A self-adaptive software system modifies its behavior at runtime in response to changes within the system or in its execution environment. The ful- fillment of the system requirements needs to be guaranteed even in the presence of adverse conditions and adaptations. Thus, a key challenge for self-adaptive software systems is assurance. Traditionally, confidence in the correctness of a system is gained through a variety of activities and processes performed at de- velopment time, such as design analysis and testing. In the presence of self- adaptation, however, some of the assurance tasks may need to be performed at runtime. This need calls for the development of techniques that enable contin- uous assurance throughout the software life cycle. Fundamental to the develop- ment of runtime assurance techniques is research into the use of models at runtime

Using Models at Runtime to Address Assurance for Self-Adaptive Systems.

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Using models at runtime to address assurance for self-adaptive systems

A comprehensive approach to the recovery of design pattern instances based on sub-patterns and method signatures

The generic model query language GMQL - Conceptual specification, implementation, and runtime evaluation

Design patterns helpful for software development are the reusable abstract documents which provide acceptable solutions for the recurring design problems. But in the process of reverse engineering, it is often desired to identify as well as recognize design pattern from source code, as it improves maintainability and documentation of the source code. In this study, the process of software design pattern recognition is presented which is based on machine learning techniques. Firstly, a training dataset is developed which is based on software metrics. Subsequently, machine learning algorithms such as Layer Recurrent Neural Network and Decision Tree are applied for patterns detection process. In order to evaluate the proposed study, an open source software i.e., JHotDraw 7.0.6 has been used for the recognition of design patterns.

Software design pattern recognition using machine learning techniques

Design Pattern Detection is a part of many solutions to Software Engineering difficulties. It is a part of reengineering process and thus gives important information to the designer. Design Pattern existence improve the program understanding and software maintenance. With the help of these patterns specific design problem can be solved and object oriented design become more flexible and reusable. Hence a reliable design pattern mining is required. Here we are applying graph decomposition followed by graph isomorphism technique for design pattern detection.

https://link.springer.com/content/pdf/10.1007%2F978-3-642-14825-5_10.pdf

A New Approach for Detecting Design Patterns by Graph Decomposition and Graph Isomorphism

Design Pattern Detection has been documented so far in the literature. The knowledge of design Pattern existence in the program improves the program understanding and software maintenance. Design pattern is a technology for design reuse. Experts store their experiences in the form of design patterns. Reengineering done by novice users will be successful if a reliable design pattern mining exists. There are 23 design patterns defined by experts. Here we are taking the UML diagrams corresponding to design pattern and corresponding to source code. Our main aim is to find out whether a particular design pattern exists in system design (source code) or not. For this we have extracted the relationship graphs (consisting of nodes and edges), and then tried to detect the design pattern. In this paper we have applied normalized cross correlation and taking design pattern as a template tried to find out its existence in system design. Normalized cross correlation (NCC) has been used extensively for many machine vision applications. Normalized cross correlation has been commonly used to evaluate the degree of similarity or dissimilarity between two images.

Design Pattern Detection by normalized cross correlation

Design Pattern Detection is a part of many solutions to Software Engineering difficulties. It is a part of reengineering process and thus gives important information to the designer. Design Pattern existence improve the program understanding and software maintenance. With the help of these patterns specific design problem can be solved and object oriented design become more flexible and reusable. Hence a reliable design pattern mining is required. A GIS is an information system designed to work with data referenced by spatial / geographical coordinates. Here we are detecting design patterns so that it can be used as a conceptual tool to cope with recurrent problems appearing in the GIS domain. In this way, GIS applications can evolve smoothly, because maintenance is achieved by focusing on different concerns at different times.

Design pattern mining for GIS application using graph matching techniques

Design Pattern Detection is a part of many solutions to Software Engineering problems. It is a part of reengineering process and thus gives significant information to the designer. Design Pattern improves the program understanding and software maintenance. Therefore, a reliable design pattern discovery is required. Graph theoretic approaches have been used for design pattern detection in past. Here we are applying state space representation of graph matching algorithm for design pattern detection. State space representation easily describes the graph matching process. Using our approach variants of each design pattern as well as any occurrence of a design pattern can be detected.

Design Pattern Mining Using State Space Representation of Graph Matching

In many object oriented softwares, there are recurring patterns of classes. Design pattern instances are important for program understanding and software maintenance.Hence a reliable design pattern mining is required. Here we are applying decision tree approach followed by subgraph isomorphism technique for design pattern detection.

Akshara Pande

Papers

A New Approach for Detecting Design Patterns by Graph Decomposition and Graph Isomorphism

Design Pattern Detection by normalized cross correlation

Design pattern mining for GIS application using graph matching techniques

Design Pattern Mining Using State Space Representation of Graph Matching

A Decision Tree Approach for Design Patterns Detection by Subgraph Isomorphism