Technical debt refers to suboptimal code that degrades software quality. When developers intentionally introduce such debt, it is called self-admitted technical debt (SATD). Since SATD hinders maintenance, identifying its categories is key to uncovering quality issues. Traditionally, constructing such taxonomies requires manually inspecting SATD comments and surrounding code, which is time-consuming, labor-intensive, and often inconsistent due to annotator subjectivity. In this study, we investigate to what extent large language models (LLMs) can generate SATD taxonomies. We designed a structured, LLM-driven pipeline that mirrors the taxonomy construction steps researchers typically follow. We evaluated it on SATD datasets from three domains: quantum software, smart contracts, and machine learning. It successfully recovered domain-specific categories reported in prior work, such as Layer Configuration in machine learning. It also completed taxonomy generation in under two hours and for less than ${\$}$1, even on the largest dataset. These results suggest that, while full automation remains challenging, LLMs can support semi-automated SATD taxonomy construction. Furthermore, our work opens up avenues for future work, such as automated taxonomy generation in other areas.

Quantum computers leverage the principles of quantum mechanics to execute operations. They require quantum programs that define operations on quantum bits (qubits), the fundamental units of computation. Unlike traditional software development, the process of creating and debugging quantum programs requires specialized knowledge of quantum computation, making the development process more challenging.

With the increasing prevalence of software incorporating deep neural networks (DNNs), quality assurance for these software systems has become a crucial concern. To this end, various methods have been proposed to repair the misbehavior of DNNs by modifying their weights. However, these repair methods may not meet the developer’s needs for a given dataset and model. In this study, we build prediction models for repair outcomes (i.e., repairs and breaks) to help determine whether the repair method is likely to work. By using our prediction models, developers and operators of DNNs can decide whether or not to apply a repair method, and if so, which method to use. Our prediction models utilize four metrics as explanatory metrics that represent the confidence or ambiguity in the DNN predictions. We experimented with four repair methods and 10 datasets. The experimental results demonstrate that our prediction models successfully select a repair method that meets developers’ needs in 16 out of 24 cases, resulting in an average time saving of 16.29% compared to the naive method. Based on these results, our prediction models can reduce costs for developers and operators when deciding whether to employ repair methods for real-world applications of DNNs.

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.

With the prevalence of software systems adopting neural network models, the quality assurance of these systems has become crucial. Hence, various studies have proposed repairing methods for neural network models so far to improve the quality of the models. While these methods are evaluated by researchers, it is difficult to tell whether they succeed in all models and datasets (i.e., all developers’ environments). Because these methods require many resources, such as execution times, failing to repair neural networks would cost developers their resources. Hence, if developers can know whether repairing methods succeed before adopting them, they could avoid wasting their resources. This paper proposes prediction models that predict whether repairing methods succeed in repairing neural networks using a small resource. Our prediction models predict repairs and side-effects of repairing methods, respectively. We evaluated our prediction models on a state-of-the-art repairing method Arachne on three datasets, Fashion-MNIST, CIFAR-10, and GTSRB, and found our prediction models achieved high performance, an average ROC-AUC of 0.931 and an average f1score of 0.880 for the side-effects and an average ROC-AUC of 0.768 and an average f1-score of 0.725 for the repairs.

If deep learning models in safety–critical systems misbehave, serious accidents may occur. Previous studies have proposed approaches to overcome such misbehavior by detecting and modifying the responsible faulty parts in deep learning models. For example, fault localization has been applied to deep neural networks to detect neurons that cause misbehavior. However, such approaches are not applicable to deep learning models that have internal states, which change dynamically based on the input data samples (e.g., recurrent neural networks (RNNs)). Hence, we propose a new fault localization approach to be applied to RNNs. We propose probabilistic automaton-based fault localization (PAFL). PAFL enables developers to detect faulty parts even in RNNs by computing suspiciousness scores with fault localization using n-grams. We convert RNNs into probabilistic finite automata (PFAs) and localize faulty sequences of state transitions on PFAs. To consider various sequences and to detect faulty ones more precisely, we use n-grams inspired by natural language processing. Additionally, we distinguish data samples related to the misbehavior to evaluate PAFL. We also propose a novel suspiciousness score, average n-gram suspiciousness (ANS) score, based on n-grams to distinguish data samples. We evaluate PAFL and ANS scores on eight publicly available datasets on three RNN variants: simple recurrent neural network, gated recurrent units, and long short-term memory. The experiment demonstrates that ANS scores identify faulty parts of RNNs when n is greater than one. Moreover, PAFL is statistically significantly better and has large effect sizes compared to state-of-the-art fault localization in terms of distinguishing data samples related to the misbehavior. Specifically, PAFL is better in 66.74% of the experimental settings. The results demonstrate that PAFL can be used to detect faulty parts in RNNs. Hence, in future studies, PAFL can be used as a baseline for fault localization in RNNs.

Issue reports are a pivotal interface between developers and users for receiving information about bugs in their products. In practice, issue reports often have incorrect information or insufficient information to enable bugs to be reproduced, and this has the effect of delaying the entire bug-fixing process. To facilitate their bug-reproduction work, GitHub has provided a new feature that allows users to share videos (e.g., mp4 files.) Using such videos, reports can be made to developers about the details of bugs by recording the symptoms, reproduction steps, and other important aspects of bug information.