Metamorphic Relations (MRs) play a key role in determining the fault detection capability of Metamorphic Testing (MT). As human judgement is required for MR identification, systematic MR generation has long been an important research area in MT. Additionally, due to the extra program executions required for follow-up test cases, some concerns have been raised about MT cost-effectiveness. Consequently, the reduction in testing costs associated with MT has become another important issue to be addressed. MR composition can address both of these problems. This technique can automatically generate new MRs by composing existing ones, thereby reducing the number of follow-up test cases. Despite this advantage, previous studies on MR composition have empirically shown that some composite MRs have lower fault detection capability than their corresponding component MRs. To investigate this issue, we performed theoretical and empirical analyses to identify what characteristics component MRs should possess so that their corresponding composite MR has at least the same fault detection capability as the component MRs do. We have also derived a convenient, but effective guideline so that the fault detection capability of MT will most likely not be reduced after composition.

The Internet world is moving toward a scenario where users and applications have very diverse service expectation, making the current best-effort model inadequate and limiting. To be able to design high-availability service systems, it is essential to consider not only the actual failure and recovery behavior of the service infrastructure, but also the behavioral aspects of its user and their subjective perceptions and reactions in the wake of failure events. In this paper, we propose to use Markov regenerative process (MRGP) models to study the availability of Internet-based services perceived by a Web user on two different online service scenarios: (1) single-user-single-host and (2) single-user-multiple-host. The MRGP models capture the interactions between the service facility and the user. We also detect its parameter bottlenecks by applying the formal sensitivity analysis technique. The trends of the users' perceived unavailability are analyzed with the changed different parameter values, and the necessity of the sophisticated MRGP modeling is evidenced by the comparisons with the corresponding continuous time Markov chain (CTMC) models, which show that the popular convenient CTMC models tend to overestimate user-perceived service unavailability. Finally, controlled experiments are carried out on a real Web service to demonstrate the proposed approach.

Deep Packet Inspection (DPI), which is one of the most important network techniques, has been widely utilized in current networking systems. By comparing the payloads of traffic to an existing signature database, DPI can identify whether traffic or packet is harmful, or belong to which application. The literal matching engine, which plays a key role in DPI, is the primary determinant of the system performance. FDR, an engine that can match a character with multiple literals in only 3 SIMD operations, has been developed. However, FDR has a significant performance drop-off when the signature database is composed of small-scale literal rule sets, whose occupations are larger than 90% in the modern database. In this paper, we have designed Teddy, an engine that is highly optimized for small-scale literal rule sets. Comparing with FDR, Teddy significantly increases the efficiency in small-scale literal rule sets by designing a novel algorithm that can parallelly match up to 64 characters with only 16 SIMD operations. Meanwhile, to evaluate Teddy in real-world DPI systems, we have implemented Teddy in Hyperscan with AVX512 platforms. The evaluation results show that Teddy achieves a maximum of 35.29x performance increases than Aho-corasick (AC), 2.16x performance increases than FDR in most cases.

Chromatic dispersion appears to be a major performance limiting problem in optical intensity modulation direct detection (IM/DD) transmission systems, especially for a double-sideband (DSB) signal. We propose a complexity-reduced look-up-table based maximum likelihood sequence estimation (LUT-MLSE) for DSB C-band IM/DD transmission based on pre-decision-assisted trellis compression and a path-decision-assisted Viterbi algorithm. To further compress the size of the LUT and reduce the length of the training sequence, we proposed a finite impulse response (FIR) and LUT hybrid channel model for the LUT-MLSE. For PAM-6 and PAM-4, the proposed methods can compress the size of the LUT into 1/6 and 1/4, and reduce the number of multipliers by 98.1% and 86.6% with slight performance degradation. We successfully demonstrate a 20-km 100-Gb/s PAM-6 and a 30-km 80-Gb/s PAM-4 C-band transmission over dispersion-uncompensated links.

Mandelbug-caused software failures are significant threats to system availability, especially in the context of mission-critical and safety-critical systems. However, there is still no systematic method for keeping the software free from Mandelbugs before release. To guarantee the availability of systems suffering from Mandelbugs, environmental-diversity-based fault tolerance techniques have been proposed to recover from the failures caused by them. In this article, we develop and study an analytic model to assess the availability of systems that utilize a sequence of environmental-diversity-based recovery methods. Improving over previous relevant studies, the availability formula we obtain in this article works for any number of recovery methods the system is equipped with; it is also independent on both the nature of those recovery methods and the order of their utilization. In addition, we consider the problem of how to arrange the set of available recovery methods to achieve the largest system availability. Based on the results of our analysis, we develop an open-source tool, called OPENS, which assists in the calculation of the optimal system availability. We validate the effectiveness of the proposed modeling approach in two ways, namely by comparing our results with those obtained for specific systems considered in relevant studies and by conducting numerical analyses for more general scenarios of its application.

Accurately predicting the remaining useful life (RUL) of lithium-ion batteries is critical for anticipating battery failure and enabling effective health management. However, existing RUL prediction methods often suffer from several limitations, including the need for large volumes of training data, significant differences across datasets, and insufficient accuracy in long-term forecasting, which hinder their applicability in real world scenarios. To address these challenges, this paper proposes a hybrid model that integrates transfer learning (TL) and particle filtering (PF) with the Mogrifier LSTM (MLSTM) network. Specifically, the model first employs a transfer learning-based Mogrifier LSTM (TL-MLSTM) to perform long-term prediction of battery capacity, thereby enhancing the model’s generalization ability to accommodate RUL prediction under varying operating conditions. Subsequently, the capacity predictions generated by TL-MLSTM are used as observations in the PF algorithm, which iteratively updates the battery state parameters and refines the capacity predictions, thereby further improving accuracy. The proposed model is validated using publicly available datasets comprising multiple types of batteries under various operational conditions. Experimental results demonstrate that the model achieves an average RMSE of 0.0199, MAPE of 0.5803%, MAE of 0.0167 and APE of 11 cycles across multiple test groups. Compared with standalone models or purely data-driven approaches, the proposed method exhibits significant advantages in robustness and accuracy for long-term capacity degradation prediction.