https://doi.org/10.18293/seke2021-060

Mondal AK, Roy B, Sumana SN and Schneider KA, ArchiNet: A Concept-token based Approach for Determining Architectural Change Categories, The 33rd International Conference on Software Engineering & Knowledge Engineering (SEKE), KSIR Virtual Conference Center, Pittsburgh, USA, 2021. pp. 7-14.

Causes of software architectural change are clas-
sified as perfective, preventive, corrective, and adaptive. Change
classification is used to promote common approaches for address-
ing similar changes, produce appropriate design documentation
for a release, construct a developer’s profile, form a balanced
team, support code review, etc. However, automated architectural
change classification techniques are in their infancy, perhaps due
to the lack of a benchmark dataset and the need for extensive
human involvement. To address these shortcomings, we present
a benchmark dataset and a text classifier for determining the
architectural change rationale from commit descriptions. First,
we explored source code properties for change classification
independent of project activity descriptions and found poor
outcomes. Next, through extensive analysis, we identified the
challenges of classifying architectural change from text and pro-
posed a new classifier that uses concept tokens derived from the
concept analysis of change samples. We also studied the sensitivity
of change classification of various types of tokens present in
commit messages. The experimental outcomes employing 10-
fold and cross-project validation techniques with five popular
open-source systems show that the F1 score of our proposed
classifier is around 70%. The precision and recall are mostly
consistent among all categories of change and more promising
than competing methods for text classification.

Computer Science Core Team, Refereed Publications