Simplifying and Isolating Failure-Inducing Input
IEEE Transactions on Software Engineering
Isolating cause-effect chains from computer programs
Proceedings of the 10th ACM SIGSOFT symposium on Foundations of software engineering
Revised Lectures on Software Visualization, International Seminar
Locating causes of program failures
Proceedings of the 27th international conference on Software engineering
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Reasoning about programs is mostly deduction: the reasoning from the abstract model to the concrete run. Deduction is useful because it allows us to predict properties of future runs—up to the point that a program will never fail its specification. However, even such a 100% correct program may still show a problem: the specification itself may be problematic, or deduction required us to abstract away some relevant property. To handle such problems, deduction is not the right answer—especially in a world where programs reach a complexity that makes them indistinguishable from natural phenomena. Instead, we should enrich our portfolio by methods proven in natural sciences, such as observation, induction, and in particular experimentation. In my talk, I will show how systematic experimentation automatically reveals the causes of program failures—in the input, in the program state, or in the program code.