API recommendation systems for novice programmers should prioritize usability and inspiration rather than merely pursuing the “best result”. Existing retrieval-based approaches, whether relying on direct similarity matching or using query expansion to generate clarification options, still cannot recover missing task semantics and often introduce additional ambiguity and interaction overhead. Learning-based methods, including neural architectures and recent LLM-driven techniques, require substantial data or strong prompt dependence and provide limited transparency, making it difficult to align model outputs with novice programmers' actual intent. To address these limitations, we propose IOCAPI (Intention-Oriented andContext-AwareAPIRecommendation), a reasoning-driven framework that integrates LLMs, LCMs, and in-context learning. It mainly contains three components: (1) Intent Detector, which refines the query and derives task semantics through model-generated I/O exemplars; (2) Code Generator, which produces representative code snippets under the confirmed I/O constraints; and (3) Task Bridger, which consolidates the results into actionable API recommendations with interpretable code examples. Evaluations on three public datasets show that IOCAPI attains a 35.7% BLEU improvement over APIGen and an 11.1% MRR gain over CLEAR, and achieves higher MAP scores than GPT-4 zero-shot, few-shot, and chain-of-thought baselines by 102%, 11.2%, and 21.8%, respectively. A controlled user study involving seven real programming tasks further provides empirical observations of IOCAPI's behavior in practice. Compared with KAHAID, IOCAPI obtained higher average scores in Correctness (1.67 vs. 1.00), Usability (1.76 vs. 0.40), and Inspiration (1.40 vs. 0.26).

Software vendors often silently release security patches without providing sufficient advisories (e.g., Common Vulnerabilities and Exposures) or delayed updates via resources (e.g., National Vulnerability Database). Therefore, it has become crucial to detect these security patches to ensure secure software maintenance. However, existing methods face the following challenges: (1) They primarily focus on the information within the patches themselves, overlooking the complex dependencies in the repository. (2) Security patches typically involve multiple functions and files, increasing the difficulty in well learning the representations. To alleviate the above challenges, this paper proposes a Repository-level Security Patch Detection framework named RepoSPD, which comprises three key components: 1) a repository-level graph construction, RepoCPG, which represents software patches by merging pre-patch and post-patch source code at the repository level; 2) a structure-aware patch representation, which fuses the graph and sequence branch and aims at comprehending the relationship among multiple code changes; 3) progressive learning, which facilitates the model in balancing semantic and structural information. To evaluate RepoSPD, we employ two widely-used datasets in security patch detection: SPI-DB and PatchDB. We further extend these datasets to the repository level, incorporating a total of 20,238 and$\mathbf{2 8, 7 8 1}$versions of repository in C/C++ programming languages, respectively, denoted as SPI-DB* and PatchDB*. We compare RepoSPD with six existing security patch detection methods and five static tools. Our experimental results demonstrate that RepoSPD outperforms the state-of-the-art baseline, with improvements of 11.90 %, and 3.10 % in terms of accuracy on the two datasets, respectively. These results underscore the effectiveness of RepoSPD in detecting security patches. Furthermore, RepoSPD can detect 151 security patches, which outperforms the best-performing baseline by$\mathbf{2 1. 3 6 \%}$with respect to accuracy.

Automatic API method recommendation is an essential task of code intelligence, which aims to suggest suitable APIs for programming queries. Existing approaches can be categorized into two primary groups: retrieval-based and learning-based approaches. Although these approaches have achieved remarkable success, they still come with notable limitations. The retrieval-based approaches rely on the text representation capabilities of embedding models, while the learning-based approaches require extensive task-specific labeled data for training. To mitigate the limitations, we propose APIGen, a generative API recommendation approach through enhanced in-context learning (ICL). APIGen has a powerful representation capability and can make effective recommendations with only a few examples via I CL. To overcome the limitations of standard ICL in capturing task-specific knowledge, APIGen involves two main components: (1) Diverse Examples Selection. APIGen searches for similar posts to the programming queries from the lexical, syntactical, and semantic perspectives, providing more informative examples for ICL. (2) Guided API Recommendation. APIGen enables large language models (LLMs) to perform reasoning before generating API recommendations, where the reasoning involves fine-grained matching between the task intent behind the queries and the factual knowledge of the APIs. With the reasoning process, APIGen makes recommended APIs better meet the programming requirement of queries and also enhances the interpretability of results. We compare APIGen with four existing approaches on two publicly available benchmarks. Experiments show that APIGen outperforms the best baseline CLEAR by 105.8% in method-level API recommendation and 54.3 % in class-level API recommendation in terms of SuccessRate@l. Besides, APIGen achieves an average 49.87 % increase compared to the zero-shot performance of popular LLMs such as GPT-4 in method-level API recommendation regardina the SuccessRate@ 3 metric.

During software development, programmers often rely on a wide range of application programming interfaces (APIs) to facilitate their tasks. However, APIs have been growing rapidly in recent years, making it difficult for developers to choose among the many APIs that suit their programming needs. To facilitate the development process, automatic API recommendation is becoming increasingly important. Although there have been many effective research methods, these methods have a high dependence on the accuracy of the user's description of his own task, and there is a knowledge difference between the user's query and the user's actual task, increasing the difficulty of accurate API recommendation. In this article, we propose REAPI, a method to bridge the knowledge gap between the user's query and the user's actual task to improve the recommendation accuracy. The REAPI approach involves reconstructing query by tapping into Stack Overflow data to glean user intentions. Refactoring the user's query to display implicit information can better capture the user's true intentions. Specifically, we generate three candidate reconstruction statements based on natural language queries and Stack Overflow data and incorporate user feedback to refine and select the final statement. To evaluate the effectiveness of REAPI, we conducted experiments at both the class-level and method-level. Our results show that REAPI outperforms state-of-the-art baselines across key evaluation metrics such as S@1, S@3, S@10, MRR, and MAP.

The collaborative filtering recommendation technique (CFR) is one of the techniques used in recommended systems, in which the most proximal neighbours to a target user are selected. Their profiles are used to predict rating for items as yet unrated by that target user. However, malicious users inject fake user profiles to destroy the security and reliability of the recommender systems, which is called shilling attacks. Therefore, it is crucial to improve the recommendation technique against shilling attacks. Malicious users use a single method to perform shilling attacks. Intuitively, fusing multiple criteria to construct CFR can effectively resist shilling attacks. A novel CFR is proposed against shilling attacks (called CFR-F). In our approach, a similar interest users’ resource set is obtained first by integrating users’ dynamic interest model and social tags. Then, a similar interest user resource set is selected according to a strategy that selects preference influence weight based on user background. Our experimental results show that our approach can recommend accurate information resources and has a lower Mean Absolute Error (MAE) and Average Prediction Shift (APS) than traditional techniques by 50% and 20%, respectively.

Emerging topics in app reviews highlight the topics (e.g., software bugs) with which users are concerned during certain periods. Identifying emerging topics accurately, and in a timely manner, could help developers more effectively update apps. Methods for identifying emerging topics in app reviews based on topic models or clustering methods have been proposed in the literature. However, the accuracy of emerging topic identification is reduced because reviews are short in length and offer limited information. To solve this problem, an improved emerging topic identification (IETI) approach is proposed in this work. Specifically, we adopt natural language processing techniques to reduce noisy data, and identify emerging topics in app reviews using the adaptive online biterm topic model. Then we interpret the implicature of emerging topics through relevant phrases and sentences. We adopt the official app changelogs as ground truth, and evaluate IETI in six common apps. The experimental results indicate that IETI is more accurate than the baseline in identifying emerging topics, with improvements in the F1 score of 0.126 for phrase labels and 0.061 for sentence labels. Finally, we release the codes of IETI on Github ( https://github.com/wanizhou/IETI ).

Python is an interpreted programming language that has been widely used in many fields. The successful execution of a Python program depends on both the correctness of Python program and the correctness of Python interpreter. As an infrastructure software, there are many bugs in the Python interpreter. Exploring the bugs in Python interpreters can help developers and maintainers of Python interpreters detect and fix bugs and help users of Python avoid risks. In this article, we conduct an empirical study on the bugs in two mainstream Python interpreters: CPython and PyPy. By analyzing 25 958 fixed bugs, 18 824 revisions, 2 116 test cases, and root causes of randomly sampled 510 bugs, we have summarized the following findings.1)The distribution of bugs in the Python interpreter is so uneven that the vast majority of bugs are distributed in a few components and source files.2)The scales of the testing programs that reveal bugs are small.3)The fixing works seem to be not complicated since the number of modified source files and lines of code are limited; however, most bugs need a long time to be fixed; nearly 15% of the bugs need more than one year to fix.4)The priorities of bugs are independent of their locations, but they significantly correlate with duration of bugs.5)Semantic bugs are the most frequent root causes of bugs, and their proportion exceeds other types of root causes.These results could indicate some potential problems during the detecting and fixing of Python interpreter’s bugs, and provide some assistance to developers and maintainers of Python interpreters, users of Python, as well as researchers in related fields.

In this paper, we present a novel low-rank matrix factorization algorithm with adaptive graph regularizer (LMFAGR). We extend the recently proposed low-rank matrix with manifold regularization (MMF) method with an adaptive regularizer. Different from MMF, which constructs an affinity graph in advance, LMFAGR can simultaneously seek graph weight matrix and low-dimensional representations of data. That is, graph construction and low-rank matrix factorization are incorporated into a unified framework, which results in an automatically updated graph rather than a predefined one. The experimental results on some data sets demonstrate that the proposed algorithm outperforms the state-of-the-art low-rank matrix factorization methods.

Complete neighbourhood preserving embedding (CNPE) is a recently proposed approach to overcome the drawbacks of neighbourhood preserving embedding (NPE) which is difficult to directly apply to face recognition because of computational complexity. However, there are still disadvantages for CNPE: (i) CNPE is time‐consuming when N is large, here N is the sample size; (ii) the solutions of CNPE may suffer from the degenerate eigenvalue problem, that is, several eigenvectors with the same maximal eigenvalue, which make them not optimal in terms of the discriminant ability. In this study, the authors proposed a new approach, namely improved complete neighbourhood preserving (ICNPE), to address the drawbacks of CNPE. ICNPE is more efficient than CNPE and can overcome the degenerate eigenvalue problem of CNPE. Experiments on the Olivetti & Oracle Research Laboratory (ORL), Yale, PIE (pose, illumination and expression) and Alex Martinez and Robert Benavente (AR) face databases show the effectiveness of the proposed ICNPE.