Software aging results from aging-related bugs (ARBs) in long-running systems, that usually causes performance decline and system crashes. Since collecting ARB data is challenging due to its scarcity, it hinders the development of effective prediction models. Moreover, existing cross-project ARB prediction methods often ignore project-specific distribution differences and neglect class imbalance and overlap issues between ARB and non-ARB classes. In this paper, a hybrid approach that combines the balanced distribution adaptation (BDA), the improved subclass discriminant analysis (ISDA), and the self-paced ensemble under-sampling (SPE) techniques, called BISP in short, is proposed to address the aforementioned problems. The main idea behind BISP is first to use BDA to adaptively reduce the difference of projects' marginal distribution and conditional distribution, and then employ ISDA and SPE to alleviate the severe class imbalance together with class overlap. Experimental results obtained for six classifiers and six cross-project datasets show that compared with the state-of-the-art approaches TLAP and JDA-ISDA based on transfer learning, BISP improves the average balance by 34.8% and 2.3% and improves the average AUC by 26.5% and 8.4%, respectively. Compared with the deep learning approach SRLA, BISP can improve the average balance value by 5.1%.

Software aging refers to a problem of performance decay in long-running software systems. This phenomenon is primarily attributed to the accumulation of run-time errors, commonly known as aging-related bugs (ARBs). Detecting ARBs through Aging-related Bug Prediction (ARBP) is crucial in ensuring system reliability. The effectiveness of ARBP heavily relies on the quality of datasets. However, ARB datasets often suffer from class overlap, where instances from different classes exhibit similar feature values. Class overlap poses a significant challenge as it compromises the quality of training data and subsequently impacts ARBP accuracy. To address this issue, we propose an improved Fuzzy C-means clustering method named IFCM, designed to mitigate class overlap in ARBP tasks. IFCM can identify whether an instance occurs overlap, and identify the overlap degree of this instance through the predefined parameters. We evaluate our proposed method on two public datasets Linux and MySQL and one self-collected dataset NetBSD using five different classifiers with five performance metrics (AUC, F1, Balance, PD, PF). Comparison with four existing methods (No clean, NCL, IKMCCA, ROCT) demonstrates that IFCM is effective in alleviating class overlap in ARBP. For Instance, IFCM achieves promising results in terms of AUC blue (which are 0.762, 0.757, and 0.642) and Balance (which are 0.709, 0.736, and 0.595) at the dataset level.

Item recognition has become a hotspot in the field of computer vision research. SIFT has the advantage of requiring a low amount of information, a fast running speed and high precision, but it requires large data calculations and thus takes a long time to perform the item recognition. In this paper we propose a method of item recognition based on SIFT and SURF that provides a new way to solve the problem of item recognition, and has both feasibility and availability. This technique currently ignores colour information when dealing with colour images, but the evaluation method is capable of taking colour quality characteristics into account so it should be possible to improve the algorithm in the future. Experimental results show that this system of item recognition based on the SURF algorithm gives better matching recognition, is faster and has greater robustness.