show that the fault detection rate of the improved oracles increases, on average, by 52% (92% over the implicit oracle).
This seminar is about the paper: "Learning Natural Coding Conventions" Allamanis M. et al. FSE ’14.
Abstract: In search based test case generation, most of the research works focus on the single-objective formulation of the test case generation problem. However, there are a wide variety of multi- and many-objective optimization strategies that could offer advantages currently not investigated when addressing the problem of test case generation. Furthermore, existing techniques and available tools mainly handle test generation for programs with primitive inputs, such as numeric or string input.