Published November 23, 2022 | Version v1
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IFRIT: Focused Testing through Deep Reinforcement Learning

  • 1. University of Genoa, FBK-ICT
  • 2. University of Verona
  • 3. University of Genoa
  • 4. Università della Svizzera italiana

Description

Software is constantly changing as developers add new features or make changes. This directly impacts the effectiveness of the test suite associated with that software, especially when the new modifications are in an area where no test case exists. This article addresses the issue of developing a high-quality test suite to repeatedly cover a given point in a program, with the ultimate goal of exposing faults affecting the given program point. Our approach, IFRIT, uses Deep Reinforcement Learning to generate diverse inputs while keeping a high level of reachability of the desired program point. IFRIT achieves better results than state-of-the-art and baseline tools, improving reachability, diversity and fault detection.

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TR-Precrime-2022-05.pdf

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Additional details

Related works

Is published in
Conference paper: 10.1109/ICST53961.2022.00013 (DOI)

Funding

PRECRIME – Self-assessment Oracles for Anticipatory Testing 787703
European Commission