Echocardiographic image segmentation plays a crucial role in analyzing cardiac function and diagnosing cardiovascular diseases. Ultrasound imaging frequently encounters challenges, such as those related to elevated noise levels, diminished spatiotemporal resolution, and the complexity of anatomical structures. These factors significantly hinder the model’s ability to accurately capture and analyze structural relationships and dynamic patterns across various regions of the heart. Mamba, an emerging model, is one of the most cutting-edge approaches that is widely applied to diverse vision and language tasks. It efficiently captures global information with linear complexity and compensates for the shortcomings of convolutional neural networks (CNNs) and conventional transformers. To this end, this article introduces a U-shaped deep learning model incorporating a large-window Mamba scale (LMS) module and a hierarchical feature fusion approach for echocardiographic segmentation. First, a cascaded residual block serves as an encoder and is employed to incrementally extract multiscale detailed features. It addresses the vanishing gradient issue by leveraging a residual structure that ensures stable and rapid convergence throughout the training process. Second, a large-window multiscale mamba module is integrated into the decoder to capture global dependencies across regions and enhance the segmentation capability for complex anatomical structures. Furthermore, our model introduces auxiliary losses at each decoder layer and employs a dual attention mechanism to fuse multilayer features both spatially and across channels. This approach enhances segmentation performance and accuracy in delineating complex anatomical structures. Finally, the experimental results using the EchoNet-Dynamic and CAMUS datasets demonstrate that the model outperforms other methods in terms of both accuracy and robustness. For the segmentation of the left ventricular endocardium ($\text{LV}_{\text{endo}}$), the model achieved optimal values of 95.01 and 93.36, respectively, while for the left ventricular epicardium ($\text{LV}_{\text{epi}}$), values of 87.35 and 87.80, respectively, were achieved. This represents an improvement ranging between 0.54 and 1.11 compared with the best-performing model.

The classification of glioma subtypes is critical in clinical diagnosis and treatment planning. Multimodal deep learning enables more informative representations through the integration of diverse and complementary feature sources. In medical imaging, multimodal fusion of radiological and pathological data offers a practical approach for leveraging heterogeneous information for improved glioma subtype classification. Nevertheless, effectively extracting and integrating discriminative features from different modalities remains challenging. In this article, glioma multi-omics ResNet (GMRNet), which is a deep learning model for glioma subtype classification, is proposed. The model integrates radiomic and pathomic features through label-supervised semantic feature fusion to improve the classification performance. It uses a ResNet50 backbone to extract multiscale features from magnetic resonance imaging (MRI) data and whole-slide imaging (WSI) data separately and then fuses these features through feature concatenation. Multi-omics fusion increases the classification accuracy across glioma subtypes, including astrocytoma, oligodendroglioma, anaplastic astrocytoma, anaplastic oligodendroglioma, and glioblastoma. First, MRI and WSI data are passed through a systematic preprocessing pipeline to improve cross-sample consistency and preserve discriminative imaging structures. To overcome the limitations of scarce clinical data, diverse augmentation strategies, such as cropping, flipping, contrast adjustment, and affine transformations, are applied. Second, modality-specific features are extracted using two independent ResNet50-based branches. An MRI branch captures tumor morphology and structural patterns across multiple modalities, while a WSI branch focuses on cellular and tissue-level characteristics. This dual-branch design preserves the unique information of each modality and provides complementary perspectives for downstream fusion. Third, the extracted features are concatenated and passed through a fully connected bottleneck layer to integrate complementary cross-modal information and produce a compact fused representation. The fused representation is then fed into a multilayer classifier to predict glioma subtypes. Finally, the experimental results demonstrate that the proposed model achieves accuracies of 93.65% on the Huashan multimodal glioma (HMG) dataset and 86.47% on the open-access glioma (OAG) dataset. Compared with single-modality configurations, the multimodal fusion setting yields consistent empirical performance gains across both datasets.

Code review is a crucial step in ensuring the quality and maintainability of software systems. However, this process can be time-consuming and resource-intensive, especially in large-scale projects where a significant number of code changes are submitted every day. Fortunately, not all code changes require human reviews, as some may only contain syntactic modifications that do not alter the behavior of the system, such as format changes, variable / function renamings, and constant extractions.

Generate-and-validate (G&V) automated program repair (APR) techniques have been extensively studied during the past decade. Meanwhile, such techniques can be extremely time-consuming due to the manipulation of program code to fabricate a large number of patches and also the repeated test executions on patches to identify potential fixes. PraPR, a recentG furthermore, UniAPR addresses the imprecise patch validation issue by resetting the JVM global state via runtime bytecode transformation. We have implemented UniAPR as a publicly available fully automated Maven Plugin. Our study demonstrates for the first time that on-the-fly patch validation can often speed up state-of-the-art source-code-level APR by over an order of magnitude, enabling all existing APR techniques to explore a larger search space to fix more bugs in the near future. Furthermore, our study shows the first empirical evidence that vanilla on-the-fly patch validation can be imprecise/unsound, while UniAPR with JVM reset is able to mitigate such issues with negligible overhead.

Manual debugging is notoriously tedious and time-consuming. Therefore, various automated fault localization techniques have been proposed to help with manual debugging. Among the existing fault localization techniques, spectrum-based fault localization (SBFL) is one of the most widely studied techniques due to being lightweight. The focus of the existing SBFL techniques is to consider how to differentiate program entities (i.e., one dimension in program spectra); indeed, this focus is aligned with the ultimate goal of finding the faulty lines of code. Our key insight is to enhance the existing SBFL techniques by additionally considering how to differentiate tests (i.e., the other dimension in program spectra), which, to the best of our knowledge, has not been studied in prior work. We present our basic approach, PRFL, a lightweight technique that boosts SBFL by differentiating tests using PageRank algorithm. Specifically, given the original program spectrum information, PRFL uses PageRank to recompute the spectrum by considering the contributions of different tests. Next, traditional SBFL techniques are applied on the recomputed spectrum to achieve more effective fault localization. On top of PRFL, we explore PRFL+ and PRFLMA, two variants which extend PRFL by optimizing its components and integrating Method-level Aggregation technique, respectively. Though being simple and lightweight, PRFL has been demonstrated to outperform state-of-the-art SBFL techniques significantly (e.g., ranking 39.2% / 82.3% more real/artificial faults at Top-1 compared with the most effective traditional SBFL technique) with low overhead (e.g., around 6 minutes average extra overhead on real faults) on 395 real faults from 6 Defects4J projects and 96925 artificial (i.e., mutation) faults from 240 GitHub projects. To further validate PRFL's effectiveness, we compare PRFL with multiple recent proposed fault localization techniques (e.g., Multric, Metallaxis and MBFL-hybrid-avg), and the experimental results show that PRFL outperforms them as well. Furthermore, we study the performance of PRFLMA, and the experimental results present it can locate 137 real faults (73.4% / 24.5% more compared with the most effective SBFL/PRFL technique) and 35058 artificial faults (159.6% / 28.1% more than SBFL/PRFL technique) at Top-1. At last, we study the generalizability of PRFL on another benchmark, Bugs.jar, and the result shows PRFL can help locate around 30 percent more faults at Top 1.

In modern software development, software libraries play a crucial role in reducing software development effort and improving software quality. However, at the same time, the asynchronous upgrades of software libraries and client software projects often result in incompatibilities between different versions of libraries and client projects. When libraries evolve, it is often very challenging for library developers to maintain the so-called backward compatibility and keep all their external behavior untouched, and behavioral backward incompatibilities (BBIs) may occur. In practice, the regression test suites of library projects often fail to detect all BBIs. Therefore, in this paper, we propose DeBBI to detect BBIs via cross-project testing and analysis, i.e., using the test suites of various client projects to detect library BBIs. Since executing all the possible client projects can be extremely time consuming, DeBBI transforms the problem of cross-project BBI detection into a traditional information retrieval (IR) problem to execute the client projects with higher probability to detect BBIs earlier. Furthermore, DeBBI considers project diversity and test relevance information for even faster BBI detection. The experimental results show that DeBBI can reduce the end-to-end testing time for detecting the first and average unique BBIs by 99.1% and 70.8% for JDK compared to naive cross-project BBI detection. Also, DeBBI has been applied to other popular 3rd-party libraries. To date, DeBBI has detected 97 BBI bugs with 19 already confirmed as previously unknown bugs.

Mutation testing is a powerful technique for evaluating the quality of test suite which plays a key role in ensuring software quality. The concept of mutation testing has also been widely used in other software engineering studies, e.g., test generation, fault localization, and program repair. During the process of mutation testing, large number of mutants may be generated and then executed against the test suite to examine whether they can be killed, making the process extremely computational expensive. Several techniques have been proposed to speed up this process, including selective, weakened, and predictive mutation testing. Among those techniques, Predictive Mutation Testing (PMT) tries to build a classification model based on an amount of mutant execution records to predict whether coming new mutants would be killed or alive without mutant execution, and can achieve significant mutation cost reduction. In PMT, each mutant is represented as a list of features related to the mutant itself and the test suite, transforming the mutation testing problem to a binary classification problem. In this paper, we perform an extensive study on the effectiveness and efficiency of the promising PMT technique under the cross-project setting using a total 654 real world projects with more than 4 Million mutants. Our work also complements the original PMT work by considering more features and the powerful deep learning models. The experimental results show an average of over 0.85 prediction accuracy on 654 projects using cross validation, demonstrating the effectiveness of PMT. Meanwhile, a clear speed up is also observed with an average of 28.7× compared to traditional mutation testing with 5 threads. In addition, we analyze the importance of different groups of features in classification model, which provides important implications for the future research.

Mutation testing is one of the most powerful methodologies to evaluate the quality of test suites, and has also been demonstrated to be effective for various other testing and debugging problems, e.g., test generation, fault localization, and program repair. However, despite various mutation testing optimization techniques, mutation testing is still notoriously time-consuming. Regression Testing Selection (RTS) has been widely used to speed up regression testing. Given a new program revision, RTS techniques only select and rerun the tests that may be affected by code changes, since the other tests should have the same results as the prior revision. To date, various practical RTS tools have been developed and used in practice. Intuitively, such RTS tools may be directly used to speed up mutation testing of evolving software systems, since we can simply recollect the mutation testing results of the affected tests while directly obtaining the mutation testing results for the other tests from the prior revision. However, to our knowledge, there is no such study. Therefore, in this paper, we perform the first extensive study (using 1513 revisions of 20 real-world GitHub Java projects, totalling 83.26 Million LoC) on the effectiveness and efficiency of various RTS techniques in speeding up mutation testing. Our study results demonstrate that both file-level static and dynamic RTS can achieve precise and efficient mutation testing, providing practical guidelines for developers.