Automatically recommending relevant law articles to a given legal case has attracted much attention as it can greatly release human labor from searching over the large database of laws. However, current researches only support coarse-grained recommendation where all relevant articles are predicted as a whole without explaining which specific fact each article is relevant with. Since one case can be formed of many supporting facts, traversing over them to verify the correctness of recommendation results can be time-consuming. We believe that learning fine-grained correspondence between each single fact and law articles is crucial for an accurate and trustworthy AI system. With this motivation, we perform a pioneering study and create a corpus with manually annotated fact-article correspondences. We treat the learning as a text matching task and propose a multi-level matching network to address it. To help the model better digest the content of law articles, we parse articles in form of premise-conclusion pairs with random forest. Experiments show that the parsed form yielded better performance and the resulting model surpassed other popular text matching baselines. Furthermore, we compare with previous researches and find that establishing the fine-grained fact-article correspondences can improve the recommendation accuracy by a large margin. Our best system reaches an F1 score of 96.3%, making it of great potential for practical use. It can also significantly boost the downstream task of legal decision prediction, increasing the F1 score by up to 12.7%. Code and dataset are available at https://github.com/gjdnju/MLMN .

Recent years have seen the remarkable capabilities of large language models (LLMs) for code generation. Different from existing work that evaluate the correctness of the code generated by LLMs, we propose to further evaluate its efficiency. More efficient code can lead to higher performance and execution efficiency of programs and software completed by LLM-assisted programming. First, we evaluate the efficiency of the code generated by LLMs on two benchmarks, HumanEval and MBPP. Then, we choose a set of programming problems from the online judge platform LeetCode to conduct a more difficult evaluation. Finally, we explore several prompts that would enable LLMs to generate more efficient code.

Bug localization is a crucial aspect of software maintenance, running through the entire software lifecycle. Information retrieval-based bug localization (IRBL) identifies buggy code based on bug reports, expediting the bug resolution process for developers. Recent years have witnessed significant achievements in IRBL, propelled by the widespread adoption of deep learning (DL). To provide a comprehensive overview of the current state of the art and delve into key issues, we conduct a survey encompassing 61 IRBL studies leveraging DL. We summarize best practices in each phase of the IRBL workflow, undertake a meta-analysis of prior studies, and suggest future research directions. This exploration aims to guide further advancements in the field, fostering a deeper understanding and refining practices for effective bug localization. Our study suggests that the integration of DL in IRBL enhances the model’s capacity to extract semantic and syntactic information from both bug reports and source code, addressing issues such as lexical gaps, neglect of code structure information, and cold-start problems. Future research avenues for IRBL encompass exploring diversity in programming languages, adopting fine-grained granularity, and focusing on real-world applications. Most importantly, although some studies have started using large language models for IRBL, there is still a need for more in-depth exploration and thorough investigation in this area.

Recommending APIs is a practical and essential feature of IDEs. Improving the accuracy of API recommendations is an effective way to improve coding efficiency. With the success of deep learning in software engineering, the state-of-the-art (SOTA) performance of API recommendation is also achieved by deep-learning-based approaches. However, existing SOTAs either only consider the API sequences in the code snippets or rely on complex operations for extracting hand-crafted features, all of which have potential risks in under-encoding the input code snippets and further resulting in sub-optimal recommendation performance. To this end, this article proposes to utilize the code understanding ability of existing general code P re- T raining M odels to fully encode the input code snippet to improve the accuracy of API Rec ommendation, namely, PTM-APIRec . To ensure that the code semantics of the input are fully understood and the API recommended actually exists, we use separate vocabularies for the input code snippet and the APIs to be predicted. The experimental results on the JDK and Android datasets show that PTM-APIRec surpasses existing approaches. Besides, an effective way to improve the performance of PTM-APIRec is to enhance the pre-trained model with more pre-training data (which is easier to obtain than API recommendation datasets).

Most automated program repair methods rely on test cases to determine the correctness of the generated patches. However, due to the incompleteness of available test suites, some patches that pass all the test cases may still be incorrect. This issue is known as the patch overfitting problem. Overfitting problem is a longstanding problem in automated program repair. Due to overfitting patches, the patches obtained by automated program repair tools require further validation to determine their correctness. Researchers have proposed many methods to automatically assess the correctness of patches, but no systematic review provides a detailed introduction to this problem, the existing solutions, and the challenges. To address this deficiency, we systematically review the existing approaches to patch correctness assessment. We first offer a few examples of overfitting patches to acquire a more detailed understanding of this problem. We then propose a comprehensive categorization of publicly available techniques and datasets, examine the commonly used evaluation metrics, and perform an in-depth analysis of the effectiveness of the existing models in addressing the challenge of overfitting. Based on our analysis, we provided the difficulties encountered by current methodologies, alongside the possible avenues for future research exploration.

Recent years have seen a rapidly growing number of online reviews of products. As a result, it is often not possible for customers to go through each review before making purchase decisions. One way to address this problem is to build a system for automatically addressing the helpfulness of reviews and present only those reviews that are determined to be helpful by the system to an end user. The vast majority of existing approaches to the task of review helpfulness prediction are based on hand-crafted features, thus making system performance heavily dependent on the quality of these features. In light of this weakness, we propose a new model of review helpfulness prediction using a combination of Convolutional Neural Network (CNN) and TransE wherein hand-crafted features can also be incorporated to improve the output. Specifically, CNN enables us to learn the semantic information from a review and TransE is used to capture the relationship between different entities mentioned in the review. Experiments on the Amazon product review datasets demonstrate that our approach significantly outperforms the state of the art.