Understanding Business System through Software Clustering Using I/O Instructions for External Systems

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1 1,a) 1,b) 2,c) 2,d) 1,e) 1 2 Java Understanding Business System through Software Clustering Using I/O Instructions for External Systems Abstract: Business systems written decades ago play a key role in modern society. The maintenance of these systems requires a huge effort because they are typically large-scale and complicated. System modernization is important to facilitate the maintenance task of a legacy system. However, understanding a system for its modernization is a difficult task; one of the main reasons is that identifier names in the legacy code do not represent their roles. This paper proposes a software clustering technique which groups functions in source code by similarity of their actions without using identifier names. The technique can provide a benefit: if a developer understands a function included in a cluster, the developer can easily understand other functions included in the same cluster. This technique extracts a sequence of input/output instructions for a database and a user interface from each function to detect similar functions. An experiment for two Java systems showed that the clustering results by the technique resemble those by a developer Graduate School of Information Science and Technology, Osaka University 2 NTT NTT DATA Corporation a) t-hatano@ist.osaka-u.ac.jp b) ishio@ist.osaka-u.ac.jp c) moroys@nttdata.co.jp d) sakatayu@nttdata.co.jp e) inoue@ist.osaka-u.ac.jp [1] [2] c 2015 Information Processing Society of Japan 17

2 (1) Java (2) FUNC0001 FUNC0002 [3] [4] [5] [6] Kobayashi [7] Kobayashi [7] Java Java [6], [7] Mitchell [8] Mancoridis [9] [10] c 2015 Information Processing Society of Japan 18

3 [11] [12] Kobayashi [12] [7] Anquetil [13] Andritsos [14] Anquetil [15] Scanniello [16] Tzerpos [17] C filea.c filea.h [18] [18] McMillan Java [19] API 3. 1 Java COBOL SQL c 2015 Information Processing Society of Japan 19

4 Java JDBC API API 3 Step 1 Step 2 N-gram Step 3 Step Step 1 1 s m SELECT java.util.list 1 SQL SELECT Ss Sm INSERT Is Im UPDATE Us Um DELETE Ds Dm Rs Rm Ws Wm if If if If} else E for, while, do L for, while, do L} 2 1 SELECT OrderDao.select DELETE CustormerDao.delete Vo.getSelectedCustomers Cesare [20] Java development tools *1 Class Hierarchy Analysis [21] [Rm, L, Sm, If, Ds, If}, L}] *1 c 2015 Information Processing Society of Japan 20

5 void batchdeletecustomer(customerdao cstdao, OrderDao orddao, Vo vo) { // ID String[] idarray = vo.getselectedcustomers(); for (String id: idarray) { // List<OrderDto> ordlist = orddao.select(id); if (ordlist.isempty()) { cstdao.delete(id); // } } } Step 2 Step 1 N-gram N-gram N-gram N-gram N N N =3 tri-gram ABCDE tri-gram {$$A, $AB, ABC, BCD, CDE, DE$, E$$} (1) $ N-gram 2 X, Y Jaccard(X, Y) = X Y X Y (2) X Y 1 X Y 0 N-gram 3.3 Step 3 Step 2 Newman [22] [12], [23] V O( V log 2 V ) [24] Newman Q Q= 1 [ A ij k ] ik j δ(c i,c j ) (3) W W i,j A ij i j W W = i,j A ij k i i k i = j A ij c x x δ(c i,c j ) c i = c j 1 0 Q Q Q NP [25] Newman Q (1) 1 (2) Q 2 (3) 1 2 (4) Q Step 2 Newman Newman 0 2 m i m j k ij N-gram M i M j { Jaccard(M i,m j ) if (Jaccard(M i,m j ) th) k ij = 0 otherwise (4) th c 2015 Information Processing Society of Japan 21

6 4. 2 Java Step 2 N-gram N Step 3 th N=1 th=1.0 N-gram N N N th N th Newman [26] MoJoFM [27] MoJoFM A B 2 Move 1 Join 2 1 MoJoFM A B mno(a, B) MoJoFM(A, B) =(1 ) 100% (5) max(mno( A, B)) mno(a, B) A B max(mno( A, B)) B MoJoFM A B MoJoFM 100% A B MoJoFM MoJoFM(A, B) MoJoFM(B, A) A B Non- Extremity of cluster Distribution NED NED NED(C) = 1 c (6) M c C;5 c max(20,m/5) c C c c M C M [12] NED A, B MoJoSim [28] MoJoSim MoJoFM Move c 2015 Information Processing Society of Japan 22

7 3 MosP Join A B B A MoJoSim(A, B) =(1 OP ) 100% (7) N OP = min(mno(a, B),mno(B,A)) (8) 2 MoJoSim 2 MoJoSim MoJoSim 4.2 MosP V4 *2 3 3 MosP JDBC ibatis MosP Action action sql CREATE TABLE TABLE - MosP MosP Dao Vo *2 4 MosP SELECT findfor INSERT insert Dao UPDATE update DELETE delete logicaldelete get Vo set MosP MosP 5 MoJoFM MoJoFM NED MoJoSim MoJoSim MoJoFM 5 MoJoSim NED N=5 th= MoJoFM N 45 55% MoJoFM NED MoJoFM NED N=1 th=1.0 MoJoFM NED N=3 N=5 N=1 th= c 2015 Information Processing Society of Japan 23

8 5 MosP N=1 N=3 N=5 th MoJoFM NED MoJoSim MoJoFM NED MoJoSim MoJoFM NED MoJoSim MoJoFM MoJoFM N, th MoJoFM NED 5, (50.76) 1 (0.94) , (55.35) 5 (0.59) , (48.62) 2 (0.74) , (46.48) 3 (0.73) , (45.57) 4 (0.70) , (46.18) 7 (0.51) , (44.95) 6 (0.54) , (49.85) 15 (0.23) , (50.46) 16.5 (0.21) , (44.04) 9 (0.42) N=3 N=5 th N=1 NED MoJoFM N=5 th= search 13 batchupdate 8 delete 2 N= MoJoFM Frequency size of cluster 2 N=5 th=0.1 N 3 5 NED MoJoFM 77.37% 300 MoJoFM 15% [27] N th MoJoFM NED NED 5 MoJoFM NED 0.62 NED NED c 2015 Information Processing Society of Japan 24

9 7 N, th MoJoFM NED 5, , , N=3 N=5 th N=3 th= update1 search1 3 SQL batch1 batch2 batch1 INSERT batch2 UPDATE SQL SQL INSERT 2 0 update3 batch1 regist1 update2 batch2 update1 search1 search2 search Java 3 Java 5. Java c 2015 Information Processing Society of Japan 25

10 API READ/WRITE 1 10 Step 1 Step 2 N-gram Step 3 Newman N-gram Newman 40 [24] 6. 2 Java NED MoJoFM 60% 77% No , [1] JUAS 2012 (2012). [2] Ducasse, S. and Pollet, D.: Software Architecture Reconstruction: A Process-Oriented Taxonomy, IEEE Transactions on Software Engineering, Vol. 35, No. 4, pp (2009). [3] Sneed, H. M.: Object-oriented COBOL recycling, Proceedings of the 3rd Working Conference on Reverse Engineering, pp (1996). [4] McConnel, S.: Code Complete, Second Edition, Microsoft Press (2004). [5] Koschke, R. and Simon, D.: Hierarchical Reflexion Models, Proceedings of the 10th Working Conference on Reverse Engineering, pp (2003). [6] Maqbool, O. and Babri, H.: Hierarchical Clustering for Software Architecture Recovery, IEEE Transactions on Software Engineering, Vol. 33, No. 11, pp (2007). [7] Kobayashi, K., Kamimura, M., Yano, K., Kato, K. and Matsuo, A.: SArF map: Visualizing software architecture from feature and layer viewpoints, Proceedings of the 21st International Conference on Program Comprehension, pp (2013). [8] Mitchell, B. S. and Mancoridis, S.: On the automatic modularization of software systems using the bunch tool, IEEE Transactions on Software Engineering, Vol. 32, No. 3, pp (2006). [9] Mancoridis, S. and Mitchell, B.: Bunch: A clustering tool for the recovery and maintenance of software system structures, Proceedings of the 7th International Conference on Software Maintenance, pp (1999). [10] (2007). [11] Müller, H. A., Orgun, M. A., Tilley, S. R. and Uhl, J. S.: A Reverse Engineering Approach To Subsystem Structure Identification, Journal of Software Maintenance: Research and Practice, Vol. 5, No. 4, pp (1993). [12] Kobayashi, K., Kamimura, M., Kato, K., Yano, K. and Matsuo, A.: Feature-gathering dependency-based soft- c 2015 Information Processing Society of Japan 26

11 ware clustering using Dedication and Modularity, Proceedings of the 28th International Conference on Software Maintenance, pp (2012). [13] Anquetil, N. and Lethbridge, T.: Comparative Study of Clustering Algorithms and Abstract Representations for Software Remodularization, IEE Proceedings-Software, Vol. 150, No. 3, pp (2003). [14] Andritsos, P. and Tzerpos, V.: Information-theoretic software clustering, IEEE Transactions on Software Engineering, Vol. 31, No. 2, pp (2005). [15] Anquetil, N. and Lethbridge, T.: Recovering software architecture from the names of source files, Journal of Software Maintenance, Vol. 11, No. 3, pp (1999). [16] Scanniello, G., D Amico, A., D Amico, C. and D Amico, T.: Using the Kleinberg Algorithm and Vector Space Model for Software System Clustering, Proceedings of the 18th International Conference on Program Comprehension, pp (2010). [17] Tzerpos, V. and Holt, R.: ACDC: An algorithm for comprehension-driven clustering, Proceedings of the 7th Working Conference on Reverse Engineering, pp (2000). [18] Kamiya, T., Kusumoto, S. and Inoue, K.: CCFinder : A Multi-linguistic Token-Based Code Clone Detection System for Large Scale Source Code, IEEE Transactions on Software Engineering, Vol. 28, No. 7, pp (2002). [19] McMillan, C., Grechanik, M. and Poshyvanyk, D.: Detecting similar software applications, Proceedings of the 34th International Conference on Software Engineering, pp (2012). [20] Cesare, S. and Xiang, Y.: Malware Variant Detection Using Similarity Search over Sets of Control Flow Graphs, Proceedings of the 10th International Conference on Trust, Security and Privacy in Computing and Communications, pp (2011). [21] Dean, J., Grove, D. and Chambers, C.: Optimization of Object-Oriented Programs Using Static Class Hierarchy Analysis, Proceedings of the 9th European Conference on Object-Oriented Programming, pp (1995). [22] Newman, M. E. J.: Fast algorithm for detecting community structure in networks, Physical Review E, Vol. 69, No. 6, pp. 1 5 (2004). [23] Ural, E., Umut, T. and Feza, B.: Object Oriented Software Clustering Based on Community Structure, Proceedings of the 18th Asia-Pacific Software Engineering Conference, pp (2011). [24] Clauset, A., Newman, M. E. J. and Moore, C.: Finding community structure in very large networks, Physical Review E, Vol. 70, No. 6 (2004). [25] Brandes, U., Delling, D., Gaertler, M., Robert, G., Hoefer, M., Nikoloski, Z. and Wagner, D.: On Modularity Clustering, IEEE Transactions on Knowledge and Data Engineering, Vol. 20, No. 2, pp (2008). [26] Wu, J., Hassan, A. and Holt, R.: Comparison of clustering algorithms in the context of software evolution, Proceedings of the 21st International Conference on Software Maintenance, pp (2005). [27] Wen, Z. and Tzerpos, V.: An effectiveness measure for software clustering algorithms, Proceedings of 12th International Workshop on Program Comprehension, pp (2004). [28] Tzerpos, V. and Holt, R.: MoJo: a distance metric for software clusterings, Proceedings of the 6th Working Conference on Reverse Engineering, pp (1999). c 2015 Information Processing Society of Japan 27

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