In today's software testing community, quality assessment remains critical, with mutation testing standing as a cornerstone technique for evaluating the effectiveness of test cases. This method involves introducing faulty code, or mutants, into the program to assess the quality of the test suite and other testing methods. However, mutation testing faces challenges such as the generation of numerous mutants, the presence of equivalent mutants that are difficult to detect through testing, and the lack of realistic mutant creation. Literature reviews indicate significant efforts to address these issues through formal solutions and heuristic methods. Recent optimization-based methods are now recognized as cost-effective result in an optimized solution. Hence, to overcome these limitations, this study introduces SQUMUTH, a novel approach for high-order mutant generation based on the Squirrel Search Algorithm (SSA). Inspired by the foraging behavior of squirrels, SSA offers a promising solution for enhancing the efficiency and effectiveness of mutation testing. Experimental evaluations on eight well-known Java benchmark programs demonstrate that SQUMUTH outperforms existing methods. Comparative analyses of mutation scores and the rates of realistic mutants consistently show its better performance across all subject programs compared to other state-of-the-art methods such as Social Group Optimization, Binary Genetic Algorithm, and random testing. The experimental results underscore its effectiveness in generating more realistic mutants. The experimental results indicated that the proposed approach has the potential to advance software testing by improving the cost-effectiveness of mutation analysis and the quality of software systems.

Sleep staging plays a crucial role in evaluating sleep quality and diagnosing various neurological and physiological disorders, including insomnia, sleep apnea, and narcolepsy. Conventionally, the annotation of sleep stages is performed manually or through semi-automated techniques based on polysomnography (PSG), requiring trained clinicians to interpret complex bio-signals. This process is inherently tedious, time-consuming, and subject to inter-scorer variability, which affects consistency and scalability in clinical practice. To address these challenges, we propose a Multi-interactive Learning Model for Sleep Staging (MILMSS), which leverages the heterogeneous nature of PSG signals by integrating a stacking-ensemble learning architecture (SEA) within a multi-interaction framework. The SEA module performs robust feature extraction from individual modalities such as EEG, EOG, and EMG, capturing local temporal dependencies and preserving physiological signal characteristics. These features are then passed into the multi-interactive learning block, which models cross-modal interactions and temporal continuity through adaptive attention mechanisms and hierarchical fusion. The model was rigorously evaluated on six publicly available PSG datasets: Sleep Heart Health Study (SHHS), Sleep European Data Format 2013 (S-EDF-13), Sleep-EDF 2018 (S-EDF-18), S-EDF-18 Sleep Cassette recordings + Sleep Telemetry recordings (S-EDF-18-SC + ST), Dreams (DRMS), and ISRUC-Sleep (SG3). Using cross-validation protocols across all datasets, our MILMSS model demonstrated consistently superior performance, achieving classification accuracies of 97.42%, 92.22%, 90.13%, 95.10%, 92.98%, and 96.49%, and corresponding Cohen’s kappa scores of 0.95, 0.89, 0.85, 0.93, 0.90, and 0.95, respectively. Overall, our proposed model offers a highly accurate, scalable, and interpretable solution for automated sleep staging. It holds strong potential for integration into both clinical diagnostic workflows and portable sleep monitoring systems, thereby addressing the growing need for reliable and cost-effective sleep health technologies.