Trust-based Requirements Traceability

1. Trust-based Requirements Traceability
ICPC 2011, Kingston
Nasir Ali, Yann-Gaël Guéhéneuc, and Giuliano Antoniol

2. Requirements Traceability
• Requirements traceability is defined as “the
ability to describe and follow the life of a
requirement, in both a forwards and
backwards direction” (Gotel, 1994)
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3. What’s Requirements Traceability Good For?
Program Comprehension
Discover what code needs to change to
handle a new requirement
Aid in determining whether a specification is
completely implemented
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4. IR-based Approaches
• Vector Space Model (Antoniol et al. 2002)
• Latent Semantic Indexing (Marcus and Maletic, 2003)
• Jensen Shannon Divergence (Abadi et al. 2008)
• Latent Dirichlet Allocation (Asuncion, 2010)
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5. Problem!
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Requirements0.000132% - Similarity

6. Goal
• Mining software repository to improve
recovery traceability links
• Using software repository links to improve
expert’s trust in an automatically recovered
link
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7. Inspiration
• Web trust model (Palmer, 2000, McKnight, 2002)
• Initial Trust
• Reputation Trust
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8. How do we trust?
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9. How do we trust?
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Other
Approaches

10. Trustrace
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11. Example: Requirements to Code Links

12. Example: Requirements to SVN Links (1)

13. Example: Requirements to SVN Links (2)

14. Example: Re-weighting All Links

15. Case Studies
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Pooka SIP Communicator
Version 2.0 1.0
Number of Classes 298 1,771
Number of Methods 20,868 31,502
LOC 244K 487K
SVN History 2000 – 2010 2005-2010
SIP Communicator: Voice over IP and instate messenger
Pooka: An email Client

16. Hypotheses
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H01: There is no statistical difference in the precision of the
recovered traceability links when using Trustrace or a VSM-
based approach
H02: There is no statistical difference in the recall of the
recovered traceability links when using Trustrace or a VSM-
based approach

17. IR Quality Measures
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18. Identifiers / Commit Messages Extraction
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Extraction
• Class Name
• Method Names
• Variable Names
• Comments
• SVN Commit Messages
• SVN Commit File Names

19. SVN Logs Preprocessing
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We extract CVS/SVN commits and discards those that:
1. Are tagged as “delete”
2. Does not concern source code (e.g., changed manual pages or
documentation only)
3. Have messages of length shorter or equal to two words.

20. Text Preprocessing
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• Filter (#43@$)
• Stop words (the, is, an….)
• Stemmer (attachment -> attach)

21. Information Retrieval (IR) Methods
• Vector Space Model (VSM) (Salton et al., 1975)
– Each document, d, is represented by a vector of ranks of
the terms in the vocabulary:
vd = [rd(w1), rd(w2), …, rd(w|V|)]
– The query is similarly represented by a vector
– The similarity between the query and document is the
cosine of the angle between their respective vectors
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22. Pooka’s Results
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23. SIP Results
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24. Statistical Tests
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Precision
VSM Trustrace p-value
Pooka 42.28 54.35 p<0.01
SIP Com. 14.23 25.13 P<0.01

25. Pooka Results
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26. SIP Results
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27. Statistical Tests
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Recall
VSM Trustrace p-value
Pooka 11.14 12.6 P>0.7
SIP Com. 13.42 16.63 P>0.5

28. Discussion
• Using different source of information reduces an
experts effort up to 50%
• Using temporal information with IR-based
approaches yields better results
• The results tend to improve when increasing the SVN
commit log size
• Trustrace also improves LSI results at k=50 and k=200
values for Pooka and SIP respectively
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29. Threats to Validity
• External validity:
• We analyzed only two systems
• Construct validity:
• The two researchers built both oracles
• Oracles were validated by other two experts
• Internal validity: Different ʎ value may lead to different results
• Reliability validity: replication package is available online at
www.ptidej.net
• Tool is online at www.factrace.net
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30. Ongoing work
More IR approaches and datasets
Empirical study
Including other friends (bug reports etc.)
Determine heuristics to identify the best ʎ
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31. Summary
• Only similarity value is not enough to trust a
link
• Other source of information is required to
increase trust of a link
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