![](bg8.png)
ATDx, in (Alves et al., 2010) an interquartile strategy
is adopted to identify the severeness of metric values.
Differently, this study does not focus on ATD and,
while adopting a system-size normalization strategy,
it takes only one level of granularity (NCLOC). In a
recent work, Ulan et al. (Ulan et al., 2019) proposed
a software metric aggregation approach based on dis-
tribution. Our approach is different by (i) focusing
specifically on outlier architectural rule violations, (ii)
considering sizes according to distinct granularities,
and (iii) clustering results into semantic dimensions.
6 CONCLUSIONS
In this study we presented ATDx, an approach de-
signed to gain an encompassing overview of the
source-code detectable ATD present in a software-
intensive system. The ATDx approach concisely en-
tails (i) the manual inspection of pre-existing source-
code rules, (ii) the identification of outlier normalized
values via statistical analysis, and (iii) the aggregation
of analysis results into a set of ATD dimensions. As
future work, we plan to address the identified draw-
backs by refining our theoretical framework of ATDx.
Additionally, we would like to enhance the current ap-
proach by including the temporal factor into our anal-
ysis. Subsequently, we plan to implement a concrete
instance of ATDx, and validate it by conducting a
large-scale study involving industrial partners in or-
der to assess (i) the usefulness and actionability of the
proposed index, (ii) its sensibility w.r.t. different anal-
ysis tools, and its (iii) performance when dealing with
larger datasets. As stated in (Nord et al., 2012), ATD
is a complex, heterogeneous, and multifaceted prob-
lem. Our approach contributes to advance the field
towards establishing a holistic, encompassing, view
of the ATD present in a software-intensive system.
ACKNOWLEDGMENTS
This material is partially based upon work funded and
supported by the Department of Defense under Con-
tract No. FA8702-15-D-0002 with Carnegie Mellon
University for the operation of the Software Engineer-
ing Institute, a federally funded research and develop-
ment center. DM20-0240.
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