MoMS: Multi-objective Miniaturization of Software

1. Nasir AliNasir Ali, Wei Wu, Giuliano Antoniol, Max Di Penta,, Wei Wu, Giuliano Antoniol, Max Di Penta,
Yann-Gaël Guéhéneuc, Jane Huffman HayesYann-Gaël Guéhéneuc, Jane Huffman Hayes
MoMS: Multi-objective Miniaturization ofMoMS: Multi-objective Miniaturization of
SoftwareSoftware
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2. Mom but not MoMSMom but not MoMS
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3. Motivation
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4. Resources vs. Feature vs. Customers
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5. Our Goal
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6. Different Customers – Different Features
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7. Customer Relative Weight
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8. Pre-requirement Elicitation andPre-requirement Elicitation and
ConsolidationConsolidation
§ We need pre-requirement documents:We need pre-requirement documents:
§ What do we have?
§ What do our customers want?
§ What is the weight of each customer?
§ We obtain and vet a list of requirements fromWe obtain and vet a list of requirements from
diverse stakeholdersdiverse stakeholders
§ We structure requirements by mapping them intoWe structure requirements by mapping them into
a representation suitable for grouping viaa representation suitable for grouping via
pattern-recognition and similarity-basedpattern-recognition and similarity-based
clusteringclustering
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9. Static Traceability Map
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10. Features to SizeFeatures to Size
§ Traceability relations are tagged with:Traceability relations are tagged with:
§ Size information
§ IDs and weights of customers requiring the feature
§ Features are divided into:Features are divided into:
§ Compulsory
§ Cherry on the pie
§ Selected features must lead to a compilableSelected features must lead to a compilable
system:system:
§ Extra code may be needed just to make sure that the
system compiles and runs
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11. Features to CPU ConsumptionFeatures to CPU Consumption
§ Assumption: CPU cycles/consumption is related to energyAssumption: CPU cycles/consumption is related to energy
consumption:consumption:
§ The higher the CPU consumption, the lower the battery life
§ Binder’s JP2 profiling tool: comprehensive calling-contextBinder’s JP2 profiling tool: comprehensive calling-context
profiles:profiles:
§ Exact number of executed bytecodes for each calling context
§ Caveat: modern hardware architecture prevent exactCaveat: modern hardware architecture prevent exact
estimation based on bytecode countingestimation based on bytecode counting
§ But: bytecode counting is a good approximation of run time
algorithmic complexity.
§ The lower the number of executed bytecodes, the lower the CPU
time, the lower the battery consumption
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12. Requirements to Features
Scenario 1
(feature a)
Scenario N
(feature z)
Scenario 2
(feature b)
…
Run System
Trace 1
Trace 2
Trace N
…
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13. Dynamic Information*Dynamic Information*
§ Call tree:Call tree:
§ Integrate call tree information for each executed feature with
static traceability relations to count executed bytecodes
§ Evaluate CPU consumption at method level: accumulateEvaluate CPU consumption at method level: accumulate
into call-tree top nodes the counts of lower nodes:into call-tree top nodes the counts of lower nodes:
§ Top nodes thus stores sub-tree bytecode counts
§ Top nodes account for all executed bytecodes, including JARs
and utility methods
§ Caveat:Caveat:
§ Some feature may not be completely implemented
§ Some feature may not be executed due to missing
components
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14. Miniaturization ProblemMiniaturization Problem
§ We would like to:We would like to:
§ Minimize size and CPU consumption
§ Maximize customer satisfaction
§ Constraints may be imposed on search space:Constraints may be imposed on search space:
§ Max available memory, max CPU power, customers
who must be satisfied
§ Generate a Pareto surface:Generate a Pareto surface:
§ Project Pareto surface onto a Pareto front
§ Final decision to the managerFinal decision to the manager
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15. Miniaturization Problem (cont’d)*Miniaturization Problem (cont’d)*
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16. Miniaturization Problem (cont’d)*Miniaturization Problem (cont’d)*
§ Traceability creates aTraceability creates a
function Impl that, given afunction Impl that, given a
feature, assignsfeature, assigns
implementation unitsimplementation units
§ Each implementation unitEach implementation unit
has properties, e.g., eachhas properties, e.g., each
method have a size and amethod have a size and a
CPU consumptionCPU consumption
§ The CustomerThe Customer
Satisfaction Ratio (CSR)Satisfaction Ratio (CSR)
is defined as:is defined as:
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17. Miniaturization Problem (cont’d)*Miniaturization Problem (cont’d)*
§ Maximize CSR(F’)Maximize CSR(F’)
means minimizemeans minimize
–CSR(F’)–CSR(F’)
§ For a given set ofFor a given set of
features F’, thefeatures F’, the
implementation unitsimplementation units
and the overalland the overall
properties are:properties are: ( )
( )( )ComFFImplProp'
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18. Miniaturization Problem (cont’d)*
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Notice that ( )( )ComFFImplProp Ȣ
is actually an array of sizes and CPU consumptions.
Thus, a solution is a surface:
CSR = FUNC(size, CPU consumption)
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19. Case StudiesCase Studies
§ 350 questionnaires, 73 completed surveys350 questionnaires, 73 completed surveys
§ Pooka V2.0 e-mail client:Pooka V2.0 e-mail client:
§ 208 classes
§ 20,868 methods
§ 245 KLOCs
§ 599 (93) pre-requirements
§ 30 traced features
§ Code size 5.39 MB
§ SIP V1.0 audio/video Internet phone:SIP V1.0 audio/video Internet phone:
§ 1,771 classes
§ 31,302 methods
§ 486 KLOCs
§ 639 (82) pre-requirements
§ 36 traced features
§ Code size 27.3 MB

20. NSGA-II ParametersNSGA-II Parameters
§ We used JMETAL:We used JMETAL:
§ Mutation probability 4%
§ Crossover 90%
§ Evaluation number 25,000
§ High iteration number to ensure that we did notHigh iteration number to ensure that we did not
miss good solutionsmiss good solutions
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21. Pooka Projection CSR vs. Size
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22. SIP Projection CSR vs. Size
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23. Pooka Projection CSR vs. Size vs. CPU
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24. SIP Projection CSR vs. Size vs. CPUSIP Projection CSR vs. Size vs. CPU
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25. Lessons LearnedLessons Learned
§ The miniaturization process is feasible but thereThe miniaturization process is feasible but there
are challenges:are challenges:
§ Traceability recovery and accuracy of traced links
§ Collecting dynamic information is difficult:
§ Missing or not 100% implemented features
§ CPU consumption difficult to run:
§ We are still completing SIP
§ Some system (SIP) may exhibit tangledSome system (SIP) may exhibit tangled
dependencies and there may be no sweet spotdependencies and there may be no sweet spot
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26. ConclusionConclusion
§ The porting problem was modeled as aThe porting problem was modeled as a
multi-objective minimization problemmulti-objective minimization problem
§ Equations can accommodate a wide range ofEquations can accommodate a wide range of
propertiesproperties
§ The process can be automated thus savingThe process can be automated thus saving
considerable manual effort in selecting featuresconsiderable manual effort in selecting features
to be ported:to be ported:
§ Yet not in validating traceability links if links do not exist
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27. Questions
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