cohesion and coupling optimisationcrest.cs.ucl.ac.uk/cow/49/slides/cow49_paixao.pdf · semantic and...
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Cohesion and Coupling OptimisationBALANCING IMPROVEMENT AND DISRUPTION
MATHEUS PAIXAO, MARK HARMAN, YUANYUAN ZHANG, YIJUN YU
What I have (not) done
The ultimate solution for software modularisation
Full understanding of developers behaviour
Identification of the best metrics for software modularisation
What I have done
Thorough empirical study of structural cohesion and coupling optimisation
Identification of disruption as an important overlooked issue
Multi-objective approach to balance structural improvement and disruption
Structural Dependencies
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Function Call
Data Access
Inheritance
Interface Implementation
Modularisation drivers
Semantic and Structural cohesion/coupling metrics better describe developers’ implementations [1][2]
[2] Candela, I., Bavota, G., Russo, B., & Oliveto, R. (2016). Using Cohesion and Coupling for Software Remodularization : Is It Enough ? ACM Transactions on Software Engineering and Methodology, 25(3), 1–28.
[1] Bavota, G., Dit, B., Oliveto, R., Di Penta, M., Poshyvanyk, D., & De Lucia, A. (2013). An empirical study on the developers’ perception of software coupling. In 2013 35th International Conference on Software Engineering (ICSE) (pp. 692–701). San Francisco: IEEE.
Literature Review
Metrics Validation
LongitudinalEvaluation
DisruptionAnalysis
largest empirical study of automated software
re-modularisation to date
Modularisation Quality - MQ
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𝑀𝐹(𝑝1) = 0.72
𝑀𝐹(𝑝2) = 0.66
𝑀𝐹(𝑝3) = 0.00
𝑀𝑄 = 1.38
RQ1: Is there any evidence that open source software systems respect structural measurements of cohesion and coupling?
RQ1.1: Purely Random Distribution RQ1.2: k-Random Neighbourhood Search
RQ1.3: Systematic 1-Neighbourhood Search
Cohesion
99.99%
0.1001%
MQ
99.99%
0.0866%
RQ2: What is the relationship between raw cohesion and the MQ metric?
-100.00%
-50.00%
0.00%
50.00%
100.00%
150.00%
200.00%
250.00%
300.00%
350.00%
400.00%
Bunch Solutions
MQ Difference Cohesion Difference
Bunch Search Solutions
Pivot 2.0.2
13
Packages
58
All Systems
493.11%
RQ2: What is the relationship between raw cohesion and the MQ metric?
Package-constrained Search Solutions
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
Package-constrained Solutions
MQ Difference Cohesion Difference
DisMoJo
Disruption metric based on MoJoFM[2]
[3] Zhihua Wen, & Tzerpos, V. (2004). An effectiveness measure for software clustering algorithms. In Proceedings. 12th IEEE International Workshop on Program Comprehension, 2004. (pp. 194–203).
Disruption Analysis Workflow
Releases
Re-modularisationapproach
ImprovedModularisations
DisMoJo
DisruptionValues
BunchPackage-constrained
30 executions
RQ3: What is the disruption caused by search based approaches for optimising software modularisation?
Bunch Package-constrained
Mean
80.39% 57.82%
Release 1.2
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Longitudinal Disruption Analysis
Release 1.1
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RQ5: What is the modularity improvement provided by the multiobjective search for acceptable disruption levels?
Package-free
Lower Bound
1.66% in MQ
0.13% in Cohesion
Upper Bound
150.38% in MQ
2.38% in Cohesion
Package-constrained
Lower Bound
3.36% in MQ
0.72% in Cohesion
Upper Bound
59.25% in MQ
23.49% in Cohesion
Conclusion
Software systems respect modularity measurements
Search based approaches for re-modularisation cause large disruption
Multiobjective search can be used to find clear and constant trade-off between modularity improvement and disruption
Modularity can be improved within lower and upper bounds of acceptable disruption performed by developers
Backup – System’s Selection Criteria
At least 10 subsequent official releases
No general libraries and APIs
Java systems
it’s an ordinal metric[1]
value has no meaning
it’s not normalised
Backup - Modularisation Metrics
avoids god packages
MQ
[4] Stanley Stevens. (1946). On the Theory of Scales of Measurement. American Association for the Advancement of Science, 103(2684), 677–680.
it’s an interval metric[1]
easy to understand
it’s normalised
leads to god packages
Cohesion
inflation effect
Backup – Classes distribution
RQ2.3: Package-constrained Search Solutions
30.06% cohesion improvement
34.15%
Biggest package
Smallest package
Original Implementations
0.32%
Biggest package
Smallest package
Package-constrained solutions
1.33%
19.80%
Backup - RQ3: Best MQ and best cohesion disruption
Bunch Package-constrained
Mean
80.39% 57.82%
Best MQ
79.67% 55.30%
Best Cohesion
78.77% 54.33%
Backup – RQ4 Two-Archive GA parameters
Population Size: number of classes (N)
Single point crossover (0.8 if N < 100; 1.0 otherwise)
Swap mutation (0.004 x log2N)
Tournament selection (size 2)
50N Generations
