Abstract-Debugging is difficult and costly. As a human programmer looks for a bug, it would be helpful to see a complete trace of events leading to the point of failure. Unfortunately, full tracing is simply too slow to use in deployment, and may even be impractical during testing.We aid post-deployment debugging by giving programmers additional information about program activity shortly before failure. We use latent information in post-failure memory dumps, augmented by low-overhead, tunable run-time tracing. Our results with a realistically-tuned tracing scheme show low enough overhead (0-5%) to be used in production runs. We demonstrate several potential uses of this enhanced information, including a novel postmortem static slice restriction technique and a reduced view of potentially-executed code. Experimental evaluation shows our approach to be very effective, such as shrinking stack-sensitive interprocedural static slices by 49-78% in larger applications.

Program coverage is used across many stages of software development. While common during testing, program coverage has also found use outside the test lab, in production software. However, production software has stricter requirements on run-time overheads, and may limit possible program instrumentation. Thus, optimizing the placement of probes to gather program coverage is important. We introduce and study the problem of customized program coverage optimization. We generalize previous work that optimizes for complete coverage instrumentation with a system that adapts optimization to customizable program coverage requirements. Specifically, our system allows a user to specify desired coverage locations and to limit legal instrumentation locations. We prove that the problem of determining optimal coverage probes is NP-hard, and we present a solution based on mixed integer linear programming. Due to the computational complexity of the problem, we also provide two practical approximation approaches. We evaluate the effectiveness of our approximations across a diverse set of benchmarks, and show that our techniques can substantially reduce instrumentation while allowing the user immense freedom in defining coverage requirements. When naïve instrumentation is dense or expensive, our optimizations succeed in lowering execution time overheads. CCS Concepts •Software and its engineering → Software testing and debugging; Software post-development issues; Software performance; •Mathematics of computing → Integer programming;

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