Automated Program Repair (APR) is increasingly vital for managing the complexity of modern software systems. However, current APR techniques suffer from inefficiently selecting repair components, resulting in suboptimal patches. To address these limitations, we propose PCRepair, a context-aware template-based methodology for automated software fault repair. This approach integrates predefined repair templates with context-aware analysis to improve repair accuracy and efficiency. PCRepair first localizes suspicious statements via the Ochiai technique, then matches their contextual patterns with relevant templates. This strategy narrows the search space and generates semantically relevant candidate patches. We prioritize these patches using a weighted fusion similarity metric and sequentially validate them against existing test cases. Evaluations on the Defects4J benchmark show that PCRepair successfully repaired 38 defects, demonstrating competitive performance compared to existing methods, particularly in terms of repair efficiency, with a 9.62% success rate and an average repair time of fewer than 30 min per defect.

Software defect prediction (SDP) is an effective approach to ensure software reliability. Machine learning models have been widely employed in SDP, but they ignore the impact of class imbalance, noise and outliers on the prediction performance. This study proposes a fuzzy cost broad learning system (FC‐BLS). FC‐BLS not only handles class imbalance problems but also considers the specific sample distribution to address noise and outliers in software defect datasets. Our approach draws fully on the idea of the cost matrix and fuzzy membership functions. It introduces them to BLS, where the cost matrix prioritises the training errors on the minority samples. Hence, the classification hyperplane position is more reasonable, and fuzzy membership functions calculate the membership degree of the sample in a feature mapping space to remove the prediction error caused by noise and outlier samples. Then, the optimisation problem is constructed based on the idea that the minority class and normal instances have relatively high costs. By contrast, the majority class and noise and outlier instances have relatively small costs. This study conducted experiments on nine NASA SDP datasets, and the experimental findings demonstrated the effectiveness of the proposed methodology on most datasets.

With the increasing scale and complexity of software recently, automated software bug repair has grown in importance. However, the current automated software bug repair process suffers from issues such as coarse-grained repair granularity and poor patch quality. To address these problems, we propose a graph mining template-based automatic software repair (GMRepair) to improve the performance of automated software bug repair. First, this approach adopts the Ochiai fault localization technique to locate and generate a list of suspicious defect statements. We utilize the GumTree tool to parse the bug and repair program files, generating edit scripts. These edit scripts are then transformed into a graphical representation. Second, we utilize a frequent graph miner to obtain graph mining templates by matching the context of the suspicious statements with the context of the graph mining templates, generating an initial population for them. The buggy program is evolved using genetic programming through mutation and crossover operations, generating new individuals. Finally, we sequentially pass the candidate patches (CPs) through corresponding test cases and prioritize the test cases using priority sorting techniques. Patches that fail to pass the test cases are filtered out, and the patches that pass the test cases are output. We conducted the experiments using two datasets, QuixBugs and Defects4J. In Defects4J, the GMRepair successfully repaired 41 defects, while in QuixBugs, it successfully repaired 15 defects. Compared to the existing methods, GMRepair offers a higher success rate and efficiency in defect repair.

For pattern recognition tasks, effective representation of key features in data is crucial. Complex and redundant information in practical applications can interfere with robust feature extraction and weaken the expression of discriminative features. Most existing methods focus on directly fitting the label space, and have limited expression of detailed reconstruction of the inherent structure of data. To solve these issues, a novel relaxed block diagonal sparse embedding (RBDSE) algorithm is proposed for image recognition. Specifically, we focus on the global and local structural details of data by imposing$l_{2,1}$norm and$F$norm constraints on the projection matrix. In addition, prior knowledge is introduced to construct a relaxed block diagonal matrix to intervene in the representation learning process, enhancing intra-class similarity and inter-class difference of the reconstructed data. Simultaneously, the joint classification regression term fully utilizes the label information. The interactive supervised learning between projection and representation further strengthens the discriminability and interpretability of feature representation. Finally, the performance advantages of RBDSE against other state-of-the-art methods are comprehensively verified on public benchmarks.

Representation learning is widely used to project high-dimensional data to low-dimensional subspace for feature extraction in image recognition tasks. However, many related methods barely explore the fuzziness and uncertainty between data classes. Besides, the classical unsupervised sparse constraint weakens the evaluation of feature importance and neglects the preservation of discriminant information during sparse representation. To solve these issues, a novel fuzzy discriminative block representation learning (FDBRL) algorithm is proposed for image feature extraction. FDBRL aims to enhance the discriminability of subspace by designing effective constraints for projection learning. Specifically, based on the label information and the fuzzy relation between data, we construct a fuzzy block weight matrix and embed it into the${l_{2,1}}$norm regularization term to realize supervised sparse constraint for the representation learning. Next, the low-rank constraint is used to capture the inherent global structure information of data. Finally, we introduce a classification loss term with transformation matrix for joint optimization, such that the projection learning is not limited to number of classes, and the discriminative ability is further improved. Comprehensive experimental results on six benchmarks verify that our method achieves promising performance with other state-of-the-arts in both robustness and effectiveness.