M
Mohammad Amin Alipour
Researcher at University of Houston
Publications - 52
Citations - 830
Mohammad Amin Alipour is an academic researcher from University of Houston. The author has contributed to research in topics: Computer science & Source code. The author has an hindex of 14, co-authored 46 publications receiving 555 citations. Previous affiliations of Mohammad Amin Alipour include Oregon State University & Association for Computing Machinery.
Papers
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Proceedings ArticleDOI
Comparing non-adequate test suites using coverage criteria
TL;DR: A large set of plausible criteria, including statement and branch coverage, as well as stronger criteria used in recent studies are evaluated: branch coverage and an intra-procedural acyclic path coverage perform best.
Journal ArticleDOI
Guidelines for Coverage-Based Comparisons of Non-Adequate Test Suites
TL;DR: This article presents the first extensive study that evaluates coverage criteria for the common case of non-adequate test suites, including basic criteria such as statement and branch coverage, as well as stronger criteria used in recent studies, including criteria based on program paths, equivalence classes of covered statements, and predicate states.
Proceedings ArticleDOI
On the limits of mutation reduction strategies
TL;DR: It is concluded that more effort should be focused on enhancing mutations than removing operators in the name of selective mutation for questionable benefit, and a simple theoretical framework for thinking about the absolute limits is provided.
Journal ArticleDOI
On the generalizability of Neural Program Models with respect to semantic-preserving program transformations
Md. Rafiqul Islam Rabin,Nghi D. Q. Bui,Ke Wang,Yijun Yu,Lingxiao Jiang,Mohammad Amin Alipour +5 more
TL;DR: The results show that even with small semantically preserving changes to the programs, these neural program models often fail to generalize their performance, and suggest that Neural program models based on data and control dependencies in programs generalize better than neural program model based only on abstract syntax trees (ASTs).
Journal ArticleDOI
Cause reduction: delta debugging, even without bugs
TL;DR: Suites produced by cause reduction provide effective quick tests for real‐world programs, including improving seeded symbolic execution, where using reduced tests can often double the number of additional branches explored.