Partial Orderings as Heuristic for Multi-Objective Model-Based Reasoning
CoRR(2023)
摘要
Model-based reasoning is becoming increasingly common in software
engineering. The process of building and analyzing models helps stakeholders to
understand the ramifications of their software decisions. But complex models
can confuse and overwhelm stakeholders when these models have too many
candidate solutions. We argue here that a technique based on partial orderings
lets humans find acceptable solutions via a binary chop needing $O(log(N))$
queries (or less). This paper checks the value of this approach via the iSNEAK
partial ordering tool. Pre-experimentally, we were concerned that (a)~our
automated methods might produce models that were unacceptable to humans; and
that (b)~our human-in-the-loop methods might actual overlooking significant
optimizations. Hence, we checked the acceptability of the solutions found by
iSNEAK via a human-in-the-loop double-blind evaluation study of 20 Brazilian
programmers. We also checked if iSNEAK misses significant optimizations (in a
corpus of 16 SE models of size ranging up to 1000 attributes by comparing it
against two rival technologies (the genetic algorithms preferred by the
interactive search-based SE community; and the sequential model optimizers
developed by the SE configuration community~\citep{flash_vivek}). iSNEAK 's
solutions were found to be human acceptable (and those solutions took far less
time to generate, with far fewer questions to any stakeholder). Significantly,
our methods work well even for multi-objective models with competing goals (in
this work we explore models with four to five goals). These results motivate
more work on partial ordering for many-goal model-based problems.
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关键词
heuristic,multi-objective,model-based
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