TY - GEN
T1 - Empirical Assessment on Interactive Detection of Code Smells
AU - Albuquerque, Danyllo
AU - Guimaraes, Everton
AU - Braga, Alexandre
AU - Perkusich, Mirko
AU - Almeida, Hyggo
AU - Perkusich, Angelo
N1 - Funding Information:
ACKNOWLEDGEMENTS The authors thank all the subjects that took part in this controlled experiment and the researchers that have collaborated with their feedback on the pilot trials. This research received support from the IFPB employee qualification incentive program (PIQIFPB) - Public Notice Nr 21/2021/PRPIPG.
Publisher Copyright:
© 2022 University of Split, FESB.
PY - 2022
Y1 - 2022
N2 - Code smell detection is traditionally supported by Non-Interactive Detection (NID) techniques, which enable devel-opers to reveal smells in later software versions. These techniques only reveal smells in the source code upon an explicit developer request and do not support progressive interaction with affect code. The later code smells are detected, the higher the effort to refactor the affected code. The notion of Interactive Detection (ID) has emerged to address NID's limitations. An ID technique reveals code smell instances without an explicit developer request, encouraging early detection of code smells. Even though ID seems promising, there is a lack of evidence concerning its impact on code smell detection. Our research focused on evaluating the effectiveness of the ID technique on code smell detection. For doing so, we conducted a controlled experiment where 16 subjects underwent experimental tasks. We concluded that using the ID technique led to an increase of 60% in recall and up to 13% in precision when detecting code smells. Consequently, developers could identify more refactoring opportunities using the ID technique than the NID.
AB - Code smell detection is traditionally supported by Non-Interactive Detection (NID) techniques, which enable devel-opers to reveal smells in later software versions. These techniques only reveal smells in the source code upon an explicit developer request and do not support progressive interaction with affect code. The later code smells are detected, the higher the effort to refactor the affected code. The notion of Interactive Detection (ID) has emerged to address NID's limitations. An ID technique reveals code smell instances without an explicit developer request, encouraging early detection of code smells. Even though ID seems promising, there is a lack of evidence concerning its impact on code smell detection. Our research focused on evaluating the effectiveness of the ID technique on code smell detection. For doing so, we conducted a controlled experiment where 16 subjects underwent experimental tasks. We concluded that using the ID technique led to an increase of 60% in recall and up to 13% in precision when detecting code smells. Consequently, developers could identify more refactoring opportunities using the ID technique than the NID.
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U2 - 10.23919/SoftCOM55329.2022.9911317
DO - 10.23919/SoftCOM55329.2022.9911317
M3 - Conference contribution
AN - SCOPUS:85141624543
T3 - 2022 30th International Conference on Software, Telecommunications and Computer Networks, SoftCOM 2022
BT - 2022 30th International Conference on Software, Telecommunications and Computer Networks, SoftCOM 2022
A2 - Begusic, Dinko
A2 - Rozic, Nikola
A2 - Radic, Josko
A2 - Saric, Matko
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 30th International Conference on Software, Telecommunications and Computer Networks, SoftCOM 2022
Y2 - 22 September 2022 through 24 September 2022
ER -