TechDebt 2022
Tue 17 - Wed 18 May 2022 Pennsylvania, United States
co-located with ICSE 2022
Tue 17 May 2022 11:26 - 11:43 at TechDebt room - ML for TD

During the development phase, software programmers usually introduce code that contains issues intentionally left for additional treatment. To allow for future fixing, they mark such code using textual comments, resulting in Self-Admitted Technical Debt (SATD). Detecting SATD contained in source code has become crucial in the development cycle since it helps programmers locate issues that need to be solved, thus improving code quality. We introduce PILOT, a technical debt detector built on top of a combination of different natural language processing (NLP) and machine learning (ML) techniques. First, the semantic among SATD comments is captured using feature extraction steps. Then, neural network algorithms are applied to classify comments, represented as vectors. We built a PILOT prototype with a feed-forward neural network and evaluated it using real-world datasets as proof of concept. The empirical evaluation shows that PILOT obtains an encouraging performance and outperforms a well-established baseline. We anticipate that our tool will come in handy, as once being embedded in the IDE, it can help developers recognize SATD manifested in their code, allowing them to conveniently identify and fix issues.

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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