Do we need to pay technical debt in blockchain software systems?

Qu, Yubin; Bao, Tie; Chen, Xiang; Li, Long; Dou, Xianzhen; Yuan, Meng; Wang, Hongmei

doi:10.1080/09540091.2022.2067125

Cited by 5 publications

(2 citation statements)

References 36 publications

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“…Recently, Yu et al focused on SATD detection in blockchain projects. In particular, in [34], the authors focus on the use of NLP techniques and a maximum entropy classifier for the identification of different types of SATD in a selection of blockchain projects, while in ref. [35], a large-scale pre-trained model for NLP, and in particular BERT, was used to identify the presence or absence of SATD in each comment of a selection of blockchain projects.…”

Section: Technical Debt In Blockchain Projectsmentioning

confidence: 99%

Investigation on Self-Admitted Technical Debt in Open-Source Blockchain Projects

et al. 2023

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Technical debt refers to decisions made during the design and development of software that postpone the resolution of technical problems or the enhancement of the software’s features to a later date. If not properly managed, technical debt can put long-term software quality and maintainability at risk. Self-admitted technical debt is defined as the addition of specific comments to source code as a result of conscious and deliberate decisions to accumulate technical debt. In this paper, we will look at the presence of self-admitted technical debt in open-source blockchain projects, which are characterized by the use of a relatively novel technology and the need to generate trust. The self-admitted technical debt was analyzed using NLP techniques for the classification of comments extracted from the source code of ten projects chosen based on capitalization and popularity. The analysis of self-admitted technical debt in blockchain projects was compared with the results of previous non-blockchain open-source project analyses. The findings show that self-admitted design technical debt outnumbers requirement technical debt in blockchain projects. The analysis discovered that some projects had a low percentage of self-admitted technical debt in the comments but a high percentage of source code files with debt. In addition, self-admitted technical debt is on average more prevalent in blockchain projects and more equally distributed than in reference Java projects.If not managed, the relatively high presence of detected technical debt in blockchain projects could represent a threat to the needed trust between the blockchain system and the users. Blockchain projects development teams could benefit from self-admitted technical debt detection for targeted technical debt management.

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Section: Technical Debt In Blockchain Projectsmentioning

confidence: 99%

Investigation on Self-Admitted Technical Debt in Open-Source Blockchain Projects

et al. 2023

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“…In the future, we will continue to design stealthy triggers for code models and test backdoor attacks on more software engineering tasks, for example, in tasks such as detecting self-admitted technical debt [62] and software defects prediction [63] [64]. In addition, we are considering using mutation testing or metamorphic testing to perform security testing on code models against poisoning attacks.…”

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confidence: 99%

A Stealthy Backdoor Attack for Code Models

qu,

Huang,

Chen

et al. 2024

Preprint

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Recent studies have shown that code models are susceptible to backdoor attacks. When injected with a backdoor, the victim code model can function normally on benign samples but may produce predetermined malicious outputs when triggers are activated. However, previous backdoor attacks on code models have used explicit triggers, and we aim to investigate the vulnerability of code models to stealthy backdoor attacks in this study. To this end, we propose a backdoor attack approach using Abstract Syntax Tree-based Triggers (ASTT) to obtain stealthiness. We evaluate ASTT on deep learning-based code models and three downstream tasks (i.e., code translation, code repair, and defect detection). With the clustering algorithm, we generated triggers based on abstract syntax trees. We find that the average attack success rate of our ASTT can reach 92.71%. Moreover, our ASTT is stealthy and can effectively bypass state-of-the-art defense approaches. Finally, we verify that the time overhead of our proposed ASTT is small and can meet the needs in real scenarios. Our finding demonstrates security weaknesses in code models under stealthy backdoor attacks.

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A Network Traffic Anomaly Detection Method Based on Shapelet and KNN

Yu,

Xie,

et al. 2024

Lecture Notes in Computer Science

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Do we need to pay technical debt in blockchain software systems?

Cited by 5 publications

References 36 publications

Investigation on Self-Admitted Technical Debt in Open-Source Blockchain Projects

Investigation on Self-Admitted Technical Debt in Open-Source Blockchain Projects

A Stealthy Backdoor Attack for Code Models

A Network Traffic Anomaly Detection Method Based on Shapelet and KNN

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