TY - GEN
T1 - Evaluating Interactive Detection of Code Smells on Software Development Activities
AU - Albuquerque, Danyllo
AU - Guimaraes, Everton
AU - Perkusich, Mirko
AU - Almeida, Hyggo
AU - Perkusich, Angelo
N1 - Publisher Copyright:
© 2023 University of Split, FESB.
PY - 2023
Y1 - 2023
N2 - Traditional code smell detection techniques rely on Non-Interactive Detection (NID), which only allows developers to identify smells in the entire source code upon request. However, studies suggest that NID may lead to fewer correctly identified smells, increasing remaining code smell instances. To address NID's limitations, Interactive Detection (ID) has emerged, allowing developers to reveal smell instances without explicit requests and promoting early detection and correction. In this study, we aimed to evaluate the impact of ID on detecting code smells during software development by conducting a controlled experiment using Eclipse ConCAD with software developers and students. Our results indicated that ID could decrease up to 40% of remaining smell instances compared to NID during software development activities. Our findings suggest that ID is an effective technique to help developers quickly detect and fix code smell instances, improving overall code quality.
AB - Traditional code smell detection techniques rely on Non-Interactive Detection (NID), which only allows developers to identify smells in the entire source code upon request. However, studies suggest that NID may lead to fewer correctly identified smells, increasing remaining code smell instances. To address NID's limitations, Interactive Detection (ID) has emerged, allowing developers to reveal smell instances without explicit requests and promoting early detection and correction. In this study, we aimed to evaluate the impact of ID on detecting code smells during software development by conducting a controlled experiment using Eclipse ConCAD with software developers and students. Our results indicated that ID could decrease up to 40% of remaining smell instances compared to NID during software development activities. Our findings suggest that ID is an effective technique to help developers quickly detect and fix code smell instances, improving overall code quality.
UR - http://www.scopus.com/inward/record.url?scp=85174514770&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85174514770&partnerID=8YFLogxK
U2 - 10.23919/SoftCOM58365.2023.10271584
DO - 10.23919/SoftCOM58365.2023.10271584
M3 - Conference contribution
AN - SCOPUS:85174514770
T3 - 2023 31st International Conference on Software, Telecommunications and Computer Networks, SoftCOM 2023
BT - 2023 31st International Conference on Software, Telecommunications and Computer Networks, SoftCOM 2023
A2 - Begusic, Dinko
A2 - Rozic, Nikola
A2 - Radic, Josko
A2 - Saric, Matko
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 31st International Conference on Software, Telecommunications and Computer Networks, SoftCOM 2023
Y2 - 21 September 2023 through 23 September 2023
ER -