A snowballing literature study on test amplification
Benjamin Danglot (Institut National de Recherche en Informatique et en Automatique (INRIA))
Oscar Vera-Perez (Institut National de Recherche en Informatique et en Automatique (INRIA))
Zhongxing Yu (KTH Royal Institute of Technology)
Andy Zaidman (TU Delft - Software Engineering)
Martin Monperrus (KTH Royal Institute of Technology)
Benoit Baudry (KTH Royal Institute of Technology)
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Abstract
The adoption of agile approaches has put an increased emphasis on testing, resulting in extensive test suites. These suites include a large number of tests, in which developers embed knowledge about meaningful input data and expected properties as oracles. This article surveys works that exploit this knowledge to enhance manually written tests with respect to an engineering goal (e.g., improve coverage or refine fault localization). While these works rely on various techniques and address various goals, we believe they form an emerging and coherent field of research, which we coin “test amplification”. We devised a first set of papers from DBLP, searching for all papers containing “test” and “amplification” in their title. We reviewed the 70 papers in this set and selected the 4 papers that fit the definition of test amplification. We use them as the seeds for our snowballing study, and systematically followed the citation graph. This study is the first that draws a comprehensive picture of the different engineering goals proposed in the literature for test amplification. 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.