![2
int readFile(String
filePath){
fp =
readFile(filePath)
if fp == NULL
return -1
else
return fp
}
int
manageMemory(int
index){
if mem[index] is
not NULL{
freeInd =
findFreeMemoryLoc()
goto(freeInd)
}
} More Risky
Concern
Can we use
concerns to
study
defects?](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
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Msr2012 chen
- 1. Explaining Software Defects Using Topic Models Tse-Hsun (Peter) Chen, Stephen W. Thomas, Meiyappan Nagappan, Ahmed E. Hassan
- 2. 2 int readFile(String filePath){ fp = readFile(filePath) if fp == NULL return -1 else return fp } int manageMemory(int index){ if mem[index] is not NULL{ freeInd = findFreeMemoryLoc() goto(freeInd) } } More Risky Concern Can we use concerns to study defects?
- 3. Capturing Concerns Using Topic Models manage memory index mem free ind find free memory loc read file file path fp file path fp Topics Models (LDA) Topic 1 Topic 2 read, file, path, fp, file, index, ind 3 manage, memory, mem, free, find, loc
- 4. 4 How defect prone are topics? Can topics explain software defects?
- 5. Case Studies 3 versions of each system 0.4 M ~ 8.8 MLOC 2.8 K ~ 17 K files 1,300 ~ 6,500 post-release defects 5
- 6. 6 How defect prone are topics? Can topics explain software defects?
- 7. If some topics are more defect- prone than others... We can allocate MORE testing resources on these topics! 7
- 8. 8 F1 F2 F3 T1 T2 T3 T4 How to Measure the Defect- proneness of a Topic
- 9. 9 F1 F2 F3 T1 T2 T3 T4 How to Measure the Defect- proneness of a Topic
- 10. 10 What are the Relationship Between Defects and Topics? T3 T2 T1 T4 T3 T2 T1 T4 T3 T2 T1 T4
- 11. 11 Few Topics are Defect-prone Lower color, Jface, Comparison check Task, Eclipse, Eclipse Mylyn, Task ui, Core, Repository
- 12. 12 How defect prone are topics? Can topics explain software defects? Few Topics are Defect-prone!
- 13. 13 How defect prone are topics? Can topics explain software defects? Few Topics are Defect-prone!
- 14. Explaining Defects 14 Lines of Code Pre-release Defects Code Churn Static Historical Topics Topic Metrics
- 15. 15 F1 F2 F3 T1 T2 T3 T4 How Topics May Explain Defects
- 16. 16 F1 F2 F3 T1 T2 T3 T4 How Topics May Explain Defects
- 17. 17 F3 T1 T2 T3 T4 How Topics May Explain Defects F1 F2
- 18. Measuring Explainability 18 Deviance Explained (D1) and AIC1 Deviance Explained (D2) and AIC2 Improvement in Explainability = D2 – D1 and AIC2 – AIC1 Static StaticTopics
- 19. More Topics in a File =>More Defects 19 %Avg.Imp.inD2
- 20. 20 F1 F2 F3 T1 T2 T3 T4 Few Topics are More Defect- prone than Others
- 21. Dealing with Large Number of Metrics 21 Solution: Use PCA to reduce the number of metrics TM and DTM may have as many as 500 variables!
- 22. Topic Memebership Metrics Explain Defects Even More 22 %Avg.Imp.inAIC
- 23. 23 How defect prone are topics? Can topics explain software defects? Few Topics are Defect-prone! YES!
- 24. Limitations 24 1. Parameter Choices •Number of topics •Thresholds 2. Used Baseline Metrics 3. Studied Three Subject Systems Static Historical
- 25. Summary
Editor's Notes
- On the file associated with these topics
- Measure fitingg og
- Mention static historical verbolly
- 2: pic 3,4, count bar chart