DSpot: Test Amplification for Automatic Assessment of Computational Diversity
Benoit Baudry, Simon Allier, Marcelino Rodriguez-Cancio, Martin, Monperrus

TL;DR
This paper introduces DSpot, a test amplification technique that automatically assesses computational diversity among program variants by exploring input and output spaces, revealing behavioral differences outside specified input domains.
Contribution
The work presents a novel method using test amplification to quantify computational diversity, enhancing detection of behavioral differences in program variants beyond traditional testing.
Findings
Test amplification increases input coverage tenfold.
Effective detection of software diversity among variants.
Behavioral diversity originates from flexible code areas.
Abstract
Context: Computational diversity, i.e., the presence of a set of programs that all perform compatible services but that exhibit behavioral differences under certain conditions, is essential for fault tolerance and security. Objective: We aim at proposing an approach for automatically assessing the presence of computational diversity. In this work, computationally diverse variants are defined as (i) sharing the same API, (ii) behaving the same according to an input-output based specification (a test-suite) and (iii) exhibiting observable differences when they run outside the specified input space. Method: Our technique relies on test amplification. We propose source code transformations on test cases to explore the input domain and systematically sense the observation domain. We quantify computational diversity as the dissimilarity between observations on inputs that are outside the…
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Taxonomy
TopicsSoftware Engineering Research · Advanced Malware Detection Techniques · Software Reliability and Analysis Research
