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HyperPUT: generating synthetic faulty programs to challenge bug-finding tools.

Riccardo Felici1, Laura Pozzi1, Carlo A Furia2

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Summary
This summary is machine-generated.

This study introduces HyperPUT, an automated technique for generating C programs with seeded bugs. HyperPUT aids in creating diverse test cases to evaluate and advance automated bug detection tools.

Keywords:
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Area of Science:

  • Software Engineering
  • Automated Software Engineering
  • Program Analysis

Background:

  • Reliable comparison of automated bug detection techniques requires comprehensive collections of programs with known bugs.
  • Existing benchmarks often rely on manually curated real-world bugs or synthetic bugs seeded into real-world programs, which are time-consuming to create or extend.

Purpose of the Study:

  • To propose and evaluate HyperPUT, a novel approach for automatically generating C programs with seeded bugs.
  • To provide a complementary method for creating diverse and challenging datasets for bug-finding tool evaluation.

Main Methods:

  • HyperPUT automatically generates C programs by starting with a "seed" bug.
  • It incrementally applies program transformations, such as introducing conditionals and loops, to reach a desired program size.
  • The generated programs contain seeded bugs suitable for testing bug-finding tools.

Main Results:

  • HyperPUT successfully generated buggy programs that effectively challenge modern bug-finding tools.
  • The characteristics of bugs generated by HyperPUT show comparability to those found in existing benchmarks.
  • The approach demonstrated its capability to create diverse and challenging test cases.

Conclusions:

  • HyperPUT offers a valuable, automated method for generating programs with seeded bugs.
  • This technique can significantly support research in automated bug detection by facilitating empirical evaluation.
  • It provides a scalable and efficient way to expand benchmark collections for software testing research.