Supporting Change Impact Analysis Using a Recommendation System - An Industrial Case Study in a Safety-Critical Context

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Journal first presentation at ICSE'17 in Buenos Aires, Argentina. M. Borg, K. Wnuk, B. Regnell, and P. Runeson. Supporting Change Impact Analysis Using a Recommendation System: An Industrial Case Study in a Safety-Critical Context, IEEE Transactions on Software Engineering, 43(6), pp. 675-700, 2017.

Science

SUPPORTING CHANGE
IMPACT ANALYSIS USING
A RECOMMENDATION
SYSTEM
- An Industrial Case Study in a Safety-
Critical Context
@mrksbrg
Markus Borg, Krzysztof Wnuk, Björn Regnell,
Per Runeson
Research Institutes of Sweden
Swedish Institute of Computer Science

Development engineer, ABB, Malmö, Sweden
 Editor and compiler development
 Safety-critical systems
PhD student, Lund University, Sweden
 Machine learning for software engineering
 Bug reports and traceability
Senior researcher, RISE SICS AB, Lund, Sweden
 Software engineering for machine learning
 V&V for self-driving cars

The Challenge
The Solution
The Evaluation

Reqts. DB
Issue Repo
Code Repo
Test DB Doc. DB

 System evolution since
the 1980s
 ~ 2 MLOC C/C++
 Safety certification
(SIL-2)
Evolving automation system
 Formal development
process
 V model, stage-gate
process
 ~200 SW engineers
 Main sites in Sweden
and India

Automated Change Impact Analysis
 Intuitive tool to jump start analyses based on
historical data
 Goal: faster & more accurate analyses
Approach:
1. Mine the history
2. Recommend impact

Part 1: Construct network of
previously reported impact
Index textual data with

Find similar bugs using Apache Lucene
Part 2: Recommend impact
Design Doc. X.Y
Req. X.Y
Test case UTC56
Req. Z.Y
Design Doc. X.Y
Follow links to identify candidate impact set

Use centrality measures to rank candidate impact
1. Requirement X.Y
2. Design Document X.Y
3. Test case UTC56
4. Design Document X.Y
5. Requirement Z.Y
Find similar bugs using Apache Lucene
Part 2: Recommend impact
Follow links to identify candidate impact set

 Replay change impact
history
 Mine and build
ImpRec using
8 years
 Test ImpRec on
2 years of incoming
bug reports
Experiment - Correctness

Industrial case study - Utility
 Selected two teams
 Conducted interviews
 Deployed ImpRec
 Collected data for 3-9 months
 Logged every click
 Calculated correctness metrics
 Conducted interviews

 Reuse previous change impact analysis effort
 Walk in the footsteps of previous engineers
 Inspired by MSR, IR-based tracing, RSSE
=> ImpRec
 Top-10 recommendations cover 40% of true impact
 Developers find the level of correctness useful
github.com/mrksbrg/ImpRec

THANKS!
markus.borg@ri.se
mrksbrg.com
@mrksbrg
Research Institutes of Sweden
Swedish Institute of Computer Science
PHOTO CREDITS
Brown stink bug
- Marlin E. Rice
Isopods
- Omoshiro Aquarium
SW dev
- Noglif, Flickr:
templetonelliot, ifl,
danburgmurmur
My wife
- My wife

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Supporting Change Impact Analysis Using a Recommendation System - An Industrial Case Study in a Safety-Critical Context

1. SUPPORTING CHANGE IMPACT ANALYSIS USING A RECOMMENDATION SYSTEM - An Industrial Case Study in a Safety- Critical Context @mrksbrg Markus Borg, Krzysztof Wnuk, Björn Regnell, Per Runeson Research Institutes of Sweden Swedish Institute of Computer Science

3. Bug tracker

4. Development engineer, ABB, Malmö, Sweden  Editor and compiler development  Safety-critical systems PhD student, Lund University, Sweden  Machine learning for software engineering  Bug reports and traceability Senior researcher, RISE SICS AB, Lund, Sweden  Software engineering for machine learning  V&V for self-driving cars

5. 5

6. The Challenge The Solution The Evaluation

7. Reqts. DB Issue Repo Code Repo Test DB Doc. DB

9. Reqts. Tests

10.  System evolution since the 1980s  ~ 2 MLOC C/C++  Safety certification (SIL-2) Evolving automation system  Formal development process  V model, stage-gate process  ~200 SW engineers  Main sites in Sweden and India

11.

12. The Challenge The Solution The Evaluation

13. Automated Change Impact Analysis  Intuitive tool to jump start analyses based on historical data  Goal: faster & more accurate analyses Approach: 1. Mine the history 2. Recommend impact

14. Part 1: Construct network of previously reported impact Index textual data with

15. Calculate centrality measures

16. Find similar bugs using Apache Lucene Part 2: Recommend impact Design Doc. X.Y Req. X.Y Test case UTC56 Req. Z.Y Design Doc. X.Y Follow links to identify candidate impact set

17. Use centrality measures to rank candidate impact 1. Requirement X.Y 2. Design Document X.Y 3. Test case UTC56 4. Design Document X.Y 5. Requirement Z.Y Find similar bugs using Apache Lucene Part 2: Recommend impact Follow links to identify candidate impact set

18.

19. The Challenge The Solution The Evaluation

20. Correctness Utility RSSE

21.  Replay change impact history  Mine and build ImpRec using 8 years  Test ImpRec on 2 years of incoming bug reports Experiment - Correctness

22. Experiment - Correctness

23. Industrial case study - Utility  Selected two teams  Conducted interviews  Deployed ImpRec  Collected data for 3-9 months  Logged every click  Calculated correctness metrics  Conducted interviews

24. Industrial case study - Utility

25. Industrial case study - Utility

26. Industrial case study - Utility

27. Conclusion

28.  Reuse previous change impact analysis effort  Walk in the footsteps of previous engineers  Inspired by MSR, IR-based tracing, RSSE => ImpRec  Top-10 recommendations cover 40% of true impact  Developers find the level of correctness useful github.com/mrksbrg/ImpRec

29. Bug tracker

30. Bug tracker

31. THANKS! markus.borg@ri.se mrksbrg.com @mrksbrg Research Institutes of Sweden Swedish Institute of Computer Science PHOTO CREDITS Brown stink bug - Marlin E. Rice Isopods - Omoshiro Aquarium SW dev - Noglif, Flickr: templetonelliot, ifl, danburgmurmur My wife - My wife

Editor's Notes

QUPER model Experienced benefit is not linear Important quality breakpoints
QUPER model Experienced benefit is not linear Important quality breakpoints
QUPER model Experienced benefit is not linear Important quality breakpoints

Supporting Change Impact Analysis Using a Recommendation System - An Industrial Case Study in a Safety-Critical Context

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Editor's Notes