Integrating Large Language Models (LLMs) into software fault localization represents a significant advancement in improving debugging efficiency for programmers. However, novice program fault localization, which is essential for computer science education, has not been thoroughly investigated in previous studies. In contrast to industrial programs target practical functionality, novice programs primarily deal with individual algorithmic issues. The distinct logic structures between novice and industrial programs can impact how effectively LLM understand and process them. Moreover, this difference reveals the inapplicability of the Competent Programmer Hypothesis, a fundamental assumption in industrial fault localization, to novice program fault localization. Therefore, industrial methodologies are unsuitable for novice programming, emphasizing the need for our empirical studies. To fill this gap, we evaluate LLMs’ effectiveness in localizing faults for novice programs in statement level. Using the widely used novice programs dataset Codeflaws and Condefects, we compare the performance of two commercial LLMs (i.e., ChatGPT-3.5 and ChatGPT-4) and three open-source LLMs (i.e., ChatGLM3, Llama2, and Code Llama) against traditional fault localization methods, examining their accuracy and overlap. Additionally, we investigate how prompt engineering improves localization precision. Our findings show ChatGPT-4’s overall superior performance, with ChatGPT-3.5 exhibiting minor advantages in certain cases. ChatGPT-4 outperforms the traditional methods with best performance by 592% and 137% on Codeflaws and Condefects. Specifically, each method exhibits unique strengths in localizing novice programming faults. Moreover, carefully crafted prompts can improve LLMs’ precision. These insights underscore the promising potential of utilizing LLMs for fault localization in novice programming.

Abstract Fault localization is a complex, costly and time‐consuming task in software debugging. Numerous automated techniques have been developed to expedite this process. Mutation‐based fault localization (MBFL) is one of the most widely studied techniques which uses mutation analysis to generate mutants for revealing potential faults in the program. However, our theoretical analysis exposes an inherent conflict between the fundamental assumption and the essential meaning of existing MBFL suspiciousness. This conflict is caused by mutant bias. Intuitively, the suspiciousness can be corrected by eliminating the mutant bias for more accurately measuring the faulty probability of the corresponding mutant statement. In this paper, we introduce Delta4Ms, a fault localization approach designed to eliminate mutant bias. Delta4Ms integrates the principles of signal theory, modelling the actual suspiciousness and mutant bias as the desired and false signal components, respectively. Based on theoretical derivation, the average suspiciousness of mutants serves as an estimate of mutant bias. Delta4Ms effectively mitigates mutant bias, extracting the desired signal and yielding corrected suspiciousness for fault localization. To precisely estimate mutant bias, higher order mutants (HOMs) are incorporated. We conduct an extensive experimental evaluation of Delta4Ms on 320 real‐fault programs from Codeflaws. The results indicate that our model significantly outperforms existing SBFL and MBFL techniques, showing a considerable improvement in fault localization effectiveness. We further assessed the robustness of Delta4Ms by examining different HOM ratios and HOM generation strategies. Moreover, Delta4Ms achieves a substantial reduction in mutation execution cost and minimal accuracy loss through the implementation of test case reduction. Finally, we perform preliminary experiments on 15 real‐fault programs from the Defects4J benchmark to assess the generalization of the model's fault localization effectiveness.

With the rapid development of society and the popularity of smart devices, the volume of information sent and received is increasing day by day. It has become very difficult to accurately and efficiently match a large number of events with a large number of subscriptions, and the event and subscription matching speed can no longer meet demand. To speed up the matching speed of events and subscriptions, this paper uses similarity and correlation to optimize the clustering operation and increase the data transfer and data throughput of the category fusion strategy. Firstly, the clustering operation is performed on the subscription messages, and the category to which the events belong is found according to the clustering result. Subsequently, in the category, the subscriptions matching the events are found. An on-the-fly subscription publishing algorithm is proposed to coordinate spatial information and event attribute information to handle not only the matching operation of events and subscriptions on-the-fly but also to perform subscription updates and category updates on the distributed environment on-the-fly. It can also perform clustering operations and matching operations instantly without prior knowledge. We design a distributed system for the publish–subscribe algorithm and propose a load balancing strategy for this algorithm on the distributed system. Subsequently, we experimentally validate the proposed publish–subscribe algorithm in this paper by building our own cluster and using real data.