fault proneness analysis

1. Fault(bug)를 예측하려는 노력
(Quantitative Analysis of Fault Distributions in Complex Software
Systems)
SEEG
김진태

2. Fault, bug, defect, error, failure를 구별하실 수 있으
세요?

3. Fault를 예측하면 무엇이 좋을까요?

4. Fault를 예측할 수 있을까요?

5. 오늘 발표는 fault를 예측해보고자 하는 눈물겨운
이야기 입니다.

7. 2000년 8월, TSE
Norman
Fenton
Professor, Queen
Mary
and Westfield
College, London
Niclas Ohlsson
Ph.D, Technical
director, GratisTel
International

8. 2007년 5월, TSE
Carina
Andersson
research associate in
the Department of
Computer Science,
Lund University,
Sweden
Per Runeson
Professor, software
engineering at Lund
University, Sweden

9. 2013년 4월, TSE
Tihana Galinac
Grbac
Professor, University
of Rijeka, Croatia
Per Runeson
Professor, software
engineering at Lund
University, Sweden
Darko Huljenic
Professor, University of
Zagreb, Croatia

10. 실험을 대상 프로젝트들의 현황

11. 실험을 대상 Module의 현황

17. Hypothesis 1a. A small number of modules contain most of
the faults detected during prerelease testing.
Pre-release post-release
YES
Hypothesis 2a. A small number of modules contain most of the
faults detected during postrelease testing.
YES

18. Hypothesis 1b. If a small number of modules contain most of
the prerelease faults, then it is because these modules
constitute most of the code size.
NO
Hypothesis 2b. If a small number of modules contain most of
the postrelease faults, then it is because these modules
constitute most of the code size.
NO

19. Hypothesis 3. Higher incidence of faults in FT implies higher
incidence of faults in ST.
YES

20. Hypothesis 4. A higher incidence of faults in prerelease
testing implies higher incidence of faults in postrelease.
YES

21. Hypothesis 5a. Smaller modules are less likely to be
failureprone than larger ones.
Hypothesis 5b. Size metrics are good predictors of
prerelease
faults in a module.
Hypothesis 5c. Size metrics are good predictors of
postrelease
faults in a module.
Hypothesis 5d. Size metrics are good predictors of a
module’s
prerelease fault density.
NO
NO
NO
NO

23. 정리