Software fault localization aims to identify faulty elements in a program by analyzing program information and the execution data of test cases. This process plays a crucial role in improving development efficiency, reducing debugging costs, and ensuring reliable software operation. However, in practical scenarios, due to the large scale of programs and the relatively small proportion of faulty statements, the number of failing test cases in a test suite is lower than that of passing test cases to some extent, leading to an imbalance in defect knowledge. Additionally, mutual interference and masking among multiple faults in a program can introduce characterizing noise into the test suite, further increasing the difficulty of fault localization. To address these limitations, we propose a novel method called SA-BCL, which aims to tackle the issues of defect knowledge imbalance and characterizing noise. SA-BCL comprises four key components: the data processing component, the defect knowledge balancing component, the confident learning denoising component, and the program spectrum reducing component. Specifically, the data processing component constructs the program spectrum by analyzing program behaviors and test results during the execution of test cases. The defect knowledge balancing component mitigates the imbalance of defect knowledge by employing boundary identification and oversampling techniques to augment the spectrum corresponding to failing test cases located at the boundary of the test suite. The confident learning denoising component leverages confident learning techniques to identify and eliminate characterizing noise in the program spectrum. The program spectrum reducing component iteratively computes the suspiciousness scores of program elements and reduces the spectrum to facilitate fault localization. We conduct a series of experiments on both the synthetic multi-fault dataset and the real multi-fault dataset, comparing SA-BCL with baseline approaches. The experimental results clearly demonstrate that SA-BCL outperforms the state-of-the-art methods, achieving improvements of up to 37.5% in average wasted effort, 29.2% in precision, and 22.5% in recall. In addition, SA-BCL maintains comparable time costs within the same order of magnitude as baseline methods, and most of the improvements are statistically significant, demonstrating its robustness.

Application review event inference aims to assess the effectiveness of application problems in response to user actions, which enables application developers to promptly discover and address potential issues in various applications, thereby improving their development and maintenance efficiency. Despite the development of event inference models for app reviews, which extract them as user action and app problem events and establish a relationship model between events and inference labels, the accuracy of these models is constrained due to limitations in labeling and characterizing noise and the lack of robustness and generalization. To address this challenge, we propose a model called Easy Data Augmentation for Application Reviews Event Inference (short for EDA-AREI), which comprises a denoising component, data augmentation component, and event inference prediction component. Specifically, the denoising component identifies labels and characterizes noisy data to enhance dataset quality, the data augmentation component replaces non-stop words with synonyms to increase textual diversity, and the event inference and prediction component reconstructs the classifier using denoised and augmented data. Experimental results on six datasets of one-star app reviews in the Apple App Store demonstrate that the EDA-AREI method achieves anAccuracyof 71.19%, 79.14%, 69.05%, 69.02%, 68.24% and 68.48%, respectively, representing an improvement of 0.83%–2.09% compared to state-of-the-art models. Regarding theF1-score, EDA-AREI achieves values of 71.30%, 69.93%, and 68.76% on the threshold_0.5, k-means_2, and random datasets, respectively, outperforming state-of-the-art models by 1.89%–4.02%. Furthermore, EDA-AREI achievesAUCvalues of 75.66% and 73.37% on the threshold_0.5 and k-means_2 datasets, respectively. As a result, EDA-AREI demonstrates substantial improvements inAccuracy, as well as enhancedF1-scoreandAUCacross most datasets, thereby enhancing the model's accuracy and robustness in identifying related action-problem pairs.