An Empirical Study on Self-Admitted Technical Debt in Quantum Software
This program is tentative and subject to change.
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.
This program is tentative and subject to change.
Thu 5 DecDisplayed time zone: Beijing, Chongqing, Hong Kong, Urumqi change
16:00 - 17:30 | |||
16:00 30mTalk | 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 30mTalk | 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 30mTalk | 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 |