A biting-down approach to hierarchical decomposition of object-oriented systems based on structure analysis

Journal of Software Maintenance and Evolution

Volumn 22, Issue 8, 2010, Pages 567-596

  Zhang, Lu ^a   Luo, Jing ^a   Li, He ^a   Sun, Jiasu ^a   Mei, Hong ^a  

^a Peking University   (China)

Author keywords

architecture recovery; hierarchical system decomposition; software clustering

Indexed keywords

ARCHITECTURE RECOVERY; COMPLEX SOFTWARE SYSTEMS; EXPERIMENTAL STUDIES; HIERARCHICAL APPROACH; HIERARCHICAL DECOMPOSITIONS; HIERARCHICAL ORGANIZATIONS; HIERARCHICAL SYSTEM DECOMPOSITION; OBJECT-ORIENTED SYSTEM; REUSABLE COMPONENTS; SOFTWARE CLUSTERING; STRUCTURE ANALYSIS; SYSTEM DECOMPOSITION; TARGET SYSTEMS;

AGGLOMERATION; COMPUTER SOFTWARE REUSABILITY; MERGING;

HIERARCHICAL SYSTEMS;

EID: 78649809137     PISSN: 1532060X     EISSN: 15320618     Source Type: Journal    
DOI: 10.1002/smr.417     Document Type: Article

References (77)

1. Müller HA, Orgun M, Tilley S, Uhl JS,. A reverse engineering approach to subsystem structure identification. Journal of Software Maintenance: Research and Practice 1993; 5 (4): 181-204.
2. de Oca CM, Carver DL,. A visual representation model for software subsystem decomposition. Proceedings of Fifth Working Conference on Reverse Engineering, October 1998; 231-240.
3. Mancoridis S, Mitchell BS, Chen Y, Gansner ER,. Bunch: A clustering tool for the recovery and maintenance of software system structures. Proceedings of International Conference on Software Maintenance, August 1999; 50-62.
4. Tzerpos V, Holt RC,. Mojo: A distance metric for software clustering. Proceedings of the Working Conference on Reverse Engineering, October 1999; 187-195.
5. Tzerpos V, Holt RC,. ACDC: An algorithm for comprehension driven clustering. Proceedings of the Working Conference on Reverse Engineering, November 2000; 258-267.
6. Mahdavi K, Harman M, Hierons RM,. A multiple hill climbing approach to software module clustering. Proceedings of 19th International Conference on Software Maintenance, September 2003; 315-324.
7. Maqbool O, Babri HA,. The weighted combined algorithm: A linkage algorithm for software clustering. Proceedings of European Conference on Software Maintenance and Reengineering, March 2004; 15-24.
8. Lung CH, Zaman M,. Using clustering technique to restructure programs. Proceedings of the International Conference on Software Engineering Research and Practice, 2004; 853-858.
9. Lung CH, Zaman M, Nandi A,. Applications of clustering techniques to software partitioning, recovery and restructuring. Journal of Systems and Software 2004; 73 (2): 227-244.
10. Wen Z, Tzerpos V,. An effectiveness measure for software clustering algorithms. Proceedings of the 12th International Workshop on Program Comprehension, June 2004; 194-203.
11. Andritsos P, Tzerpos V,. Information-theoretic software clustering. IEEE Transactions on Software Engineering 2005; 31 (2): 150-165.
12. Shokoufandeh A, Mancoridis S, Denton T, Maycock M,. Spectral and meta-heuristic algorithms for software clustering. Journal of Systems and Software 2005; 77 (3): 213-223.
13. Zhao W, Zhang L, Mei H, Sun J,. Requirements guided dynamic software clustering. Proceedings of the 21st International Conference on Software Maintenance, September 2005; 605-608.
14. Lung CH, Xu X, Zaman M, Srinivasan A,. Program restructuring using clustering techniques. Journal of Systems and Software 2006; 79 (9): 1261-1279.
15. Christl A, Koschke R, Storey MA,. Automated clustering to support the reflexion method. Information and Software Technology 2007; 49 (3): 255-274.
16. Maqbool O, Babri HA,. Hierarchical clustering for software architecture recovery. IEEE Transactions on Software Engineering 2007; 33 (11): 759-780.
17. Abd-El-Hafiz SK, Basili VR, Caldiera G,. Towards automated support for extraction of reusable components. Proceedings of Ninth International Conference on Software Maintenance, October 1991; 212-219.
18. Dunn MF, Knight JC,. Automating the detection of reusable parts in existing software. Proceedings of 15th International Conference on Software Engineering, May 1993; 381-390.
19. Canfora G, Cimitile A, Munro M,. An improved algorithm for identifying objects in code. Software Practice and Experience 1996; 26 (1): 25-48.
20. Girard JF, Koschke R,. Finding components in a hierarchy of modules: A step towards architectural understanding. Proceedings of the International Conference on Software Maintenance, October 1997; 58-65.
21. Koschke R,. An incremental semi-automatic method for component recovery. Proceedings of the Working Conference on Reverse Engineering, October 1999; 256-267.
22. Spinellis D, Raptis K,. Component mining: A process and its pattern language. Information and Software Technology 2000; 42 (9): 609-617.
23. Sartipi K, Kontogiannis K,. Component clustering based on maximal association. Proceedings of Working Conference on Reverse Engineering, October 2001; 103-114.
24. Zhou X, Chen XK, Sun JS, Yang FQ,. Software measurement based reusable component extraction in object-oriented system. Acta Electronica Sinica 2003; 31 (5): 649-653 (in Chinese).
25. Sartipi K, Kontogiannis K,. A user-assisted approach to component clustering. Journal of Software Maintenance and Evolution: Research and Practice 2003; 15 (4): 265-295.
26. Chiricota Y, Jourdan F, Melancon G,. Software components capture using graph clustering. Proceedings 11th IEEE International Workshop on Program Comprehension, May 2003; 217-226.
27. Koschke R, Canfora G, Czeranskic J,. Revisiting the ΔIC approach to component recovery. Science of Computer Programming 2006; 60 (2): 171-188.
28. Doval D, Mancoridis S, Mitchell BS,. Automatic clustering of software systems using a genetic algorithm. Proceedings of Ninth International Workshop on Software Technology and Engineering Practice, August 1999; 73-81.
29. Schwanke R,. An intelligent tool for re-engineering software modularity. Proceedings 13th International Conference on Software Engineering, May 1991; 83-92.
30. Patel S, Chu W, Baxter R,. A measure for composite module cohesion. Proceedings of the International Conference on Software Engineering, May 1992; 38-48.
31. Ogando RM, Yau SS, Wilde N,. An object finder for program structure understanding in software maintenance. Journal of Software Maintenance: Research and Practice 1994; 6 (5): 261-283.
32. Yeh AS, Harris D, Reubenstein H,. Recovering abstract data types and object instances from a conventional procedural language. Proceedings of the Working Conference on Reverse Engineering, July 1995; 227-236.
33. Mancoridis S, Holt RC,. Recovering the structure of software systems using tube graph interconnection clustering. Proceedings of International Conference on Software Maintenance, November 1996; 23-32.
34. Cimitile A, Visaggio G,. Software salvaging and the call dominance tree. Journal of Systems Software 1995; 28 (2): 117-127.
35. Lindig C, Snelting G,. Assessing modular structure of legacy code based on mathematical concept analysis. Proceedings of International Conference on Software Engineering, May 1997; 349-359.
36. Mancoridis S, Mitchell BS, Rorres C, Chen Y, Gansner ER,. Using automatic clustering to produce high-level system organizations of source code. Proceedings of Sixth International Workshop on Program Comprehension, June 1998; 45-52.
37. Girard JF, Koschke R, Schied G,. A metric-based approach to detect abstract data types and abstract state encapsulation. Automated Software Engineering 1999; 6 (4): 357-386.
38. Anquetil N,. A comparison of graphs of concepts for reverse engineering. Proceedings of the International Workshop on Program Comprehension, June 2000; 231-240.
39. Girard JF, Koschke R,. A comparison of abstract data type and objects recovery techniques. Science of Computer Programming 2000; 36 (2-3): 149-181.
40. Mitchell BS, Mancoridis S,. Comparing the decompositions produced by software clustering algorithms using similarity measurements. Proceedings 19th IEEE International Conference on Software Maintenance, November 2001; 744-753.
41. Pinzger M, Gall H,. Pattern-supported architecture recovery. Proceedings of the 10th International Workshop on Program Comprehension, June 2002; 53-61.
42. Mitchell BS, Mancoridis S,. On the automatic modularization of software systems using the Bunch tool. IEEE Transactions on Software Engineering 2006; 32 (3): 193-208.
43. Etzkorn LH, Davis CG,. Automatically identifying reusable components in object oriented legacy code. IEEE Computer 1997; 30 (10): 66-71.
44. Anquetil N, Lethbridge T,. Extracting concepts from file names: A new file clustering criterion. Proceedings of the 20th International Conference on Software Engineering, April 1998; 84-93.
45. Sartipi K, Kontogiannis K, Mavaddat F,. Architectural design recovery using data mining techniques. Proceedings of the European Conference on Software Maintenance and Reengineering, February 2000; 129-140.
46. Hutchens D, Basili R,. System structure analysis: Clustering with data bindings. IEEE Transactions on Software Engineering 1985; 11 (8): 749-757.
47. Schwanke R, Hanson S,. Using neural networks to modularize software. Machine Learning 1994; 15 (2): 137-168.
48. Anquetil N, Fourrier C, Lethbridge T,. Experiments with hierarchical clustering algorithms as software remodularization methods. Proceedings of Sixth Working Conference on Reverse Engineering, October 1999; 235-255.
49. Luo J, Zhao W, Qin T, Jiang RK, Zhang L, Sun JS,. A decomposition method for object-oriented systems based on iterative analysis of the directed weighted graph. Journal of Software 2004; 15 (9): 1292-1300 (in Chinese).
50. Luo J, Jiang RK, Zhang L, Mei H, Sun JS,. An experimental study of two graph analysis based component capture methods for object-oriented systems. Proceedings 20th IEEE International Conference on Software Maintenance, September 2004; 390-398.
51. Müller HA, Uhl JS,. Composing subsystem structures using (k, 2)-partite graphs. Proceedings of Sixth International Conference on Software Maintenance, November 1990; 12-19.
52. Luo J, Zhang L, Sun JS,. A hierarchical decomposition method for object-oriented systems based on identifying omnipresent clusters. Proceedings of the 21st International Conference on Software Maintenance, September 2005; 647-650.
53. Valasareddi RR, Carver DL,. A graph-based object identification process for procedural programs. Proceedings of Fifth Working Conference on Reverse Engineering, October 1998; 50-58.
54. Koschke R, Eisenbarth T,. A framework for experimental evaluation of clustering techniques. Proceedings of the Eighth International Workshop on Program Comprehension, June 2000; 201-210.
55. Shtern M, Tzerpos V,. A framework for the comparison of nested software decompositions. Proceedings of 11th Working Conference on Reverse Engineering, October 2004; 284-292.
56. Wen Z, Tzerpos V,. An optimal algorithm for MoJo distance. Proceedings of the 11th International Workshop on Program Comprehension, May 2003; 227-235.
57. Lakhotia A,. A unified framework for expressing software subsystems classification techniques. Journal of Systems and Software 1997; 36 (3): 211-231.
58. Tzerpos V, Holt RC,. Software botryology: Automatic clustering of software systems. Proceedings of the International Workshop on Large-scale Software Composition, August 1998; 811-818.
59. Wiggerts T,. Using clustering algorithms in legacy systems remodularization. Proceedings of Working Conference on Reverse Engineering, October 1997; 33-43.
60. Koschke R,. Atomic architectural component detection for program understanding and system evolution. PhD Thesis, University of Stuttgart, Stuttgart, Germany, 2000. Available at: [9 October 2009 ].
61. Pollet D, Ducasse S, Poyet L, Alloui I, Cimpan S, Verjus H,. Towards a process-oriented software architecture reconstruction taxonomy. Proceedings of 11th European Conference on Software Maintenance and Reengineering, March 2007; 137-148.
62. Saeed M, Maqbool O, Babri HA, Sarwar SM, Hassan SZ,. Software clustering techniques and the use of the combined algorithm. Proceedings of the Seventh European Conference on Software Maintenance and Reengineering, March 2003; 301-306.
63. Sahraoui H, Melo W, Lounis H, Dumont F,. Applying concept formation methods to object identification in procedural code. Proceedings of the International Conference on Automated Software Engineering, November 1997; 210-218.
64. Siff M, Reps T,. Identifying modules via concept analysis. Proceedings of the International Conference on Software Maintenance, October 1997; 170-179.
65. Canfora G, Cimitile A, De Lucia A, Di Lucca GA,. A case study of applying an eclectic approach to identify objects in code. Proceedings of the Workshop on Program Comprehension, May 1999; 136-143.
66. van Deursen A, Kuipers T,. Identifying objects using cluster and concept analysis. Proceedings of International Conference on Software Engineering, May 1999; 246-255.
67. Tonella P,. Concept analysis for module restructuring. IEEE Transactions on Software Engineering 2001; 27 (4): 351-363.
68. Sartipi K, Kontogiannis K, Mavaddat F,. A pattern matching framework for software architecture recovery and restructuring. Proceedings of the International Workshop on Program Comprehension, June 2000; 37-47.
69. Sartipi K,. Alborz: A query-based tool for software architecture recovery. Proceedings of the International Workshop on Program Comprehension, May 2001; 115-116.
70. Sartipi K, Kontogiannis K,. A graph pattern matching approach to software architecture recovery. Proceedings of the International Conference on Software Maintenance, November 2001; 408-419.
71. Anquetil N, Lethbridge T,. Recovering software architecture from the names of source files. Journal of Software Maintenance 1999; 11 (3): 201-221.
72. Bojic D, Velasevic D,. A use-case driven method of architecture recovery for program understanding and reuse reengineering. Proceedings of European Conference on Software Maintenance and Reengineering, February 2000; 23-32.
73. Lakhotia A, Gravley JM,. Toward experimental evaluation of subsystem classification recovery techniques. Proceedings of the Second Working Conference on Reverse Engineering, July 1995; 262-269.
74. Wen Z, Tzerpos V,. Evaluating similarity measures for software decompositions. Proceedings of the International Conference on Software Maintenance, September 2004; 368-377.
75. Davey J, Burd E,. Evaluating the suitability of data clustering for software remodularization. Proceedings of Seventh Working Conference on Reverse Engineering, November 2000; 268-277.
76. Wu J, Hassan AE, Holt RC,. Comparison of clustering algorithms in the context of software evolution. Proceedings of International Conference on Software Maintenance, September 2005; 525-535.
77. Shokoufandeh A, Mancoridis S, Maycock M,. Applying spectral methods to software clustering. Proceedings of Ninth Working Conference on Reverse Engineering, October 2002; 3-10.