APSEC 2024
Tue 3 - Fri 6 December 2024 China
Thu 5 Dec 2024 16:30 - 17:00 at Room 4 (Xianglin Ballroom) - Session (15) Chair(s): Xiaoxue Ren

Quantum computers, which utilize the principles of quantum mechanics, are expected to be applied to a wide range of fields. With the advancement of quantum computer development, a lot of quantum software, which enables the operation of quantum computers, has been developed. It has a distinct nature (e.g., superposition and entanglement of qubits) compared to traditional software, leading to the unique challenges of its development. While prior studies have clarified and defined some unique challenges of quantum software, many remain unclear due to limited research. In this study, we conducted an empirical study of Self-Admitted Technical Debt (SATD) for quantum software. SATD is a type of technical debt, a problem in the code that the developer is aware of. Hence, we conjecture that analyzing SATDs can reveal the unique challenges developers face when developing quantum software. We manually coded 202 comments from the Python® files of the 61 open-source quantum software on GitHub®. The 202 comments correspond to a 95% confidence level with a 5% confidence interval, as in previous studies. The results showed that 88 comments (45.6% of all SATD comments) were quantum-specific SATDs (QSATDs), which require knowledge of quantum computation to repay. Furthermore, we propose a taxonomy for QSATDs. This taxonomy, which consists of four main categories and eight subcategories, classifies QSATDs in terms of quantum-specific aspects such as circuit implementation, backend, and algorithms. Our empirical results are beneficial for quantum software developers, helping them understand implementation areas that require attention. For researchers, our results promote further research, including the exploration of challenges in QSATD repayment.

Thu 5 Dec

Displayed time zone: Beijing, Chongqing, Hong Kong, Urumqi change

16:00 - 17:30
Session (15)Technical Track at Room 4 (Xianglin Ballroom)
Chair(s): Xiaoxue Ren Zhejiang University
16:00
30m
Talk
On the Relative Value of Feature Selection Techniques for Code Smell Detection
Technical Track
Zexian Zhang Wuhan University of Technology, Shuang Yin Wuhan University of Technology, Lin Zhu Wuhan University of Technology, Shan Gao Hokkaido University, Haoxuan Chen Wuhan University of Technology, Wenhua Hu Wuhan University of Technology, Fuyang Li Wuhan University of Technology
16:30
30m
Talk
An Empirical Study on Self-Admitted Technical Debt in Quantum Software
Technical Track
Yuta Ishimoto Kyushu University, Yuto Nakamura Kyushu University, Ryota Katsube Hitachi, Ltd., Naoto Sato Research & Development Group, Hitachi, Ltd., Hideto Ogawa Hitachi Ltd., Masanari Kondo Kyushu University, Yasutaka Kamei Kyushu University, Naoyasu Ubayashi Kyushu University
17:00
30m
Talk
Deep Learning and Data Augmentation for Detecting Self-Admitted Technical Debt
Technical Track
Edi Sutoyo Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence, University of Groningen, Paris Avgeriou University of Groningen, The Netherlands, Andrea Capiluppi University of Groningen