We introduce equivalence modulo inputs (EMI), a simple, widely applicable methodology for validating optimizing compilers. Our key insight is to exploit the close interplay between (1) dynamically executing a program on some test inputs and (2) statically compiling the program to work on all possible inputs. Indeed, the test inputs induce a natural collection of the original program's EMI variants, which can help differentially test any compiler and specifically target the difficult-to-find miscompilations.To create a practical implementation of EMI for validating C compilers, we profile a program's test executions and stochastically prune its unexecuted code. Our extensive testing in eleven months has led to 147 confirmed, unique bug reports for GCC and LLVM alone. The majority of those bugs are miscompilations, and more than 100 have already been fixed.Beyond testing compilers, EMI can be adapted to validate program transformation and analysis systems in general. This work opens up this exciting, new direction.

Programming by Examples (PBE) has the potential to revolutionize end-user programming by enabling end users, most of whom are non-programmers, to create small scripts for automating repetitive tasks. However, examples, though often easy to provide, are an ambiguous specification of the user's intent. Because of that, a key impedance in adoption of PBE systems is the lack of user confidence in the correctness of the program that was synthesized by the system. We present two novel user interaction models that communicate actionable information to the user to help resolve ambiguity in the examples. One of these models allows the user to effectively navigate between the huge set of programs that are consistent with the examples provided by the user. The other model uses active learning to ask directed example-based questions to the user on the test input data over which the user intends to run the synthesized program. Our user studies show that each of these models significantly reduces the number of errors in the performed task without any difference in completion time. Moreover, both models are perceived as useful, and the proactive active-learning based model has a slightly higher preference regarding the users' confidence in the result.

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