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ASE 2020
Mon 21 - Fri 25 September 2020 Melbourne, Australia
Tue 22 Sep 2020 02:20 - 02:40 at Kangaroo - Test Generation Chair(s): Xusheng Xiao

Concolic execution and fuzzing are two complementary coverage-based testing techniques. How to achieve the best of both remains an open challenge. To address this research problem, we propose and evaluate Legion. Legion uses a variation of the Monte Carlo tree search (MCTS) framework from the AI literature to treat automated test generation as a problem of sequential decision-making under uncertainty. Its best-first search strategy provides a principled way to learn the most promising program states to investigate at each search iteration, based on observed rewards from previous iterations. Legion incorporates a form of directed fuzzing that we call approximate path-preserving fuzzing (APPFuzzing) to investigate program states selected by MCTS. APPFuzzing serves as the Monte Carlo simulation technique and is implemented by extending prior work on constrained sampling. We evaluate Legion against competitors in Test-Comp 2020, as well as measuring its sensitivity to hyperparameters, demonstrating its effectiveness on a wide variety of input programs.

Tue 22 Sep

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02:20 - 03:20
Test GenerationResearch Papers / Industry Showcase / Tool Demonstrations at Kangaroo
Chair(s): Xusheng Xiao Case Western Reserve University
Legion: Best-First Concolic Testing
Research Papers
Dongge Liu The Univeristy of Melbourne, Gidon Ernst LMU Munich, Toby Murray University of Melbourne, Australia, Benjamin I.P. Rubinstein University of Melbourne
The New Approach to IT Testing
Industry Showcase
MetPurity: A Learning-Based Tool of Pure Method Identification for Automatic Test Generation
Tool Demonstrations
Runze Yu Wuhan University, Youzhe Zhang Wuhan University, Jifeng Xuan Wuhan University