![](bg9.png)
6 DISCUSSION AND FUTURE
WORK
We used the Data Follow Graph model to illustrate the
abstracted models due to ease of understanding for the
domain experts. We received various qualitative feed-
back, such as: “[. . . ] I can now understand the user
interactions and assess the user behaviors without be-
ing overwhelmed by many activities that do represent
the actual processes, [. . . ] I am wondering why we
have such a bad performance on restoring files, I need
to investigate this.”, referring to a violation of a KPI
for restoring files that was previously unknown. Thus,
the proposed method could successfully identify non-
functional requirements and draw developers’ atten-
tion to the right software components for further tech-
nical investigations.
Despite our effort to propose a generalizable event
log abstraction technique, the suggested method is
only applicable to client-server applications. We have
to acknowledge that one of the most time-consuming
and manual efforts in this technique is to evaluate
the input log for noise, outliers, and anomalies in the
event log. Unfortunately, despite our attempt, our
case studies’ report lacks the exploration of the pre-
cision and the generalization as other quality indica-
tors due to extensive computing power required for
calculating these factors via ProM implementation.
Thus these calculations never terminated. Moreover,
we use the concatenation of activity names for rela-
beling task; yet, this may cause readability issues for
some domain experts if the number of original activ-
ities increases drastically. Lastly, we are also relying
on event logs being generated by the OAuth2 work-
flow (authorization service). Consequently, there may
be server-side activities that are not recorded. So, fur-
ther study is required to validate our approach while
including all executing software components.
7 CONCLUSIONS
This paper describes a novel approach for event log
abstraction in client-server applications. In Section
2, we discussed the state of the art and elaborated on
the benefits and shortcomings of each work, and ex-
plained how our suggested method could overcome
existing limitations for the system under study. Our
suggested approach enabled us to gradually abstract
the fine-grained event logs to higher levels without
losing essential information, enabling the domain ex-
perts to use the appropriate discovered model for fur-
ther analysis. Besides answering our research ques-
tions, we evaluated the proposed approach with the
help of two real-life experiments. Overall, this work’s
contributions are adaptability of the approach to any
client-server application and enabling automatic rela-
beling of abstracted activity names at a desired level
of granularity. Additionally, it empowers discovering
the exact software execution life-cycle, facilitating the
discovery of user behavior on client-server applica-
tions with high accuracy.
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