Open Access
Refine
Document Type
- Conference Proceeding (104) (remove)
Is part of the Bibliography
- yes (104)
Keywords
- virtual reality (5)
- Business Process Management Systems (2)
- Fuzzy Logic (2)
- visualization (2)
- Assignment Automation (1)
- Augmented Reality (1)
- Business Process Modeling Notation (1)
- Business Process Modelling (1)
- Ethik (1)
- Git (1)
- KI (1)
- Re- source Assignment Automation (1)
- Staff Assignment Algorithms (1)
- Systems Modeling Language (SysML) (1)
- Technikfolgenabschätzung (1)
- artificial consciousness (1)
- artificial neural networks (1)
- business process management (1)
- business process mining (1)
- code coverage (1)
- data pipelines (1)
- data stream processing (1)
- event stream processing (1)
- event-driven architecture (1)
- graph embeddings (1)
- machine learning (1)
- process analysis (1)
- process mining (1)
- rule-based expert system (1)
- software configuration management (1)
- software design pattern detection (1)
- software test coverage (1)
- software testing (1)
- systems engineering (1)
- systems modeling (1)
- version control systems (1)
Institute
As the amount of software source code increases, manual approaches for documentation or detection of software design patterns in source code become inefficient relative to the value. Furthermore, typical automatic pattern detection tools are limited to a single programming language. To address this, our Design Pattern Detection using Machine Learning (DPDML) offers a generalized and programming language agnostic approach for automated design pattern detection based on machine learning (ML). The focus of our evaluation was on ensuring DPDML can reasonably detect one design pattern in the structural, creational, and behavioral category for two popular programming languages (Java and C#). 60 unique Java and C# code projects were used to train the artificial neural network (ANN) and 15 projects were then used to test pattern detection. The results show the feasibility and potential for pursuing an ANN approach for automated design pattern detection.