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After software or program updates, it is crucial to establish a new set of test cases. Reusing parts of the old test case set in unit testing is a cost-effective, efficient, and common approach. However, only a few commercial software are utilized for this purpose, and their techniques for reusing test cases are not publicly available. PC-TRT is a test case reuse tool primarily designed for software and programs written in the C language. PC-TRT reuses test cases from historical program versions and generates test data for uncovered paths, resulting in a high path coverage test case set. Its key functions include analyzing test case path coverage information, selecting reusable cases from old test case sets based on path similarity, and generating test data for uncovered paths. PC-TRT significantly improves both the efficiency and reliability of software testing.

Acute coronary syndrome (ACS) is a predominant cause of mortality, and the prompt and precise identification of this condition is crucial to minimize its impact. Recent research indicates that gut microbiota is associated with the onset, progression, and treatment of ACS. To investigate its role, we sequenced the gut microbiota of 38 ACS patients before and after percutaneous coronary intervention and statin therapy at three time points, examining differential species and metabolic pathways. We observed a decrease in the abundance of Parabacteroides, Escherichia, and Blautia in patients after treatment and an increase in the abundance of Gemalla, Klebsiella variicola, Klebsiella pneumoniae, and others. Two pathways related to sugar degradation were more abundant in patients before treatment, possibly correlated with disorders of sugar metabolism and risk factors, such as hyperglycemia, insulin resistance, and insufficient insulin secretion. Additionally, seven pathways related to the biosynthesis of vitamin K2 and its homolog were reduced after treatment, suggesting that ACS patients may gradually recover after therapy. The gut microbiota of patients treated with different statins exhibited notable differences after treatment. Rosuvastatin appeared to promote the growth of anti-inflammatory bacteria while reducing pro-inflammatory bacteria, whereas atorvastatin may have mixed effects on pro-inflammatory and anti-inflammatory bacteria while increasing the abundance of Bacteroides. Our research will provide valuable insights and enhance comprehension of ACS, leading to better patient diagnosis and therapy.

In recent years, replacing internal combustion engine vehicles with electric vehicles has been a significant option for supporting reducing carbon emissions because of fossil fuel shortage and environmental contamination. However, the rapid growth of electric vehicles (EVs) can bring new and uncertain load conditions to the electric network. Precise load forecasting for EV charging stations becomes vital to reduce the negative influence on the grid. To this end, a novel day-ahead load forecasting method is proposed to forecast loads of EV charging stations with Bayesian deep learning techniques. The proposed methodological framework applies long short-term memory (LSTM) network combined with Bayesian probability theory to capture uncertainty in forecasting. Based on the actual operational data of the EV charging station collected on the Caltech campus, the experiment results show the superior performance of the proposed method compared with other methods, indicating significant potential for practical applications.

In software testing, the quality of test cases is crucial, butmanual generation is time-consuming. Various automatic test case generation methods exist, requiring careful selection based on program features. Current evaluation methods compare a limited set of metrics, which does not support a larger number of metrics or consider the relative importance of each metric to the final assessment. To address this, we propose an evaluation tool, the Test Case Generation Evaluator (TCGE), based on the learning to rank (L2R) algorithm. Unlike previous approaches, our method comprehensively evaluates algorithms by considering multiple metrics, resulting in a more reasoned assessment. The main principle of the TCGE is the formation of feature vectors that are of concern by the tester. Through training, the feature vectors are sorted to generate a list, with the order of the methods on the list determined according to their effectiveness on the tested assembly. We implement TCGE using three L2R algorithms: Listnet, LambdaMART, and RFLambdaMART. Evaluation employs a dataset with features of classical test case generation algorithms and three metrics--Normalized Discounted Cumulative Gain (NDCG), Mean Average Precision (MAP), and Mean Reciprocal Rank (MRR). Results demonstrate the TCGE's superior effectiveness in evaluating test case generation algorithms compared to other methods. Among the three L2R algorithms, RFLambdaMART proves the most effective, achieving an accuracy above 96.5%, surpassing LambdaMART by 2% and Listnet by 1.5%. Consequently, the TCGE framework exhibits significant application value in the evaluation of test case generation algorithms. [ABSTRACT FROM AUTHOR]