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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Modern software engineering practices rely on program comprehension as the most basic underlying component for improving developer productivity and software reliability. Software developers are often tasked to work with unfamiliar code in order to remove security vulnerabilities, port and refactor legacy code, and enhance software with new features desired by users. Automatic identification of behavioral clones, or behaviorally-similar code, is one program comprehension technique that can provide developers with assistance. The idea is to identify other code that "does the same thing" and that may be more intuitive; better documented; or familiar to the developer, to help them understand the code at hand. Unlike the detection of syntactic or structural code clones, behavioral clone detection requires executing workloads or test cases to find code that executes similarly on the same inputs. However, a key problem in behavioral clone detection that has not received adequate attention is the "preponderance of the evidence" problem, which advocates for more convincing evidence from nontrivial test case executions to gain confidence in the behavioral similarities. In other words, similar outputs for some inputs matter more than for others. We present a novel system, SABER, to address the "preponderance of the evidence" problem, for which we adapt the legal metaphor of "more likely to be true than not true" burden of proof. We develop a novel test case generation methodology with three primary dynamic analysis techniques for identifying important behavioral clones. Further, we investigate filtering and weighting schemes to guide developers toward the most convincing behavioral similarities germane to specific software engineering tasks, such as code review, debugging, and introducing new features.more » « less
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When software engineering researchers discuss "similar" code, we often mean code determined by static analysis to be textually, syntactically or structurally similar, known as code clones (looks alike). Ideally, we would like to also include code that is behaviorally or functionally similar, even if it looks completely different. The state of the art in detecting these behavioral clones focuses on checking the functional equivalence of the inputs and outputs of code fragments, regardless of its internal behavior (focusing only on input and output states). We argue that with an advance in dynamic code clone detection towards detecting behavioral clones (i.e., those with similar execution behavior), we can greatly increase the applications of behavioral clones as a whole for general program understanding tasks.more » « less
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When software engineering researchers discuss "similar" code, we often mean code determined by static analysis to be textually, syntactically or structurally similar, known as code clones (looks alike). Ideally, we would like to also include code that is behaviorally or functionally similar, even if it looks completely different. The state of the art in detecting these behavioral clones focuses on checking the functional equivalence of the inputs and outputs of code fragments, regardless of its internal behavior (focusing only on input and output states). We argue that with an advance in dynamic code clone detection towards detecting behavioral clones (i.e., those with similar execution behavior), we can greatly increase the applications of behavioral clones as a whole for general program understanding tasks.more » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ICSE-NIER.2019.00014
