Automated Static Warning Identification via Path-based Semantic Representation

Despite their ability to aid developers in detecting potential defects early in the software development life cycle, static analysis tools often suffer from precision issues (i.e., high false positive rates of reported alarms). To improve the availability of these tools, many automated warning identification techniques have been proposed to assist developers in classifying false positive alarms. However, existing approaches mainly focus on using hand-engineered features or statement-level abstract syntax tree token sequences to represent the defective code, failing to capture semantics from the reported alarms. To overcome the limitations of traditional approaches, this paper employs deep neural networks' powerful feature extraction and representation abilities to generate code semantics from control flow graph paths for warning identification. The control flow graph abstractly represents the execution process of a given program. Thus, the generated path sequences of the control flow graph can guide the deep neural networks to learn semantic information about the potential defect more accurately. In this paper, we fine-tune the pre-trained language model to encode the path sequences and capture the semantic representations for model building. Finally, this paper conducts extensive experiments on eight open-source projects to verify the effectiveness of the proposed approach by comparing it with the state-of-the-art baselines.Comment: 17 pages, in Chinese language, 9 figure

Containerships Sailing Speed and Fleet Deployment Optimization under a Time-Based Differentiated Freight Rate Strategy

This paper investigates the problem of containership sailing speed and fleet deployment optimization in an intercontinental liner shipping network. Under the consideration of the time value of container cargo, three kinds of impact of sailing speed changes on long legs of each liner route are analysed, and a time-based freight rate strategy is proposed. Then, the optimization problem is formulated as a mixed-integer nonlinear programming. Its goal is to maximize the total profits of a container liner shipping. To find the optimal solution to the model and improve the efficiency of model solution, a discretization algorithm is proposed. Numerical results verify the applicability of the proposed model and the efficiency of the algorithm. In addition, the time-based freight rate strategy is able to achieve more profit compared to a fixed freight rate strategy. Document type: Articl

SAGA: Summarization-Guided Assert Statement Generation

Generating meaningful assert statements is one of the key challenges in automated test case generation, which requires understanding the intended functionality of the tested code. Recently, deep learning-based models have shown promise in improving the performance of assert statement generation. However, existing models only rely on the test prefixes along with their corresponding focal methods, yet ignore the developer-written summarization. Based on our observations, the summarization contents usually express the intended program behavior or contain parameters that will appear directly in the assert statement. Such information will help existing models address their current inability to accurately predict assert statements. This paper presents a novel summarization-guided approach for automatically generating assert statements. To derive generic representations for natural language (i.e., summarization) and programming language (i.e., test prefixes and focal methods), we leverage a pre-trained language model as the reference architecture and fine-tune it on the task of assert statement generation. To the best of our knowledge, the proposed approach makes the first attempt to leverage the summarization of focal methods as the guidance for making the generated assert statements more accurate. We demonstrate the effectiveness of our approach on two real-world datasets when compared with state-of-the-art models.Comment: Preprint, to appear in the Journal of Computer Science and Technology (JCST

Optimization of ship speed and fleet deployment under carbon emissions policies for container shipping

In this paper, under the consideration of two carbon emissions policies, the issues of optimizing ship speed and fleet deployment for container shipping were addressed. A mixed-integer nonlinear programming model of ship speed and fleet deployment was established with the objective of minimising total weekly operating costs. A simulated annealing algorithm was proposed to solve the problem. An empirical analysis was conducted with the data selected from the benchmark suite. The applicability and effectiveness of the established model and its algorithm are verified by the results. According to the results, two policies of the cap-and-trade programme and the carbon tax can better optimize the results of the ship speed and fleet deployment problem to achieve the goal of reducing carbon emissions. The research remarks in this paper will provide a solution for container shipping companies to make optimized decisions under carbon emissions policies

Singlemode-Multimode-Singlemode Fiber Structures for Sensing Applications – A Review

A singlemode-multimode-singlemode (SMS) fiber structure consists of a short section of multimode fiber fusion-spliced between two SMS fibers. The mechanism underpinning the operation of an SMS fiber structure is multimode interference and associated self-imaging. SMS structures can be used in a variety of optical fiber systems but are most commonly used as sensors for a variety of parameters, ranging from macro-world measurands such as temperature, strain, vibration, flow rate, RI and humidity to the micro-world with measurands such as proteins, pathogens, DNA, and specific molecules. While traditional SMS structures employ a short section of standard multimode fiber, a large number of structures have been investigated and demonstrated over the last decade involving the replacement of the multimode fiber section with alternatives such as a hollow core fiber or a tapered fiber. The objective of replacing the multimode fiber has most often been to allow sensing of different measurands or to improve sensitivity. In this paper, several different categories of SMS fiber structures, including traditional SMS, modified SMS and tapered SMS fiber structures are discussed with some theoretical underpinning and reviews of a wide variety of sensing examples and recent advances. The paper then summarizes and compares the performances of a variety of sensors which have been published under a number of headings. The paper concludes by considering the challenges faced by SMS based sensing schemes in terms of their deployment in real world applications and discusses possible future developments of SMS fiber sensors

Trends in CO2 Emissions from China-Oriented International Marine Transportation Activities and Policy Implications

The demand for marine transportation and its associated CO2 emissions are growing rapidly as a result of increasing international trade and economic growth. An activity-based approach is developed for forecasting CO2 emissions from the China-oriented international seaborne trade sector. To accurately estimate the aggregated emissions, CO2 emissions are calculated individually for five categories of vessels: crude oil tanker, product tanker, chemical tanker, bulk carrier, and container. A business-as-usual (BAU) scenario was developed to describe the current situation without additional mitigation policies, whilst three alternative scenarios were developed to describe scenarios with various accelerated improvements of the key factors. The aggregated CO2 emissions are predicted to reach 419.97 Mt under the BAU scenario, and 258.47 Mt under the optimal case, AD3. These predictions are 4.5 times and 2.8 times that of the aggregated emissions in 2007. Our analysis suggests that regulations for monitoring, reporting, and verifying the activities of vessels should be proposed, in order to quantify the CO2 emissions of marine transportation activities in Chinese territorial waters. In the long-term future, mitigation policies should be employed to reduce CO2 emissions from the marine trade sector and to address the climatic impact of shipping

Neural Program Repair with Program Dependence Analysis and Effective Filter Mechanism

Automated program repair is a crucial task for improving the efficiency of software developers. Recently, neural-based techniques have demonstrated significant promise in generating correct patches for buggy code snippets. However, most existing approaches arbitrarily treat the buggy context without any analysis to capture the semantic relationship between the buggy statement and its context. Additionally, we observe that existing neural models may output an unaltered patch consistent with the input buggy code snippet, which fails to be the correct human-written one for fixing the given bug. To address the aforementioned limitations, we present in this paper a novel neural program repair framework called \approach, which adapts the general pre-trained language model for fixing single-line Java bugs. We make the first attempt to use program slicing to extract contextual information directly related to the given buggy statement as repair ingredients from the corresponding program dependence graph and eliminate unaltered patches using an intuitive but effective filter mechanism. We demonstrate the effectiveness of \approach on five benchmarks when compared with state-of-the-art baselines.Comment: 12 pages, 7 figure