The SPEC SERT suite is the industry-standard benchmark for evaluating server energy efficiency and is widely adopted in government regulations and certification programs. Current certification rules require every CPU memory channel to be populated with at least one Dual In-line Memory Module (DIMM). In real-world deployments, however, servers are sometimes configured with fewer DIMMs, leaving some channels unpopulated. This discrepancy introduces a significant gap between certified efficiency scores and the actual efficiency of deployed systems. To address this issue, there are ongoing discussions about certifying servers with partially populated memory channels. However, the impact of memory configuration on SPEC SERT results has not been systematically studied. In this paper, we present a comprehensive analysis of how different memory configurations affect performance, power consumption, and energy efficiency on two state-of-the-art server systems using the SPEC SERT 2 suite. We vary both the number of populated channels and the type of DIMMs. Our experimental results show that server efficiency scores can be up to 3.4 times lower with only one channel populated and still up to 1.3 times lower with half the channels, compared to fully populated configurations. Detailed analysis of individual SPEC SERT 2 worklets reveals that some CPU worklets are highly sensitive to memory bandwidth, and that the impact of memory configuration is dependent on both server architecture and workload intensity. These findings underscore the need to reconsider certification criteria and highlight the importance of memory configuration for accurate energy efficiency assessment.

Transfer learning from pre-trained language models to encoder-decoder translation models faces a challenge due to the mismatch between the tasks of pre-training and fine-tuning. Pre-trained models are not explicitly trained to understand the semantic interactions between different languages. To address this issue, a cross-lingual embedding space is used as an interface during the pre-training phase. This approach enables the decoder inputs to attend to the encoder outputs, similar to the fine-tuning process. However, the effectiveness of this transfer heavily relies on the quality of the pre-trained unsupervised cross-lingual embeddings, which introduces complexity and reduces reproducibility. In this study, we propose a pre-training method called Cross-lingual Interaction Transfer (XLIT), which does not depend on other embedding techniques. XLIT effectively reconciles the task discrepancy in machine translation fine-tuning. We conducted extensive experiments involving four low-resource and six very low-resource translation directions. The results of our experiments demonstrate that our method surpasses randomly initialized models and previous pre-training techniques by up to 9.4 BLEU. Furthermore, we demonstrate that our method achieves comparable performance when pre-trained with large-scale monolingual data from various languages.

The process of creating test cases from requirements written in natural language (NL) requires intensive human efforts and can be tedious, repetitive, and error-prone. Thus, many studies have attempted to automate that process by utilizing Natural Language Processing (NLP) approaches. Furthermore, with the advent of massive language models and transfer learning techniques, people have introduced various advancements in NLP-assisted software testing with promising results. More notably, in recent years, not only have researchers been engrossed in solving the above task, but many companies have also embedded the feature to translate from human language to test cases their products. This paper presents an overview of NLP-assisted solutions being used in both the literature and the software testing industry.