Predictive testing: amplifying the effectiveness of software testing

  • Authors:

  • Pallavi Joshi;Koushik Sen;Mark Shlimovich

  • Affiliations:

  • University of California, Berkeley;University of California, Berkeley;University of California, Berkeley

  • Venue:
  • The 6th Joint Meeting on European software engineering conference and the ACM SIGSOFT symposium on the foundations of software engineering: companion papers
  • Year:
  • 2007

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Abstract

Testing with manually generated test cases often results in poor coverage and fails to discover many corner case bugs and security vulnerabilities. Automated test generation techniques based on static or symbolic analysis usually do not scale beyond small program units. We propose predictive testing, a new method for amplifying the effectiveness of existing test cases using symbolic analysis. We assume that a software system has an associated test suite consisting of a set of test inputs and a set of program invariants, in the form of a set of assert statements that the software must always satisfy. Predictive testing uses a combination of concrete and symbolic execution, similar to concolic execution, on the provided test inputs to discover if any of the assertions encountered along a test execution path could be violated for some closely related inputs. We extend predictive testing to catch bugs related to memory-safety violations, integer overflows, and string-related vulnerabilities. Furthermore, we propose a novel technique that leverages the results of unit testing to hoist assertions located deep inside the body of a unit function to the beginning of the unit function. This enables predictive testing to encounter assertions more often in test executions and thereby significantly amplifies the effectiveness of testing.