Social networks, combined with location-based services, offer valuable opportunities to examine social media sentiment and interactions across regions. Information flow within social networks is often highly directional and intense, transcending geographic distances. As a result, Geographically Weighted Regression (GWR), a traditional model that uses geographic distance to measure spatial proximity, falls short in explaining the factors influencing social media sentiment. To address this limitation, this study proposed a bi-directional flow weighted regression (BDFWR) model, supported by large language models, to interpret influencing factors of social media sentiment. The results demonstrated that the BDFWR model outperformed the GWR model by effectively capturing the relationship between social media sentiment and socioeconomic factors. This approach revealed deeper insights into the spatial heterogeneity of social media sentiment across diverse regions, enhancing the accuracy of modelling social media sentiment distribution. Incorporating bi-directional flow distance significantly improved the model’s performance, particularly in cases involving ‘closely low interflow’ and ‘remotely high interflow’ phenomena—critical aspects often neglected in conventional geographic models. Moreover, large language models excelled in detecting implicit positive and negative trends within textual data, offering a promising avenue for advancing sentiment analysis research.

Continuous Integration (CI) is a fundamental practice in modern software development. It enables early fault detection through regression testing, where Test Case Prioritization (TCP) plays a crucial role in improving detection efficiency. Reinforcement Learning-based TCP (RL-TCP) has shown promise in CI scenarios, but its performance often fluctuates due to CI’s dynamic nature. Existing solutions address this by assigning additional rewards or periodically retraining agents. However, these methods either risk over-adjusting strategies due to excessive reliance on additional rewards or fail to respond promptly to changes due to fixed retraining intervals. We propose a novel Environment Adaptation Agent-based RL-TCP method (EAA) that addresses these challenges through a dual mechanism. EAA detects significant environmental changes by analyzing fluctuations in prioritization effectiveness. When a change is detected, it assigns targeted rewards to test cases. EAA also refines the agent’s gradient update so that environmental dynamics are better incorporated into retraining. This enables agents to swiftly adapt while retaining learned prioritization knowledge. Evaluations on 12 real-world industrial datasets show that EAA improves the NAPFD metric by 4.7–24.79% and reduces the average TTF by 35.85–50.37 positions compared to state-of-the-art RL-TCP methods. Additionally, EAA significantly reduces occurrences of NAPFD equal to zero, effectively mitigating prioritization instability.

3D multi-object tracking has been extensively applied in areas such as autonomous driving, uncrewed aerial vehicles, and robots. However, existing 3D multi-object tracking methods still face challenges including inaccurately fitting the true motion of objects, insufficient utilization of trajectory information, and trajectory drift. To address these issues, we propose a 3D multi-object tracking framework driven by multi-level association and intelligent filtering. We design an adaptive prediction module for state estimation that reduces noise and error in prediction information through estimation and smoothing processes, thereby enhancing the stability and accuracy of state prediction. Subsequently, a multi-level trajectory integration-guided association strategy is introduced. This strategy integrates detection results, trajectory data, and potential real-world states of the objects. It minimizes incorrect associations and identity switches, thus achieving more accurate and robust data association. Finally, we propose a quality-aware intelligent filtering module for trajectory correction. This module assesses the quality of all matched detection-trajectory pairs and applies regression correction to low-quality drift detections, effectively reducing trajectory fragmentization and identity switches. On the KITTI test dataset, our method achieves 80.64% HOTA and 53.68% HOTA for the car and pedestrian categories, respectively, while reducing identity switches to 50 and 97. These results demonstrate that, compared with existing methods, our method delivers superior tracking performance and exhibits stronger robustness in handling challenges such as large inter-frame displacements, long-term occlusions, and identity switches.

3D multi-object tracking is an important component of autonomous driving technology. Recent 3D multi-object tracking methods still suffer from issues such as information loss during the fusion of multimodal features, weak discriminative power of the association matrix, and poor robustness of single similarity measure. To address these problems, this paper proposes a Multimodal Collaboration-guided Cascade Association network for 3D multi-object tracking (MCCA-MOT). We design a point cloud feature adaptive diffusion fusion module. This module utilizes inverse distance weighting aggregation diffusion technology to address the issue of information loss during the feature fusion process. This enhances the tracking performance of small objects. Secondly, we propose a dynamic sampling feature cooperative fusion module. This module performs fine-grained local-global feature cooperative fusion based on dynamic sampling, enhancing the distinctiveness of object features. It improves the tracking capability of occluded objects. Finally, in the multi-similarity measure-driven cascading association module, we construct a more discriminative association matrix using multiple types of information and design a cascading strategy. This strategy applies different similarity measures at different association stages for objects with ambiguous features. This reduces identity switches and trajectory fragmentation. Extensive experiments on the KITTI dataset demonstrate the superiority of our method in various performance metrics. Our detailed implementations can be obtained athttps://github.com/yuanfuture/MCCA-MOT.

As a key phase in software testing and debugging, fault localization can significantly influence the efficiency of fixing software faults. Among the various techniques, Mutation-Based Fault Localization (MBFL) is a widely studied fault localization technique that uses mutation analysis to guide the process of localizing faults. However, as the essential input source for MBFL, traditional mutation generates syntactical mutants, which cannot mimic the real faults and may affect the fault localization effectiveness. To address this issue, we resort to a code pre-trained model for program mutation, which is called neural mutation. Neural mutation can generate semantical mutants and even utilize the context information surrounding the mutation position. Based on the neural mutation, we propose Neural-MBFL by utilizing the high-quality mutants generated by neural mutation. To evaluate the effectiveness of Neural- MBFL, we conduct experiments on 393 faulty programs from the Defects4J benchmark. The experiment results show that Neural-MBFL can localize more faults than traditional MBFL in terms of TOP-N (i.e., 9 for TOP-I, 17 for TOP-3 and 18 for TOP-5 on average) and MAP (i.e., 2.32% relative improvement on average). We also analyze the unique faults localized by Neural-MBFL and traditional MBFL. The statistical results show their complementarity. It motivates further analysis into the repair pattern distributions between Neural-MBFL and traditional MBFL to better understand their complementarity. By further comprehensive analysis of the repair pattern distribution, traditional MBFL has advantages in localizing faults related to rule-based code modifications. In contrast, Neural-MBFL has advantages in localizing complex faults requiring deep code comprehension. These findings show that incorporating neural mutation is promising in improving the effectiveness of MBFL.

Programming learning is becoming increasingly prevalent in college curricula, yet novices often encounter substantial difficulties in debugging due to their limited programming experience. In response to these challenges, automatic fault localization methods, such as Spectrum-Based Fault Localization (SBFL) and Mutation-Based Fault Localization (MBFL), have emerged as promising solutions. However, these methods are typically designed for industrial programs, which differ markedly from novice programs in terms of size and complexity. This discrepancy highlights a significant research gap in the application of these methods to novice programs. To address this gap, we conducted an empirical study to evaluate the fault localization performance and execution overhead of SBFL and MBFL in environments typical of novice programmers. Our research specifically examined how various program characteristics, including code coverage and mutation score, affect the accuracy of these localization methods. The study was comprehensive, involving experiments on 190 real novice faulty programs. The findings from our study demonstrate that both SBFL and MBFL are effective for fault localization in novice programs, though MBFL was notably more effective in our tests. MBFL demonstrated superior performance by accurately localizing 67, 96, and 114 faults within the${TOP}-{N} (N=1.\ 3.\ 5)$.

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.

MBFL (Mutation-Based Fault Localization) is one of the most commonly studied fault localization techniques due to its promising fault localization effectiveness. However, MBFL incurs a high execution cost as it needs to execute the test suite on a large number of mutants. While previous studies have proposed mutant reduction methods for FOMs (First-Order Mutants) to help alleviate the cost of MBFL, the reduction of HOMs (Higher-Order Mutants) has not been thoroughly investigated. In this study, we propose SGS (Statement Granularity Sampling), a method which conducts HOMs reduction for HMBFL (Higher-Order Mutation-Based Fault Localization). Considering the relationship between HOMs and statements, we sample HOMs at the statement level to ensure each statement has corresponding HOMs. We empirically evaluate the fault localization effectiveness of HMBFL using SGS on 237 multiple-fault programs taken from the SIR and Codeflaws benchmarks. The experimental results show that (1) The best sampling ratio for HMBFL with SGS is 20%, which preserves the performance and reduces execution costs by 80% ; (2) The fault localization accuracy of HMBFL with SGS outperforms the state-of-the-art SBFL (Spectrum-Based Fault Localization) and MBFL techniques by 20%.

Abstract Compilation errors are unavoidable during the debugging process of novice students. Compiler error messages can help novices to localize and remove errors, but these messages are difficult to understand for students. Previous studies have investigated the compilation error categorization by analyzing compiler error messages, but the categorization cannot cover all kinds of errors, which limits the evaluation of compilation error studies. Therefore, a comprehensive categorization for compilation errors is needed for evaluating the performance of models or tools related to compilation errors. In this study, we first propose a new compilation error categorization, which is based on the smallest unit of the program, tokens. The experiments on 29,573 programs from three datasets show that our proposed compilation error categorization can cover more types of errors and the distribution of the error categorization are significantly different between the datasets. Then, based on our proposed categorization, we develop a neural network model CLACER (CLAssification of Compilation ERrors) for predicting the compilation errors. The results indicate that CLACER can improve the compiler's error localization accuracy and predicts the compilation error effectively. Moreover, based on the proposed categorization, we conduct empirical studies to evaluate the performance of three repairing tools (i.e., DeepFix, RLAssist, and MACER). The comparison results illustrate that DeepFix and RLAssist can fix more errors in the category of delimiter than errors in other categories. Furthermore, MACER performs better than DeepFix and RLAssist because it has a sufficient repairing pattern set for the errors. We also provide some suggestions for improving the repairing tools in the future.

Abstract Identifying the location of faults in real‐world programs is one of the costly processes during software debugging. To reduce the debugging effort, various fault localization techniques have been proposed in recent years. Spectrum‐based fault localization (SBFL) is one kind of widely investigated fault localization technique. Most SBFL techniques first calculate the suspiciousness of program elements (such as statements, methods) to be faulty using the coverage information and execution results of tests. Then a rank list of program elements is generated according to their suspiciousness. However, some SBFL techniques only consider the binary coverage information (i.e., whether the program element is covered) but ignore some of the tests' running behaviors, such as the execution frequency when faults occur in the iteration entities or loop bodies, which are more likely to be faulty followed the propagation‐infection‐execution model. The execution frequency based techniques only replace the feature items of the existing formula limiting their effectiveness in fault localization. In this article, we propose a fault localization technique, class reduction and method call frequency (CRMF), which utilizes mutation analysis and information retrieval techniques. In particular, CRMF first uses mutation analysis to identify and reduce the classes, in which the program elements with a low probability of being faulty. Then we propose a new suspiciousness formula that applies information retrieval and considers method call frequency. To evaluate the effectiveness of CRMF, we conduct empirical studies on 264 real‐world programs from the Defects4J benchmark. Final results show that CRMF outperforms the statement frequency based technique FLSF and SBFL techniques (i.e., Ochiai, OP2, Tarantula, and Dstar) in both single‐fault programs and multiple‐fault programs. Specifically, CRMF can rank 29, 74, and 112 faults at the top 1, 3, 5 ranks and achieve a higher mean reciprocal rank for single‐fault programs and multiple‐fault programs. Finally, we discuss the essence of CRMF and analyze its effectiveness on multi‐fault programs in detail.