Context: The increasing adoption of test-driven development results in software projects with strong test suites. These suites include a large number of test cases, in which developers embed knowledge about meaningful input data and expected properties in the form of oracles. Objective: This article surveys various works that aim at exploiting this knowledge in order to enhance these manually written tests with respect to an engineering goal (e.g., improve coverage of changes or increase the accuracy of fault localization). While these works rely on various techniques and address various goals, we believe they form an emerging and coherent field of research, and which we call "test amplification". Method: We devised a first set of papers based on our knowledge of the literature (we have been working in software testing for years). Then, we systematically followed the citation graph. Results: This survey is the first that draws a comprehensive picture of the different engineering goals proposed in the literature for test amplification. In particular, we note that the goal of test amplification goes far beyond maximizing coverage only. Conclusion: We believe that this survey will help researchers and practitioners entering this new field to understand more quickly and more deeply the intuitions, concepts and techniques used for test amplification.

Descartes is a tool that implements extreme mutation operators and aims at finding pseudo-tested methods in Java projects. It leverages the efficient transformation and runtime features of PITest. The demonstration compares Descartes with Gregor, the default mutation engine provided by PITest, in a set of real open source projects. It considers the execution time, number of mutants created and the relationship between the mutation scores produced by both engines. It provides some insights on the main features exposed byDescartes.