TechDebt 2022
Tue 17 - Wed 18 May 2022 Pennsylvania, United States
co-located with ICSE 2022
Tue 17 May 2022 11:10 - 11:26 at TechDebt room - ML for TD

Technical Debt (TD) refers to the consequences of taking shortcuts when developing software. Technical Debt Management (TDM) becomes complex since it relies on a decision process based on multiple and heterogeneous data, which are not straightforward to be synthesized. In this context, there is a promising opportunity to use Intelligent Techniques to support TDM activities since these techniques explore data for knowledge discovery, reasoning, learning, or supporting decision-making. Although these techniques can be used for improving TDM activities, there is no empirical study exploring this research area. This study aims to identify and analyze solutions based on Intelligent Techniques employed to support TDM activities. A Systematic Mapping Study was performed, covering publications between 2001 and 2020. From 2276 extracted studies, we selected 111 unique studies. We found a positive trend in the application of Intelligent Techniques to support TDM activities being Machine Learning, Reasoning Under Uncertainty, and Natural Language Processing were the most recurrent ones. Identification, measurement, and monitoring were the more recurrent TDM activities whereas Design, Code, and Architectural were the most frequently investigated TD types. Overall, although the research area is up-and-coming, it is still in its infancy. Meaning that there is a need for more high-relevance empirical studies, and there are a plethora of new opportunities for researchers and practitioners.

Tue 17 May

Displayed time zone: Eastern Time (US & Canada) change

11:10 - 12:00
Research paper
Comprehending the Use of Intelligent Techniques to Support Technical Debt Management
Technical Papers
Danyllo Albuquerque UFCG, Brazil, Everton Guimaraes Pennsylvania State University, USA, Graziela Tonin , Mirko Perkusich VIRTUS, Hyggo Almeida , Angelo Perkusich
PILOT: Synergy between Text Processing and Neural Networks to Detect Self-Admitted Technical Debt
Technical Papers
Amleto Di Salle University of L'Aquila, Alessandra Rota University of Milano Bicocca, Phuong T. Nguyen University of L’Aquila, Davide Di Ruscio University of L'Aquila, Francesca Arcelli Fontana University of Milano-Bicocca, Irene Sala University of Milano - Bicocca
TD Classifier: Automatic Identification of Java Classes with High Technical Debt
Tools Track
Dimitrios Tsoukalas CERTH/ITI, Alexander Chatzigeorgiou University of Macedonia, Apostolos Ampatzoglou University of Macedonia, Nikolaos Mittas International Hellenic University, Dionysios Kehagias Centre for Research and Technology Hellas, Thessaloniki, Greece

Information for Participants
Tue 17 May 2022 11:10 - 12:00 at TechDebt room - ML for TD
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