COMBINING INFORMATION RETRIEVAL MODULES AND ...

35 features. LSICG performs the same to LSI for 5.7% (2) of the 35 bugs. However, LSICG is
less accurate than LSI for 5.7% (2) of the 35 bugs.
For the jEdit study, the results in Table 5.28 and Table 5.30 indicate that LSICG is more
accurate than LSI alone for 89% (8) of 3 features and 6 bugs. LSICG performs the same to LSI
for 11.1% (1) of the 3 features and 6 bugs.
Overall, for the three projects JavaHMO, Rhino and jEdit, LSICG is more accurate than LSI
alone for 87% (60) of the 69 features and bugs. LSICG performs the same to LSI for 7% (5) of
the 69 features and bugs, and LSICG is less accurate than LSI for 6% (4) of the 69 features and
bugs. The overall performance of LSICG is promising. It indicates that LSICG outperforms or at
least performs the same as LSI on 94% of all identified bugs and features in this research study.
5.7 Class Level all bugs/features
In the third experiment, we examine class level structural connectivity. The study uses the bugs
and features identified from the three projects: 25 features from JavaHMO, 35 bugs from Rhino,
and 3 features as well as 6 bugs from jEdit. For this class level study, each document in the
corpora represents one class from the source code. The ranking of the documents represents the
ranking of relevant classes to a given bug or feature.
5.7.1 JavaHMO
For JavaHMO, we use the identified 25 features to test the performance of LSI and LSICG at the
class level. Table 5.32 lists the features information including feature number, feature description
as well as targeted class names for each feature.
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