Property-Aware Program Sampling

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Monitoring or profiling programs provides us with an understanding for its further improvement and analysis. Typically, for monitoring or profiling, the program is instrumented to execute additional code that collects necessary data. A problem is that program instrumentation is often reported to cause between 10% and 390% time and space overhead. A number of techniques based on statistical sampling have been proposed to reduce the instrumentation overhead. Statistical sampling based instrumentation techniques, although effective in reducing the overall overhead, often lead to poor coverage or less accurate results. In this work, we present a profiling technique based on property-aware program sampling. The key ideas are (i) to use program slicing to narrow down the scope of instrumentation to the sections of program relevant to the property of interest, (ii) to decompose large program slices into logically related slice fragments, and (iii) to apply statistical sampling on the set of slice fragments. Our experimental results show that our technique can collect profiles at high assurance levels, at a significantly lower overhead.

Bib Info

  author = {Harish Narayanappa and Mukul S. Bansal and Hridesh Rajan},
  title = {Property-Aware Program Sampling},
  booktitle = {PASTE '10: 9th ACM SIGPLAN-SIGSOFT Workshop on Program Analysis for Software Tools and Engineering},
  location = {Toronto, Canada},
  month = {June},
  year = {2010},