Detecting Bad Smells with Machine Learning Algorithms: an Empirical Study (TechDebt 2020 - International Conference on Technical Debt 2020) - TechDebt 2020

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Sun 28 - Tue 30 June 2020

co-located with ICSE 2020

Who

Daniel Cruz, Eduardo Figueiredo, Amanda Santana

Track

Time Zone

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When

Tue 30 Jun 2020 15:00 - 15:20 at TechDebt - Technical Debt in Source Code and Quality Chair(s): Matthias Galster

Daniel Cruz

Eduardo Figueiredo

UFMG

Brazil

Amanda Santana

Time Zone

The program is currently displayed in (UTC) Coordinated Universal Time.

Use conference time zone: (UTC) Coordinated Universal TimeSelect other time zone

The GMT offsets shown reflect the offsets at the moment of the conference.

Time Band

By setting a time band, the program will dim events that are outside this time window. This is useful for (virtual) conferences with a continuous program (with repeated sessions).
The time band will also limit the events that are included in the personal iCalendar subscription service.

Display full programSpecify a time band

Session Program

Tue 30 Jun
Times are displayed in time zone: (UTC) Coordinated Universal Time change

	15:00 - 16:00: Technical Debt in Source Code and QualityTechDebt 2020 at TechDebt Chair(s): Matthias GalsterUniversity of Canterbury Every paper talk includes a 15 minute talk followed by 5 minutes for Q&A.

	15:00 - 15:20 Research paper		Detecting Bad Smells with Machine Learning Algorithms: an Empirical Study TechDebt 2020 Daniel Cruz, Eduardo FigueiredoUFMG, Amanda Santana
	15:20 - 15:40 Research paper		An Empirical Study on Self-Fixed Technical Debt TechDebt 2020 Jie Tan, Daniel FeitosaUniversity of Groningen, Paris AvgeriouUniversity of Groningen, The Netherlands
	15:40 - 16:00 Research paper		How Junior Developers Deal with Their Technical Debt? TechDebt 2020 Fabian GilsonUniversity of Canterbury, Miguel Morales, Moffat Mathews