Deep Imperative Mutations have Less Impact
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @Article{langdon:2024:ASE,
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author = "W. B. Langdon and David Clark",
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title = "Deep Imperative Mutations have Less Impact",
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journal = "Automated Software Engineering",
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year = "2025",
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volume = "32",
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pages = "article number 6",
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keywords = "genetic algorithms, genetic programming, genetic
improvement, Software testing, robust software, fault
masking, fault localization, resilience, repair,
automatic code optimisation, failed disruption
propagation, FDP, Voas PIE (propagation, infection, and
execution), fitness landscape, information theory,
local search, SBSE, image processing, RNAfold",
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ISSN = "0928-8910",
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URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/langdon_2024_ASE.pdf",
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DOI = "doi:10.1007/s10515-024-00475-4",
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size = "39 pages",
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abstract = "Information theory and entropy loss predict deeper
more hierarchical software will be more robust. Using
the genetic improvement (GI) tool MAGPIE we measure the
impact of source code mutations and how this varies
with execution depth in two diverse multi-level nested
software. gem5 is a million line single threaded
state-of-the-art C++ discrete time VLSI circuit
simulator, whilst PARSEC VIPS is a non-deterministic
parallel computing multi-threaded image processing
benchmark written in C. More than 53 percent (VIPS) 28
percent (gem5, omitting obviously equivalent) of
mutants compile and generate identical results to the
original program. We observe 12 percent and 16 percent
Failed Disruption Propagation (FDP). Excluding internal
errors, exceptions and asserts, most faults below 30
nested function levels which are Executed and Infect
data or divert control flow are not Propagated to the
output, i.e. these deep PIE changes have no visible
external effect. Suggesting (where it relies on runtime
analysis or testing) automatic software engineering on
highly structured code will be hard.",
- }
Genetic Programming entries for
William B Langdon
David Clark
Citations