• Tan J, Feitosa D and Avgeriou P. (2023). The lifecycle of Technical Debt that manifests in both source code and issue trackers. Information and Software Technology. 159:C. Online publication date: 1-Jul-2023.

    https://doi.org/10.1016/j.infsof.2023.107216

  • Ho A, Bui A, Nguyen P and Di Salle A. Fusion of deep convolutional and LSTM recurrent neural networks for automated detection of code smells. Proceedings of the 27th International Conference on Evaluation and Assessment in Software Engineering. (229-234).

    https://doi.org/10.1145/3593434.3593476

  • Rio A and Brito e Abreu F. (2023). PHP code smells in web apps. Journal of Systems and Software. 200:C. Online publication date: 1-Jun-2023.

    https://doi.org/10.1016/j.jss.2023.111644

  • Chen Q, Câmara R, Campos J, Souto A and Ahmed I. The Smelly Eight: An Empirical Study on the Prevalence of Code Smells in Quantum Computing. Proceedings of the 45th International Conference on Software Engineering. (358-370).

    https://doi.org/10.1109/ICSE48619.2023.00041

  • Tan J, Feitosa D and Avgeriou P. (2022). Does it matter who pays back Technical Debt? An empirical study of self-fixed TD. Information and Software Technology. 143:C. Online publication date: 1-Mar-2022.

    https://doi.org/10.1016/j.infsof.2021.106738

  • Baldassarre M, Lenarduzzi V, Romano S and Saarimäki N. (2020). On the diffuseness of technical debt items and accuracy of remediation time when using SonarQube. Information and Software Technology. 128:C. Online publication date: 1-Dec-2020.

    https://doi.org/10.1016/j.infsof.2020.106377

  • Jebnoun H, Ben Braiek H, Rahman M and Khomh F. The Scent of Deep Learning Code. Proceedings of the 17th International Conference on Mining Software Repositories. (420-430).

    https://doi.org/10.1145/3379597.3387479

  • Tan J, Feitosa D and Avgeriou P. An empirical study on self-fixed technical debt. Proceedings of the 3rd International Conference on Technical Debt. (11-20).

    https://doi.org/10.1145/3387906.3388621

  • Pecorelli F, Di Nucci D, De Roover C and De Lucia A. On the role of data balancing for machine learning-based code smell detection. Proceedings of the 3rd ACM SIGSOFT International Workshop on Machine Learning Techniques for Software Quality Evaluation. (19-24).

    https://doi.org/10.1145/3340482.3342744

  • Yasir R, Asad M, Galib A, Ganguly K and Siddik M. GodExpo. Proceedings of the 3rd International Workshop on Refactoring. (47-50).

    https://doi.org/10.1109/IWoR.2019.00016

  • Saarimäki N, Lenarduzzi V and Taibi D. On the diffuseness of code technical debt in Java projects of the apache ecosystem. Proceedings of the Second International Conference on Technical Debt. (98-107).

    https://doi.org/10.1109/TechDebt.2019.00028

  • Pecorelli F, Palomba F, Di Nucci D and De Lucia A. Comparing heuristic and machine learning approaches for metric-based code smell detection. Proceedings of the 27th International Conference on Program Comprehension. (93-104).

    https://doi.org/10.1109/ICPC.2019.00023

  • El-Dahshan K, Elsayed E and Ghannam N. Comparative Study for Detecting Mobile Application's Anti-Patterns. Proceedings of the 8th International Conference on Software and Information Engineering. (1-8).

    https://doi.org/10.1145/3328833.3328834

  • Walter B, Fontana F and Ferme V. (2018). Code smells and their collocations. Journal of Systems and Software. 144:C. (1-21). Online publication date: 1-Oct-2018.

    https://doi.org/10.1016/j.jss.2018.05.057

  • Aniche M, Bavota G, Treude C, Gerosa M and Deursen A. (2018). Code smells for Model-View-Controller architectures. Empirical Software Engineering. 23:4. (2121-2157). Online publication date: 1-Aug-2018.

    https://doi.org/10.1007/s10664-017-9540-2

  • Palomba F, Bavota G, Di Penta M, Fasano F, Oliveto R and De Lucia A. (2018). A large-scale empirical study on the lifecycle of code smell co-occurrences. Information and Software Technology. 99:C. (1-10). Online publication date: 1-Jul-2018.

    https://doi.org/10.1016/j.infsof.2018.02.004

  • Gopstein D, Zhou H, Frankl P and Cappos J. Prevalence of confusing code in software projects. Proceedings of the 15th International Conference on Mining Software Repositories. (281-291).

    https://doi.org/10.1145/3196398.3196432

  • Chen Z, Chen L, Ma W, Zhou X, Zhou Y and Xu B. (2018). Understanding metric-based detectable smells in Python software. Information and Software Technology. 94:C. (14-29). Online publication date: 1-Feb-2018.

    /doi/10.5555/3163583.3163670

  • Ó Cinnéide M, Yamashita A and Counsell S. Measuring refactoring benefits: a survey of the evidence. Proceedings of the 1st International Workshop on Software Refactoring. (9-12).

    https://doi.org/10.1145/2975945.2975948

  • Tufano M, Palomba F, Bavota G, Di Penta M, Oliveto R, De Lucia A and Poshyvanyk D. An empirical investigation into the nature of test smells. Proceedings of the 31st IEEE/ACM International Conference on Automated Software Engineering. (4-15).

    https://doi.org/10.1145/2970276.2970340

  • Palomba F, Di Nucci D, Panichella A, Oliveto R and De Lucia A. On the diffusion of test smells in automatically generated test code. Proceedings of the 9th International Workshop on Search-Based Software Testing. (5-14).

    https://doi.org/10.1145/2897010.2897016

  • Le D, Behnamghader P, Garcia J, Link D, Shahbazian A and Medvidovic N. An empirical study of architectural change in open-source software systems. Proceedings of the 12th Working Conference on Mining Software Repositories. (235-245).

    /doi/10.5555/2820518.2820547

  • Palomba F. Textual analysis for code smell detection. Proceedings of the 37th International Conference on Software Engineering - Volume 2. (769-771).

    /doi/10.5555/2819009.2819162

  • Tufano M, Palomba F, Bavota G, Oliveto R, Di Penta M, De Lucia A and Poshyvanyk D. When and why your code starts to smell bad. Proceedings of the 37th International Conference on Software Engineering - Volume 1. (403-414).

    /doi/10.5555/2818754.2818805

  • Chatzigeorgiou A and Manakos A. (2014). Investigating the evolution of code smells in object-oriented systems. Innovations in Systems and Software Engineering. 10:1. (3-18). Online publication date: 1-Mar-2014.

    https://doi.org/10.1007/s11334-013-0205-z

  • Chatzigeorgiou A and Stiakakis E. (2013). Combining metrics for software evolution assessment by means of Data Envelopment Analysis. Journal of Software: Evolution and Process. 25:3. (303-324). Online publication date: 1-Mar-2013.

    https://doi.org/10.1002/smr.584