What does it mean that an encoding is fully abstract? What does itnotmean? In this position paper, we want to help the reader to evaluate the real benefits of using such a notion when studying the expressiveness of programming languages. Several examples and counterexamples are given. In some cases, we work at a very abstract level; in other cases, we give concrete samples taken from the field of process calculi, where the theory of expressiveness has been mostly developed in the last years.

In this paper, we comparatively analyze some mainstream calculi for mobility and distribution, together with some of their variants: asynchronous π-calculus, distributed π-calculus, and some dialects of Mobile/Boxed/Safe ambients. In particular, we focus on their relative expressive power, i.e. we try to encode every language in the other while respecting some reasonable properties. According to the possibility or the impossibility for such results, we set up a taxonomy of these languages. Our study enables understanding, for every pair of calculi, which features of one can be rendered in the other and how this is possible, or which features cannot be rendered and why this is impossible.

Diverse test sets are able to expose bugs that test sets generated with structural coverage techniques cannot discover. Input-diverse test set generators have been shown to be effective for this, but also have limitations: e.g., they need to be complemented with semantic information derived from the Software Under Test. We demonstrate how to drive the test set generation process with semantic information in the form of output diversity. We present the first totally automatic output sampling for output diversity unit test set generation tool, called OutGen. OutGen transforms a program into an SMT formula in bit-vector arithmetic. It then applies universal hashing in order to generate an output-based diverse set of inputs. The result offers significant diversity improvements when measured as a high output uniqueness count. It achieves this by ensuring that the test set's output probability distribution is uniform, i.e. highly diverse. The use of output sampling, as opposed to any of input sampling, CBMC, CAVM, behaviour diversity or random testing improves mutation score and bug detection by up to 4150% and 963% respectively on programs drawn from three different corpora: the R-project, SIR and CodeFlaws. OutGen test sets achieve an average mutation score of up to 92%, and 70% of the test sets detect the defect. Moreover, OutGen is the only automatic unit test generation tool that is able to detect bugs on the real number C functions from the R-project.