ContrastRepair: Enhancing Conversation-Based Automated Program Repair via Contrastive Test Case Pairs

Automated Program Repair (APR) aims to automatically generate patches for rectifying software bugs. Recent strides in Large Language Models (LLM), such as ChatGPT, have yielded encouraging outcomes in APR, especially within the conversation-driven APR framework. Nevertheless, the efficacy of conversation-driven APR is contingent on the quality of the feedback information. In this paper, we propose ContrastRepair, a novel conversation-based APR approach that augments conversation-driven APR by providing LLMs with contrastive test pairs. A test pair consists of a failing test and a passing test, which offer contrastive feedback to the LLM. Our key insight is to minimize the difference between the generated passing test and the given failing test, which can better isolate the root causes of bugs. By providing informative and specific feedback, ContrastRepair enables the LLM to produce effective bug fixes. The implementation of ContrastRepair is based on the state-of-the-art LLM, ChatGPT, and it iteratively interacts with ChatGPT until plausible patches are generated. We evaluate ContrastRepair on multiple benchmark datasets, including Defects4j, QuixBugs, and HumanEval-Java. The results demonstrate that ContrastRepair significantly outperforms existing methods, achieving a new state-of-the-art in program repair. For instance, among Defects4j 1.2 and 2.0, ContrastRepair correctly repairs 143 out of all 337 bug cases, while the best-performing baseline fixes 124 bugs

Quantitative Analysis of Leakage Consequences of LNG Ship-to-Ship Bunkering Based on CFD

Leakage incidents on LNG bunker vessels will result in a serious degree of hazard. This paper investigates typical high-risk scenarios such as hose ruptures and valve joint leakages. The consequences of an LNG leakage accident during the simultaneous operation of a bunker vessel and a container carrier are simulated using the FLACS software, and the dispersion range of the combustible vapor cloud is quantitatively analyzed under both ballast and laden conditions. Under the ballast condition, the diffusion range of the combustible vapor cloud on the side of the bunker vessel is 58 × 15.5 m from the front wall of the cargo equipment room to the bow of the vessel, and 35 × 9.5 m between the cargo equipment room and the transom of the vessel. Under the laden condition, the diffusion range on the side of the bunker vessel is 58 × 15.5 m from the front wall of the cargo equipment room to the bow of the vessel, and 15 × 4 m between the rear end wall of the cargo equipment room and the front wall of the stern of the vessel. These results provide important technical guidance and reference values for the safe operation of LNG bunker vessels