Rashid Naseem

Improved Similarity Measures for Software Clustering

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 domain the Jaccard and the Unbiased Ellenberg measures have shown better results than other measures for binary and non-binary features respectively. In this paper we analyze the Russell and Rao measure for binary features to show the conditions under which its performance is expected to be better than that of Jaccard. We also show how our proposed Jaccard-NM measure is suitable for software clustering and propose its counterpart for non-binary features. Experimental results indicate that our proposed Jaccard-NM measure and Russell & Rao measure perform better than Jaccard measure for binary features, while for non-binary features, the proposed Unbiased Ellenberg-NM measure produces results which are closer to the decomposition prepared by experts.

Cooperative clustering for software modularization

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 has been explored in various disciplines, no work has been done for modularizing software. In this paper, the main research question we investigate is whether the Cooperative Clustering Technique (CCT), a type of CBT, can improve software modularization results. The main contributions of this paper are as follows. First, we propose our CCT in which more than one similarity measure cooperates during the hierarchical clustering process. To this end, we present an analysis of well-known measures. Second, we present a cooperative clustering approach for two types of well-known agglomerative hierarchical software clustering algorithms, for binary as well as non-binary features. Third, to evaluate our proposed CCT, we conduct modularization experiments on five software systems. Our analysis identifies certain cases that reveal weaknesses of the individual similarity measures. The experimental results support our hypothesis that these weaknesses may be overcome by using more than one measure, as our CCT produces better modularization results for test systems in which these cases occur. We conclude that CCTs are capable of showing significant improvement over individual clustering algorithms for software modularization.

A survey on bug prioritization

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, whether the bugs are duplicate or unique, important or unimportant, and who will resolve them. Assigning bug reports based on their priority or importance may play an important role in enhancing the bug triaging process. The accurate and timely prioritization and hence resolution of these bug reports not only improves the quality of software maintenance task but also provides the basis to keep particular software alive. In the past decade, various studies have been conducted to prioritize bug reports using data mining techniques like classification, information retrieval and clustering that can overcome incorrect prioritization. Due to their popularity and importance, we survey the automated bug prioritization processes in a systematic way. In particular, this paper gives a small theoretical study for bug reports to motivate the necessity for work on bug prioritization. The existing work on bug prioritization and some possible problems in working with bug prioritization are summarized.

Software Clustering Using Automated Feature Subset Selection

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, however, some of the extracted features may have less information to use for calculating the entities, which result in dropping the quality of software clusters. Therefore, further research is required to select those features which have high relevancy in finding associations between entities. In this article first we propose a supervised feature selection technique for unlabeled data, and then we apply this technique for software clustering. A number of feature subset selection techniques in software architecture recovery have been proposed. However none of them focus on automated feature selection in this domain. Experimental results on three software test systems reveal that our proposed approach produces results which are closer to the decompositions prepared by human experts, as compared to those discovered by the well-known K-Means algorithm.

Hybrid Guided Artificial Bee Colony Algorithm for Numerical Function Optimization

Many different earning algorithms used for getting high performance in mathematics and statistical tasks. Recently, an Artificial Bee Colony (ABC) developed by Karaboga is a nature inspired algorithm, which has been shown excellent performance with some standard algorithms. The hybridization and improvement strategy made ABC more attractive to researchers. The two famous improved algorithms are: Guided Artificial Bee Colony (GABC) and Gbest Guided Artificial Bee Colony (GGABC), are used the foraging behaviour of the gbest and guided honey bees for solving optimization tasks. In this paper, GABC and GGABC methods are hybrid and so-called Hybrid Guided Artificial Bee Colony (HGABC) algorithm for strong discovery and utilization processes. The experiment results tested with sets of numerical benchmark functions show that the proposed HGABC algorithm outperforms ABC, PSO, GABC and GGABC algorithms in most of the experiments.

Optimization of ANFIS Using Artificial Bee Colony Algorithm for Classification of Malaysian SMEs

Adaptive Neuro-Fuzzy Inference System (ANFIS) has been widely applied in industry as well as scientific problems. This is due to its ability to approximate every plant with proper number of rules. However, surge in auto-generated rules, as the inputs increase, adds up to complexity and computational cost of the network. Therefore, optimization is required by pruning the weak rules while, at the same time, achieving maximum accuracy. Moreover, it is important to note that over-reducing rules may result in loss of accuracy. Artificial Bee Colony (ABC) is widely applied swarm-based technique for searching optimum solutions as it uses few setting parameters. This research explores the applicability of ABC algorithm to ANFIS optimization. For the practical implementation, classification of Malaysian SMEs is performed. For validation, the performance of ABC is compared with one of the popular optimization techniques Particle Swarm Optimization (PSO) and recently developed Mine Blast Algorithm (MBA). The evaluation metrics include number of rules in the optimized rule-base, accuracy, and number of iterations to converge. Results indicate that ABC needs improvement in exploration strategy in order to avoid trap in local minima. However, the application of any efficient metaheuristic with the modified two-pass ANFIS learning algorithm will provide researchers with an approach to effectively optimize ANFIS when the number of inputs increase significantly.

Improved binary similarity measures for software modularization

Various binary similarity measures have been employed in clustering approaches to make homogeneous groups of similar entities in the data. These similarity measures are mostly based only on the presence or absence of features. Binary similarity measures have also been explored with different clustering approaches (e.g., agglomerative hierarchical clustering) for software modularization to make software systems understandable and manageable. Each similarity measure has its own strengths and weaknesses which improve and deteriorate the clustering results, respectively. We highlight the strengths of some well-known existing binary similarity measures for software modularization. Furthermore, based on these existing similarity measures, we introduce several improved new binary similarity measures. Proofs of the correctness with illustration and a series of experiments are presented to evaluate the effectiveness of our new binary similarity measures.

Program restructuring using agglomerative clustering technique based on binary features

Restructuring makes large programs highly cohesive and decomposes the statements into meaningful functions. These meaningful functions help the programmer to understand and maintain the code easily. In this paper, we apply agglomerative clustering technique to restructure the program using binary features. Our approach uses Jaccard similarity measure for binary features to assess the clustering process. We applied this approach to two non-cohesive structured programs available openly. We provide comparative evaluation in which clustering algorithms and similarity measures are used to validate our clustering results. Results indicate that this approach produces clustering that helps to translate a non-cohesive procedure into cohesive procedures.

Exploration and Exploitation Measurement in Swarm-Based Metaheuristic Algorithms: An Empirical Analysis

Swarm-based metaheuristics, inspired from intelligent social behaviors in nature, have achieved wider acceptance among researchers as compared to other population-based methods. The success of any swarm-based algorithm highly depends upon the mechanism of social interaction which maintains the balance between exploration and exploitation. This research examines these two significant cornerstones of top five swarm-based metaheuristics using diversity measurement. The results show that ACO and FA maintained balance between exploration and exploitation throughout iterations thus achieved better results as compared to counterparts taken in this study.

Euclidean space based hierarchical clusterers combinations: an application to software clustering

Hierarchical clustering groups similar entities on the basis of some similarity (or distance) association and results in a tree like structure, called dendrogram. Dendrograms represent clusters in a nested manner, where at each step an entity makes a new cluster or merges into an existing cluster. Hierarchical clustering has many applications, therefore researchers have made efforts to come up with improved hierarchical clustering approaches. An approach that has received attention is based on combining clustering results, since different hierarchical clustering algorithms produce different dendrograms and their combination has produced more promising results as compared to individual hierarchical clustering. This paper proposes the hierarchical clustering combination (HCC) approach which uses the different types of structural features present in the dendrogram. Firstly, the dendrograms are represented in a 4+N (4 is the extracted number of features and can be extended to N number) dimensional euclidean space (4+NDES) which results in vector matrices. 4+NDES is the structural representation of the dendrogram which contains not only the relative features but also the absolute features of the entities in the dendrogram. Then the vector matrices are aggregated and the distance is calculated between each two vector using the Euclidean distance measure. The final hierarchy is obtained using a recovery tool like individual hierarchical clustering. 4+NDES-HCC utilizes the structural contents of the dendrogram and has the flexibility to handle an increasing number of features. The proposed approach is tested for software clustering which plays an important role in maintenance of software systems. The experimental results of the proposed approach and comparative analysis with existing approaches reveal the effectiveness of the HCC for software clustering.