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In recent years, there has been a growing consensus among researchers regarding the dual nature of code clones. While some instances of code are valuable for reuse or extraction as components, the utilization of specific code segments can pose significant maintenance challenges for developers. Consequently, the judicious management of code clones has emerged as a pivotal solution to address these issues. Nevertheless, it remains critical to ascertain the number of code clones within a project, and identify components where code clones are more concentrated. In this paper, we introduce three novel metrics, namely Clone Distribution, Clone Density, and Clone Entropy (the dispersion of code clone within a project), for the quantification and characterization of code clones. We have formulated associated mathematical expressions to precisely represent these code clone metrics. We collected a dataset covering three different domains of Java projects, formulated research questions for the proposed three metrics, conducted a large-scale empirical study, and provided detailed numerical statistics. Furthermore, we have introduced a novel clone visualization approach, which effectively portrays Clone Distribution and Clone Density. Developers can leverage this approach to efficiently identify target clones. By reviewing clone code concerning its distribution, we have identified nine distinct code clone patterns and summarized specific clone management strategies that have the potential to enhance the efficiency of clone management practices. Our experiments demonstrate that the proposed code clone metrics provide valuable insights into the nature of code clones, and the visualization approach assists developers in inspecting and summarizing clone code patterns.
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Semantic code clone detection for enterprise applications
Enterprise systems are widely adopted across industries as methods of solving complex problems. As software complexity increases, the software's codebase becomes harder to manage and maintenance costs raise significantly. One such source of cost-raising complexity and code bloat is that of code clones. We proposed an approach to identify semantic code clones in enterprise frameworks by using control flow graphs (CFGs) and applying various proprietary similarity functions to compare enterprise targeted metadata for each pair of CFGs. This approach enables us to detect semantic code clones with high accuracy within a time complexity of O(n2) where n is equal to the number of CFGs composed in the enterprise application (usually around hundreds). We demonstrated our solution on a blind study utilizing a production enterprise application.
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- Award ID(s):
- 1854049
- PAR ID:
- 10203748
- Date Published:
- Journal Name:
- Proceedings of the 35th Annual ACM Symposium on Applied Computing (SAC '20)
- Page Range / eLocation ID:
- 129 to 131
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Detecting “similar code” is useful for many software engineering tasks. Current tools can help detect code with statically similar syntactic and–or semantic features (code clones) and with dynamically similar functional input/output (simions). Unfortunately, some code fragments that behave similarly at the finer granularity of their execution traces may be ignored. In this paper, we propose the term “code relatives” to refer to code with similar execution behavior. We define code relatives and then present DyCLINK, our approach to detecting code relatives within and across codebases. DyCLINK records instruction-level traces from sample executions, organizes the traces into instruction-level dynamic dependence graphs, and employs our specialized subgraph matching algorithm to efficiently compare the executions of candidate code relatives. In our experiments, DyCLINK analyzed 422+ million prospective subgraph matches in only 43 minutes. We compared DyCLINK to one static code clone detector from the community and to our implementation of a dynamic simion detector. The results show that DyCLINK effectively detects code relatives with a reasonable analysis time.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3341105.3374117
