Chenhui Cui

Qi Dong, Rubing Huang, Chenhui Cui et al.

Short-Term Electricity-Load Forecasting (STELF) refers to the prediction of the immediate demand (in the next few hours to several days) for the power system. Various external factors, such as weather changes and the emergence of new electricity consumption scenarios, can impact electricity demand, causing load data to fluctuate and become non-linear, which increases the complexity and difficulty of STELF. In the past decade, deep learning has been applied to STELF, modeling and predicting electricity demand with high accuracy, and contributing significantly to the development of STELF. This paper provides a comprehensive survey on deep-learning-based STELF over the past ten years. It examines the entire forecasting process, including data pre-processing, feature extraction, deep-learning modeling and optimization, and results evaluation. This paper also identifies some research challenges and potential research directions to be further investigated in future work.

Zixiang Xian, Chenhui Cui, Rubing Huang et al.

The advent of large language models (LLMs) has significantly advanced artificial intelligence (AI) in software engineering (SE), with source code embeddings playing a crucial role in tasks such as source code clone detection and source code clustering. However, existing methods for source code embedding, including those based on LLMs, often rely on costly supervised training or fine-tuning for domain adaptation. This paper proposes a novel approach to embedding source code by combining large language and sentence embedding models. This approach attempts to eliminate the need for task-specific training or fine-tuning and to effectively address the issue of erroneous information commonly found in LLM-generated outputs. To evaluate the performance of our proposed approach, we conducted a series of experiments on three datasets with different programming languages by considering various LLMs and sentence embedding models. The experimental results have demonstrated the effectiveness and superiority of our approach over the state-of-the-art unsupervised approaches, such as SourcererCC, Code2vec, InferCode, TransformCode, and LLM2Vec. Our findings highlight the potential of our approach to advance the field of SE by providing robust and efficient solutions for source code embedding tasks.

Rubing Huang, Chenhui Cui, Dave Towey et al.

Adaptive Random Testing (ART) is an enhancement of Random Testing (RT), and aims to improve the RT failure-detection effectiveness by distributing test cases more evenly in the input domain. Many ART algorithms have been proposed, with Fixed-Size-Candidate-Set ART (FSCS-ART) being one of the most effective and popular. FSCS-ART ensures high failure-detection effectiveness by selecting the next test case as the candidate farthest from previously-executed test cases. Although FSCS-ART has good failure-detection effectiveness, it also faces some challenges, including heavy computational overheads. In this paper, we propose an enhanced version of FSCS-ART, Vantage Point Partitioning ART (VPP-ART). VPP-ART addresses the FSCS-ART computational overhead problem using vantage point partitioning, while maintaining the failure-detection effectiveness. VPP-ART partitions the input domain space using a modified Vantage Point tree (VP-tree) and finds the approximate nearest executed test cases of a candidate test case in the partitioned sub-domains -- thereby significantly reducing the time overheads compared with the searches required for FSCS-ART. To enable the FSCS-ART dynamic insertion process, we modify the traditional VP-tree to support dynamic data. The simulation results show that VPP-ART has a much lower time overhead compared to FSCS-ART, but also delivers similar (or better) failure-detection effectiveness, especially in the higher dimensional input domains. According to statistical analyses, VPP-ART can improve on the FSCS-ART failure-detection effectiveness by approximately 50% to 58%. VPP-ART also compares favorably with the KDFC-ART algorithms (a series of enhanced ART algorithms based on the KD-tree). Our experiments also show that VPP-ART is more cost-effective than FSCS-ART and KDFC-ART.