Modular design and algorithmic complexity optimization for large-scale software systems

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Figure 7.

Figure 8.

Semantic variables based on triangular fuzzy numbers

Semantic variable	Triangular ambiguity
	l	m	μ
Very weak influence	0	0	0.1
Weak influence	0	0.1	0.3
General influence	0.3	0.5	0.7
Strong influence	0.7	0.9	1.0
Very strong influence	0.9	1.0	1.0

User demand

Demand number	Specific demand	Demand number	Specific demand
SR1	Strong cleaning ability	SR11	Voice control
SR2	Monitoring failure information	SR12	Time booking
SR3	Clean speed	SR13	Small running sound
SR4	Anti-fall collision	SR14	Lightness
SR5	Drag integration	SR15	Simple operation
SR6	Mobile control	SR16	Dump dust box
SR7	Automatic recharge	SR17	Automatic cleaning mop
SR8	Standby time	SR18	Automatic add cleaner
SR9	Fault prediction	SR19	Large dust box
SR10	Price appropriate	SR20	Maintenance convenience

Relationship between User Requirements and Product Quality

Demand node	Weight	FR1	FR2	FR3	FR4	FR5
SR1	0.0658	7	3	5	9	5
SR2	0.0628	9	7	7	1	9
SR3	0.0637	5	5	3	3	3
SR4	0.0661	3	3	1	3	1
SR5	0.0537	0	0	0	5	0
SR6	0.0577	1	1	9	0	3
SR7	0.0550	3	0	0	2	7
SR8	0.0555	5	0	1	3	3
SR9	0.0499	3	5	1	5	1
SR10	0.0531	1	0	3	7	0
SR11	0.0550	5	3	5	3	0
SR12	0.0534	7	1	7	5	3
SR13	0.0476	3	7	7	3	5
SR14	0.0419	0	0	5	7	0
SR15	0.0396	0	5	3	9	0
SR16	0.0448	1	3	9	3	0
SR17	0.0439	0	1	3	1	3
SR18	0.0400	0	3	1	0	1
SR19	0.0457	3	0	0	3	5
SR20	0.0048	5	3	3	5	7

User demand triangular fuzzy number comprehensive evaluation value

Demand number	SR1	SR2	SR3	SR4	⋯	SR17	SR18	SR19	SR20
SR1	1	(0,0.03,0.12)	(0.78,0.93,1)	(0.32,0.58,0.74)	⋯	0	0	0	0
SR2		1	(0,0.08,0.17)	(0,0.04,0.19)	⋯	0	0	(0.9,1.0,1.0)	(0.28,0.47,0.63)
SR3			1	(0.16,0.34,0.58)	⋯	(0,0.09,0.26)	0	(0,0,0.07)	0
SR4				1	⋯	(0.6,0.81,0.93)	(0,0.15,0.33)	0	0
⋯					1	⋯	⋯	⋯	⋯
SR18					⋯	1	(0,0.05,0.17)	0	0
SR19					⋯		1	0	0
SR20					⋯			1	(0.54,0.71,0.82)
SR20					⋯				1

Experimental data

Case number	Software system name	Class number	Method number	Number of calls	Code number
1	Merge	103	138	113	12983
2	Carch	192	291	462	13482
3	Yahoo	231	584	593	15671
4	Bitcoin	576	4083	2194	19834
5	Emule	588	8326	3586	20976
6	Svn	784	9248	5735	25847
7	V8	932	11823	10842	39594

Product function importance

Importance	FR1	FR2	FR3	FR4	FR5
Absolute importance	3.9543	3.1687	3.4832	2.5738	1.7064
Relative importance	0.2386	0.2438	0.2573	0.1354	0.1249

User demand relationship comprehensive evaluation value

Demand number	SR1	SR2	SR3	SR4	⋯	SR17	SR18	SR19	SR20
SR1	1	0.09	0.93	0.58	⋯	0	0	0	0
SR2		1	0.02	0.04	⋯	0	0	0.99	0.47
SR3			1	0.37	⋯	0.06	0	0.05	0
SR4				1	⋯	0.79	0.17	0	0
⋯					1	⋯	⋯	⋯	⋯
SR18						1	0.08	0	0
SR19							1	0	0
SR20								1	0.73
SR20									1

Users Require Network Node Eigenvalues

Demand node	Degrees	Network sparseness	Average path length	Network diameter	Concentration coefficient	Weight
SR1	12	3.51	39.08	0.80	0.55	0.0658
SR2	10	5.21	45.65	0.91	0.50	0.0628
SR3	15	5.91	50.16	0.81	0.52	0.0637
SR4	12	5.24	45.98	0.86	0.54	0.0661
SR5	16	3.83	49.13	0.71	0.43	0.0537
SR6	10	4.17	22.49	0.83	0.50	0.0577
SR7	8	3.13	36.80	0.83	0.40	0.0550
SR8	9	3.62	43.00	0.75	0.48	0.0555
SR9	9	4.93	39.93	0.58	0.37	0.0499
SR10	17	7.08	53.58	0.61	0.40	0.0531
SR11	9	3.31	35.27	0.66	0.46	0.0550
SR12	9	2.86	38.62	0.64	0.40	0.0534
SR13	8	1.30	35.54	0.79	0.41	0.0476
SR14	6	1.59	25.22	0.68	0.37	0.0419
SR15	5	2.01	22.94	0.49	0.33	0.0396
SR16	5	2.21	22.80	0.69	0.39	0.0448
SR17	4	1.47	16.64	0.69	0.37	0.0439
SR18	3	1.31	15.05	0.61	0.37	0.0400
SR19	5	1.72	12.73	0.61	0.31	0.0457
SR20	8	2.67	29.35	0.61	0.34	0.0048

Module division experimental results

Case number	Software system name	Vertex number	Population number	Partition module
1	Merge	13	15	5
2	Carch	19	12	4
3	Yahoo	31	29	7
4	Bitcoin	138	108	9
5	Emule	283	236	13
6	Svn	302	197	19
7	V8	734	494	134