Скачать или смотреть [Onward23] Cloning And Beyond: A Quantum Solution to Duplicate Code

Cloning and Beyond: A Quantum Solution to Duplicate Code (Video, Onward! 2023)
Samyak Jhaveri, Alberto Krone-Martins, and Cristina V. Lopes
(University of California at Irvine, USA; University of California at Irvine, USA; University of California at Irvine, USA)

Abstract: Quantum computers are becoming a reality. The advantage of quantum computing is that it has the potential to solve computationally complex problems in a fixed amount time, independent of the size of the problem. However, the kinds of problems for which these computers are a good fit, and the ways to express those problems, are substantially different from the kinds of problems and expressions used in classical computing. Quantum annealers, in particular, are currently the most promising and available quantum computing devices in the short term. However, they are also the most foreign compared to classical programs, as they require a different kind of computational thinking.
In order to ease the transition into this new world of quantum computing, we present a novel quantum approach to a well-known software problem: code clone detection. We express code clone detection as a subgraph isomorphism problem that is mapped into a quadratic optimization problem, and solve it using a DWave quantum annealing computer. We developed a quantum annealing algorithm that compares Abstract Syntax Trees (AST) and reports an energy value that indicates how similar they are.


The motivation behind this research goes well beyond code duplicate detection: our approach paves the way into how to express software engineering problems as optimization problems that can be solved by quantum annealers.

Article: https://doi.org/10.1145/3622758.3622889

ORCID: https://orcid.org/0009-0006-0051-5858, https://orcid.org/0000-0002-2308-6623, https://orcid.org/0000-0003-0551-3908

Video Tags: Quantum-Based Code Clone Detection, Subgraph Isomorphism, Quantum Computing, Quadratic Unconstrained Discrete Optimization (QUDO), onward23papers-p43-p, doi:10.1145/3622758.3622889, orcid:0009-0006-0051-5858, orcid:0000-0002-2308-6623, orcid:0000-0003-0551-3908

Presentation at the Onward! 2023 conference, October 25–27, 2023, https://2023.splashcon.org/track/spla...
Sponsored by ACM SIGPLAN, ACM SIGAda,