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.

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

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

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