While metrics generated by static code analysis are well established as predictors of possible future defects, there is another untapped source of useful information, namely your source code revision history. This presentation will discuss converting this revision information into a graph representation, various defect prediction models and how to generate their related change metrics through graph traversal, as well as the potential applications and benefits of these graph enabled prediction models.

2. Nathan

3. Graphs in Software Development
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8. AFFECTS
IMPLEMENTS
COMMITTED TO
RELATING TO
STATUS
PARENT OF
ASSIGNED
TO
REPORTED
AUTHO
RED
AFFECTED
CONTAINSHAS
IS
A
ISA
IS
A
HAS
HAS
BRANCH COMMIT
COMPONENTPROJECT
ISSUE OPEN
ASSIGNEE
REPORTE
R
AUTHOR
PERSON
DIRECTORY
METRICS
FILE

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12. Example Query
//Targeted QA Component Change Report
MATCH (f:File)<-[:AFFECTED]-(c:Commit) -[:
RELATING_TO]->(i:Issue)
WHERE c.date > 1422748800000
AND c.date < 1429283945438
AND NOT ((f.name =~ '.*Test.*')
OR (f.name =~ '.*.xml'))
RETURN i.component,i.type,i.ref, count(DISTINCT
c) AS commitCount, count(f) AS fileAffectedCount
ORDER BY fileAffectedCount DESC,commitCount DESC
LIMIT 25

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15. //Bug commits ordered by number of files
affected
MATCH
(f:File)<-[:AFFECTED]-(c:Commit)-[:RELATING_TO]-
>(i:Issue{issueType:'Bug'})
WITH count(f) asfileCountForCommit,c,i
RETURN c,i,fileCountForCommit
ORDER BY fileCountForCommit DESC

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