1. Presentation on the article: Identifying effective software metrics using genetic algorithms
Presenter: Randy Hunt
Presenter: Vitaliy Krestnikov
Date: April 27, 2009
course: comp 589
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2. Introduction
Team Leaders commonly use software metrics as a measure of the overall quality of the design and the eventual implementation of systems.
The ability to predict the quality of a software object from a set of software metrics is in essence a problem of classification.
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3. Classification
Take a set of objects with known features (software metrics)
Combine them with group labels (quality rankings)
And you get a classifier that can predict the quality of new objects using only the computed metrics
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4. Software Metrics
Software metrics are used to quantitatively map a set of numerical values, such as the number of lines of code in a file or the number of methods in a class, to a subjective measure of quality, in terms of the apparent complexity, maintainability and usability.
Not all metrics provide the same classification power though, but different combination can yield results that certain people are looking for.
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5. Proposition
This article proposes using a genetic algorithm feature selection procedure to indicate the optimal metrics used in the classification process.
To test this proposal, software produced by Evident was used.
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6. Software Metrics
All 338 software objects in EvIdent were subjectively labeled by an experienced software architect in terms of maintainability.
Ranked each Java class as low, medium-low, medium or high.
High represents easy to modify.
Low represents difficult to modify.
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7. Software Metrics There were 16 different software metrics used.
LOC, SLC, CLC, WLC, RCC, RCS, SMC, MET, ANL, CAN, AE, ALC, ASC, ASL, ACC. AEC
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8. The Genetic Algorithm
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9. Genetic Algorithm step 1: initialize population
Population of Genes:
Each chromosome is a software metric
Chromo-somes:
gene #1
gene #2
gene #3
gene #4
gene #5 T Y P L O C S W R
SMC ME
ANL
CAN
AIE … 1
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10. 2. Begin the algorithm* for creating offspring for generation N, starting with generation 1
* The algorithm is shown on the following slides
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11. 3. Calculate fitness by LDA* 4. Select pair based on fitness
* LDA is explained later in this presentation
Chromo-somes:
gene #1
gene #2
gene #3
gene #4
gene #5 T Y P L O C S W R
SMC ME
ANL
CAN
AIE … 1
Fitness
(LDA %) 44 63 37 67 50
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12. 5. Produce child gene by swapping bits starting from the randomly-picked crossover point
Chromo-somes:
gene #2
gene #4
crossover T Y P L O C S W R
SMC ME
ANL
CAN
AIE … 1 *
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13. 6. mutate each child bit where a random probability number exceeds the control parameter
* Control parameter should be small (e.g. 10%)
Chromo-somes:
Crossover
mutated T Y P L O C S W R
SMC ME
ANL
CAN
AIE … 1
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14. 7. Insert child into population; replacing the least fit gene
Chromo-somes:
gene #1
gene #2
child
gene #4
gene #5 T Y P L O C S W R
SMC ME
ANL
CAN
AIE … 1
Fitness
(LDA %) 44 63
N/A 67 50
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15. 8. Return to step 3 and repeat this process until one generation has reproduced.
* There is a control parameter, the number of elite genes (those which survive to the next generation) which determines when one generation is complete.
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16. 9. Return to step 2 and repeat for the next generation, until N generations have reproduced.
* There is a control parameter, the number of generations, which determines when this loop terminates.
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17. Control parameters for the GA
Number of genes in the population
Number of generations
Percent of elite genes (those that survive to the next generation) *
The probability of mutations
* In the previous example, we have a very small population and only one reproduction was demonstrated. There are many reproductions per generation.
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18. Control parameters for the GA
Number of genes in the population
Number of generations
Percent of elite genes (those that survive to the next generation) *
The probability of mutations
* In the previous example, we have a very small population and only one reproduction was demonstrated. There are many reproductions per generation.
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19. Linear Discriminate Analysis (LDA)
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20. Computing “Fitness” using LDA
Java object: Zoo
Quality ranking: low
SW metrics: TYP: 1,
LOC:539, SLC: 401,
CLC: 138, Etc.
Java object: Bar
Quality ranking: low
SW metrics: TYP: 1,
LOC:539, SLC: 401,
CLC: 138, Etc.
Objective Function using LDA
Fitness
Value
Java object: Foo
Quality ranking: high
SW metrics: TYP: 1,
LOC:539, SLC: 401,
CLC: 138, Etc.
* This shows computing fitness for
only one gene (set of SW metrics)
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21. SW metric (“known feature”):
Group: High
max
Group: Low
Group:
Medium
SW metric (“known feature”):
LOC
Group: medium-low
SW Metric (“known feature”):TYP
* In reality, we can have up to 16 dimensions (only 2 shown here)
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22. Aspects of LDA function logic
For a point on the previous graph, the LDA algorithm will allocate it to the group based on:
the greatest probability distribution
The prior probability (for the last SW object processed, presumably) is also a factor
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23. Results
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24. Top 5 These are the 6 metrics that were common to the top 5 genes. SLC
WLC
RCC AE
ASL
ACC
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25. Conclusion
The GA metrics appear to indicate that code that is easy to read along with comments help developers understand the purpose of the code.
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