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Identifying similar code in software systems can assist many software engineering tasks such as program understanding and software refactoring. While most approaches focus on identifying code that looks alike, some techniques aim at detecting code that functions alike. Detecting these functional clones - code that functions alike - in object oriented languages remains an open question because of the difficulty in exposing and comparing programs' functionality effectively. We propose a novel technique, In-Vivo Clone Detection, that detects functional clones in arbitrary programs by identifying and mining their inputs and outputs. The key insight is to use existing workloads to execute programs and then measure functional similarities between programs based on their inputs and outputs, which mitigates the problems in object oriented languages reported by prior work. We implement such technique in our system, HitoshiIO, which is open source and freely available. Our experimental results show that HitoshiIO detects more than 800 functional clones across a corpus of 118 projects. In a random sample of the detected clones, HitoshiIO achieves 68+% true positive rate with only 15% false positive rate.
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Towards a cognizant virtual software modeling assistant using model clones
We present our new ideas on taking the first steps towards cultivating synergy between model-driven engineering (MDE), machine learning, and software clones. Specifically, we describe our vision in realizing a cognizant virtual software modeling assistant that uses the latter two to improve software design and MDE. Software engineering has benefited greatly from knowledge-based cognizant source code completion and assistance, but MDE has few and limited analogous capabilities. We outline our research directions by describing our vision for a prototype assistant that provides suggestions to modelers performing model creation or extension in the form of 1) complete models for insertion or guidance, and 2) granular single-step operations. These suggestions are derived by detecting clones of the in-progress model and existing domain, organizational, and exemplar models. We overview our envisioned workflow between modeler and assistant, and, using Simulink as an example, illustrate different manifestations including multiple overlays with percentages and employing variant elements.
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- Award ID(s):
- 1849632
- PAR ID:
- 10104898
- Date Published:
- Journal Name:
- Proceedings of the 41st International Conference on Software Engineering: New Ideas and Emerging Results
- Page Range / eLocation ID:
- 21 - 24
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ICSE-NIER.2019.00014
