Augmenting Source Code Lines with Sample Variable Values

Source code is inherently abstract, which makes it difficult to understand. Activities such as debugging can reveal concrete runtime details, including the values of variables. However, they require that a developer explicitly requests these data for a specific execution moment. We present a simple approach, RuntimeSamp, which collects sample variable values during normal executions of a program by a programmer. These values are then displayed in an ambient way at the end of each line in the source code editor. We discuss questions which should be answered for this approach to be usable in practice, such as how to efficiently record the values and when to display them. We provide partial answers to these questions and suggest future research directions

A real explanation for heterogeneous investment dynamics

Household investment, that is investment in consumer durables and housing, leads non-residential fixed investment over the U.S. business cycle. This observation represents a potent challenge to real business cycle (RBC) theory. First of all the theory has been unable to account for it. In addition, research suggests the observation is driven by monetary shocks, supporting the view that these shocks play a leading role in the U.S. business cycle. This paper shows that RBC theory is consistent with the investment dynamics after all. It does so by generalizing the standard home production environment to take into account the fact that household capital is useful in market production.Monetary theory ; Business cycles

DSpot: Test Amplification for Automatic Assessment of Computational Diversity

Context: Computational diversity, i.e., the presence of a set of programs that all perform compatible services but that exhibit behavioral differences under certain conditions, is essential for fault tolerance and security. Objective: We aim at proposing an approach for automatically assessing the presence of computational diversity. In this work, computationally diverse variants are defined as (i) sharing the same API, (ii) behaving the same according to an input-output based specification (a test-suite) and (iii) exhibiting observable differences when they run outside the specified input space. Method: Our technique relies on test amplification. We propose source code transformations on test cases to explore the input domain and systematically sense the observation domain. We quantify computational diversity as the dissimilarity between observations on inputs that are outside the specified domain. Results: We run our experiments on 472 variants of 7 classes from open-source, large and thoroughly tested Java classes. Our test amplification multiplies by ten the number of input points in the test suite and is effective at detecting software diversity. Conclusion: The key insights of this study are: the systematic exploration of the observable output space of a class provides new insights about its degree of encapsulation; the behavioral diversity that we observe originates from areas of the code that are characterized by their flexibility (caching, checking, formatting, etc.).Comment: 12 page