“…Res. 5(9), 38-45 39 migration from PP to OOP named as Genetic Algorithm (GA) [5], Local Search (LS) [6], Variable Neighborhood Search (VNS) [7] and Build Cluster Hierarchy (BCH) [8].…”
Section: Issn: 2320-5407mentioning
confidence: 99%
“…In this section four selected algorithms are described, named Genetic Algorithm (GA) [5], Local Search (LS) [6], Variable Neighborhood Search (VNS) [7] and Build Cluster Hierarchy (BCH) [8] for an empirical analysis. All four algorithms addressed different solutions for the same problem.…”
Section: Existing Algorithms For Design Migration:-mentioning
confidence: 99%
“…Processes like inheritance, mutation, selection and crossover are used. In [7], GA has been used to find a solution to the graph clustering problem. Previously, in our study, we had presented a GA based meta-heuristic approach that takes a procedural code and uses the underlying undirected graph G(V,E) as input and produces a scheme for clustering the graph to form k number of clusters.…”
Section: A Genetic Algorithm For Design Migration [5]:-mentioning
confidence: 99%
“…The solution is then iteratively improved by the GA as it searches for a better solution. The algorithm is programmed to stop when the current best solution cannot be improved any further for a consecutive t number of times [7]. The followings tasks are iteratively performed:…”
Section: A Genetic Algorithm For Design Migration [5]:-mentioning
confidence: 99%
“…C [7] presented a Variable Neighborhood Search (VNS) approach. The method provides a set of clusters that gives a clue for possible structure of the object oriented architecture.…”
“…Res. 5(9), 38-45 39 migration from PP to OOP named as Genetic Algorithm (GA) [5], Local Search (LS) [6], Variable Neighborhood Search (VNS) [7] and Build Cluster Hierarchy (BCH) [8].…”
Section: Issn: 2320-5407mentioning
confidence: 99%
“…In this section four selected algorithms are described, named Genetic Algorithm (GA) [5], Local Search (LS) [6], Variable Neighborhood Search (VNS) [7] and Build Cluster Hierarchy (BCH) [8] for an empirical analysis. All four algorithms addressed different solutions for the same problem.…”
Section: Existing Algorithms For Design Migration:-mentioning
confidence: 99%
“…Processes like inheritance, mutation, selection and crossover are used. In [7], GA has been used to find a solution to the graph clustering problem. Previously, in our study, we had presented a GA based meta-heuristic approach that takes a procedural code and uses the underlying undirected graph G(V,E) as input and produces a scheme for clustering the graph to form k number of clusters.…”
Section: A Genetic Algorithm For Design Migration [5]:-mentioning
confidence: 99%
“…The solution is then iteratively improved by the GA as it searches for a better solution. The algorithm is programmed to stop when the current best solution cannot be improved any further for a consecutive t number of times [7]. The followings tasks are iteratively performed:…”
Section: A Genetic Algorithm For Design Migration [5]:-mentioning
confidence: 99%
“…C [7] presented a Variable Neighborhood Search (VNS) approach. The method provides a set of clusters that gives a clue for possible structure of the object oriented architecture.…”
Management of legacy software and its code, generally written in procedural languages, is often costly and time-consuming. To help this management, a migration from procedural to object oriented paradigm could be a cost effective option. One approach for such migration can be based on the underlying dependency structure of the procedural source code. In this work, we propose a new heuristic algorithm that utilizes such structure for the design migration using agglomerative hierarchical clustering. The dependency structure that has been used involve functions, parameters and global data of the procedural code. Given a procedural code, the proposed approach produces candidate classes for an object oriented design. The proposed algorithm was tested against a database of procedural codes. The results obtained have been empirically validated using Jaccard similarity coefficient. It is observed that the proposed method yields 75.6% similarity with respect to the ground truth in the average case.
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