Role of Structural and Semantic Relations in Determining Coupling among Software Elements

doi:10.23940/ijpe.19.05.p3.12791288

Abstract

Abstract: Software maintenance, as a long-term, cost-intensive activity and an unmanaged structure of a software system, further increases maintenance efforts and thereby development costs. A well-structured software system must achieve an optimal balance between cohesion and coupling among different software elements. Therefore, in this paper, a coupling measurement technique is proposed. This technique helps measure the conceptual similarity between different elements (classes) of a software system. It utilizes two kinds of relations, structural and semantic, in order to obtain more accurate coupling measures. In particular, our proposed approach mainly extracts lexical information from four portions of the underlying source code of a software element. The four main parts are comments, Javadoc, signatures, and member variable zone. Similarly, the structural coupling is measured by counting calls to member functions of other classes generated from a given class. The proposed approach is first tested on a student record management system software and finally applied to three standard open source Java software systems. While applying on standard open source software systems, three coupling measurement schemes are designed (including the proposed one): structural, semantic, and structural+semantic (proposed). Finally, the results are presented. The results obtained are very promising and reflect the actual coupling present between classes.

Key words: class dependence, coupling, lexical, restructuring, reverse engineering, semantic, structural

Randeep Singh and Ashok Kumar. Role of Structural and Semantic Relations in Determining Coupling among Software Elements [J]. Int J Performability Eng, 2019, 15(5): 1279-1288.

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References

[1] G. Antoniol, M. Di Penta, G. Casazza,E. Merlo, “A Method to Re-Organize Legacy Systems via Concept Analysis,” inProceedings of 9th International Workshop on Program Comprehension, pp. 281-292, Toronto, Canada, 2001
[2] A. Cimitile and G. Visaggio, “Software Salvaging and the Call Dominance Tree,” Journal of Systems and Software, Vol. 28, No. 2, pp. 117-127, 1995
[3] S. C. Shaw, M. Goldstein, M. Munro,E. Burd, “Moral Dominance Relations for Program Comprehension,” IEEE Transactions on Software Engineering, Vol. 29, No. 9, pp. 851-863, 2003
[4] M. Abdellatief, A. B. M.Sultan, A. Ghani, and M. A. Jabar, “Component-based Software System Dependency Metrics based on Component Information Flow Measurements,” in Proceedings of the Sixth International Conference on Software Engineering Advances, IARIA, 2011
[5] D. H. Qiu, H. Li,J. L. Sun, “Measuring Software Similarity based on Structure and Property of Class diagram,” in Proceedings of the Sixth International Conference on Advanced Computational Intelligence (ICACI), IEEE, 2013
[6] M. Savic, G. Rakic, Z. Budimac,M. Ivanovic, “A Language-Independent Approach to the Extraction of Dependencies Between Source Code Entities,”IST, Vol. 56, pp. 1268-1288, 2014
[7] P. Tonella, “Concept Analysis for Module Restructuring,” IEEE Transaction Software Engineering, Vol. 27, No. 4, pp. 351-363, 2001
[8] A. Van Deursen and T. Kuipers, “Identifying Objects using Cluster and Concept Analysis,” inProceedings of 21st International Conference on Software Engineering, pp. 246-255, Los Angeles, California, USA, 1999
[9] S. Mancoridis, B. S. Mitchell, C. Rorres, Y. -F. Chen, and E. R. Gansner, “Using Automatic Clustering to Produce High-Level System Organizations of Source Code,” in Proceedings of Sixth International Workshop on Program Comprehension, Ischia, Italy, IEEE CS Press, 1998
[10] B. S.Mitchell and S. Mancoridis, “On the Automatic Modularization of Software Systems using the Bunch Tool,” IEEE Transactions on Software Engineering, Vol. 32, No. 3, pp. 193-208, 2006
[11] M. Harman, R. M. Hierons,M. Proctor, “A New Representation and Crossover Operator for Search-based Optimization of Software Modularization,” in Proceedings of the Genetic and Evolutionary Computation Conference, New York, USA, 2002
[12] O. Seng, M. Bauer, M. Biehl,G. Pache, “Search-based Improvement of Subsystem Decompositions,” inProceedings of the Genetic and Evolutionary Computation Conference, pp. 1045-1051, Washington, Columbia, USA, 2005
[13] H. Abdeen, S. Ducasse, H. A. Sahraoui,I. Alloui, “Automatic Package Coupling and Cycle Minimization,” inProceedings of the 16th Working Conference on Reverse Engineering, pp. 103-112, Lille, France, 2009
[14] C. Srinivas, V. Radhakrishna,C. V. G.Rao, “Software Component Clustering and Classification using Novel Similarity Measure,”Procedia Technology, Vol. 19, pp. 866-873, 2015
[15] A. Marcus, D. Poshyvanyk,R. Ferenc, “Using The Conceptual Cohesion of Classes for Fault Prediction in Object-Oriented Systems,” IEEE Transaction on Software Engineering, Vol. 34, No. 2, pp. 287-300, 2008
[16] H. Kagdi, M. Gethers,D. Poshyvanyk, “Integrating Conceptual and Logical Couplings for Change Impact Analysis in Software,”Empirical Software Engineering, Vol. 18, pp. 933-969, 2013
[17] G. Bavota, R. Oliveto, M. Gethers, D. Poshyvanyk,A. De Lucia, “Method Book: Recommending Move Method Refactoring's Via Relational Topic Models,” IEEE Transactions on Software Engineering, Vol. 40, No. 7, pp. 671-694, 2014
[18] G. Bavota, A. De Lucia, A. Marcus,R. Oliveto, “Software Re-Modularization based on Structural and Semantic Metrics,” inProceedings of the International Working Conference on Reverse Engineering, pp. 195-204, IEEE Computer Society, 2010
[19] A. B. Belle, G. E. Boussaidi,S. Kpodjedo, “Combining Lexical and Structural Information to Reconstruct Software Layers,” Information and Software Technology, Vol. 74, pp. 1-16, 2016
[20] A. Corazza, S. Di Martino,G. Scanniello, “A Probabilistic based Approach Towards Software System Clustering,” inProceedings of European Conference on Software Maintenance and Reengineering, pp. 89-98, IEEE Computer Society, 2010
[21] A. Corazza, S. Di Martino, V. Maggio,G. Scanniello, “Investigating the Use of Lexical Information for Software System Clustering,” inProceedings of European Conference on Software Maintenance and Reengineering, pp. 35-44, IEEE Computer Society, 2011
[22] A. Kuhn, S. Ducasse,T. Girba, “Semantic Clustering: Identifying Topics in SourceCode,” Information and Software Technology, Vol. 49, No. 3, pp. 230-243, 2007
[23] J. I.Maletic and A. Marcus, “Supporting Program Comprehension using Semantic and Structural Information,” inProceedings of 23rd International Conference on Software Engineering, pp. 103-112, Toronto, Ontario, Canada, 2001
[24] G. Scanniello, M. Risi,G. Tortora, “Architecture Recovery using Latent Semantic Indexing and K-Means: An Empirical Evaluation,” inProceedings of International Conference on Software Engineering and Formal Methods, pp. 103-112, 2010
[25] P. Andritsos and V. Tzerpos, “Information-Theoretic Software Clustering,” IEEE Transactions on Software Engineering, Vol. 31, No. 2, pp. 150-165, 2005
[26] Y. -S. Lee, B. -S. Liang, S. -F. Wu, and F. -J. Wang, “Measuring the Coupling and Cohesion of an Object-Oriented Program based on Information Flow,” in Proceedings of International Conference on Software Quality, Maribor, Slovenia, 1995