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Software clustering is a useful technique to recover architecture of a software system. The results of clustering depend upon choice of entities, features, similarity measures and clustering algorithms. Different similarity measures have been used for determining similarity between entities during the clustering process. In software architecture recovery(More)
Clustering is a useful technique to group data entities. Many different algorithms have been proposed for software clustering. To combine the strengths of various algorithms, researchers have suggested the use of Consensus Based Techniques (CBTs), where more than one actors (e.g. algorithms) work together to achieve a common goal. Although the use of CBTs(More)
In recent years, there has been increasing interest in exploring clustering as a technique to recover the architecture of software systems. The efficacy of clustering depends not only on the clustering algorithm, but also on the choice of entities, features and similarity measures used during clustering. It is also important to understand characteristics of(More)
This paper proposes a feature selection technique for software clustering which can be used in the architecture recovery of software systems. The recovered architecture can then be used in the subsequent phases of software maintenance, reuse and re-engineering. A number of diverse features could be extracted from the source code of software systems,(More)
Daily large number of bug reports are received in large open and close source bug tracking systems. Dealing with these reports manually utilizes time and resources which leads to delaying the resolution of important bugs. As an important process in software maintenance, bug triaging process carefully analyze these bug reports to determine, for example,(More)
Web Applications (WAs) are complex systems and it is difficult to understand their architecture without proper documentation. Due to high pressure of meeting deadlines and short time to market, WAs are liable to be poorly structured and are rarely well documented. Even if documentation is present, it often does not comply with code because code is updated(More)
Artificial Neural Networks (ANN) performance depends on network topology, activation function, behaviors of data, suitable synapse's values and learning algorithms. Researchers used different learning algorithms to train ANN for getting high performance. Artificial Bee Colony (ABC) algorithm is one of the latest successfully Swarm Intelligence based(More)