In open-source software and platforms, developers utilize issues to record software failures or propose new features. The title of an issue, which is a mandatory field, should accurately describe the core content in a concise way. However, developers often face challenges in crafting high-quality issue titles due to insufficient experience or limited proficiency. As a result, researchers have proposed several methods for automatically generating issue titles, but typically relying on constructing large datasets to train models. Recently, Large Language Models (LLMs) have exhibited exceptional performance across a variety of general tasks, suggesting significant potential for issue title generation. Initial experiments indicate that the direct application of LLMs fails to yield satisfactory results. Therefore, we propose a method named LBITG (LLMs-Based Issue Title Generation). LBITG enhances the effectiveness of LLMs by providing contextual information through four types of prompts, which include example prompt and label prompt. These prompts serve as guidance for LLMs, thereby further improving their performance. Experimental results demonstrate that LBITG can significantly enhance the quality of issue titles generated by LLMs without any training. In the within-project scenario, LBITG achieves a minimum improvement of 111.29% in ROUGE, 104.54% in BLEU, and 188.48% in METEOR compared to iTAPE, and achieves performance comparable to that of the SOTA method iTiger. Moreover, LBITG demonstrates superior performance in the cross-project scenario, which outperforms iTiger by 25.33%, 30.14%, and 27.29% in terms of ROUGE-1, BLEU-1, and METEOR, respectively.

Smart contracts are programs running on the blockchain.In recent years, due to the continuous occurrence of smart contract security accidents, how to effectively detect vulnerabilities in smart contracts has received extensive attention.Machine learning-based vulnerability detection techniques have the advantage of not requiring expert rules.However, existing approaches have limitations in identifying vulnerabilities caused by version updates of smart contract compilers.In this paper, we propose OC-Detector, a smart contract vulnerabilities detection approach based on opcode instruction clustering.OC-Detector learns the characteristics of opcode instructions to cluster them and replaces opcode instructions belonging to the same cluster with the cluster number.After that, the similarity is calculated against the contract in the vulnerability database to identify vulnerabilities.Experimental results demonstrate that OC-Detector improves the F 1 value of detecting vulnerabilities from 0.04 to 0.40 compared to DC-Hunter, Securify, SmartCheck, and Osiris.Additionally, compared to DC-Hunter, F 1 value is improved by 0.27 when detecting vulnerabilities in smart contracts compiled by different version compilers.

High quality code comments are of great value for program maintenance. However, during the development process, developers often neglect to update corresponding comments when changing code. In such case, inconsistent comments are introduced which affect the maintainability of the software. In previous work, code changes are usually performed by treating the code as ordinary text and the structural information of the code are ignored. In this paper, we propose an approach named SICUP (Structural Information based Comment UPdater) to provide a new solution for comment updating tasks. SICUP uses the structural information of the code to help updating comments by constructing different sequences of ASTs. Experiments on a popular dataset demonstrates that SICUP outperforms CUP, which is an effective deep learning-based approach in terms of accuracy and recall.

Smart contracts are programs running on blockchain. In recent years, due to the persistent occurrence of security-related accidents in smart contracts, the effective detection of vulnerabilities in smart contracts has received extensive attention from researchers and engineers. Machine learning-based vulnerability detection techniques have the advantage that they do not need expert rules for determining vulnerabilities. However, existing approaches cannot identify vulnerabilities when the versions of smart contract compilers are updated. In this paper, we propose OC-Detector (Opcode Clustering Detector), a smart contract vulnerability detection approach based on clustering opcode instructions. OC-Detector learns the characteristics of opcode instructions to cluster them and replaces opcode instructions belonging to the same cluster with the ID of the cluster. After that, the similarity between the contract under analysis and contracts in the vulnerability database is calculated to identify vulnerabilities. The experimental results demonstrate that OC-Detector improves the F1 value of detecting vulnerabilities from 0.04 to 0.40 compared to DC-Hunter, Securify, SmartCheck and Osiris. Additionally, compared to DC-Hunter, the F1 value is improved by 0.27 when detecting vulnerabilities in smart contracts compiled by different versions of compilers.