The rapid expansion of online programming exercise platforms has brought abundant learning resources for programming education, but also presents challenges for personalized exercise recommendation due to missing or imprecise knowledge annotations and the sparsity of learner-exercise interaction records, which together lead to reduced accuracy and a lack of explainability in the recommendation results. Existing natural language processing and large language models (LLM)-based annotation methods struggle to capture implicit knowledge and often generate redundant or inconsistent results. Moreover, mainstream recommendation systems are ineffective at handling the severe sparsity of interaction sequences in programming exercise datasets, and typically model student preferences in a single dimension—overlooking key educational factors such as knowledge gaps, difficulty tolerance, and preferred learning rhythm. To address these challenges, we propose KDG-Rec, a novel framework that first introduces AgentCo-KAS, a multiagent LLM-based collaborative annotation method with role-specific fine-tuning and cross-agent verification for high-precision, fine-grained knowledge annotation. Building on these enriched annotations, we develop a disentangled graph neural network model that constructs dual exercise-interaction graphs to effectively capture learning patterns at multiple granularities in interaction sequences and explicitly decouples student preferences into four interpretable dimensions for adaptive fusion. Extensive experiments on real-world datasets demonstrate that KDG-Rec outperforms nine state-of-the-art methods across multiple metrics, significantly advancing personalized programming exercise recommendation.

In the realm of contemporary educational data mining, aspect-based sentiment analysis plays a crucial role in deciphering students’ nuanced perceptions of MOOC courses. However, sentiment analysis in educational context often encounters the prevalent challenge of cold start issues. This paper proposes a novel methodology for aspect-level sentiment analysis of course reviews, beginning with the identification of critical aspects in course reviews, followed by a comprehensive sentiment analysis at the aspect level. We introduce a Dual-Track Sentiment Analysis model (DTSA), which dynamically integrates two analytical tracks: one utilizing fine-tuned BERT model and the other employing sentiment dictionaries to effectively mitigate the cold start problem. Experimental results demonstrate the superiority of our approach over baseline models in various key metrics, particularly in addressing cold start challenges with limited review data. By incorporating a matching strategy, our model ensures reliable and timely sentiment analysis of course reviews, even with small amount of course reviews. This methodology effectively alleviates the cold start problem in aspect-level sentiment analysis in educational evaluation text, providing accurate insights when lacking sufficient initial learners’ review data and enhancing the robustness of MOOC course evaluation processes.

Implementing test cases as programs to automate test execution is a popular testing practice. Current industrial practices usually use test functions to implement the test steps of a test case and then to compose the executable test case by choosing the test functions to call manually. It is time-consuming and could lead to invalid test results by selecting inappropriate test functions. In this article, we propose an automatic test function recommendation approach named Scenario-based Recommendation of TEst Function (SRTEF). Given a test step of a test case, SRTEF uses the weighted description similarity and the scenario similarity to recommend test functions. The description similarity utilizes the deep structured semantic model (DSSM) to measure the relatedness between a test step and a test function by their literal descriptions. The test scenario and the test function usage scenario are considered to calculate the scenario similarity. SRTEF has been successfully applied in Huawei. The systematic experiments have been conducted to evaluate SRTEF by using the dataset from Huawei and comparing with BiInformation source-based KnowledgE Recommendation (BIKER), reported as the best approach so far. The results show that SRTEF outperforms BIKER with significant positive ratios consistently in all the three selection strategies, i.e., Top-3, Top-5, and Top-10. The DSSM shows its advantage over word embedding by the double performance of capturing the semantic relatedness in SRTEF.

Implementing test cases to automate test execution is a popular testing practice currently. A stepwise test case consists of several sequential test steps. Given a test function library, the typical way to implement a test case is calling the existing test functions in the library to reduce test cost. How to find the appropriate test function(s) to implement a test step in a given test case thus becomes an important problem. However, in current testing practices, test engineers usually select the appropriate test function manually by experience. It is time-consuming and could lead to invalid test results by selecting inappropriate or wrong test functions to call. In this paper, we propose an automatic test function recommendation approach with scenario named SRTEF (Scenario-based Recommendation of TEst Function). Given a test step, SRTEF uses two levels of similarities to recommend test functions, description similarity and scenario similarity. The description similarity measures the semantic relatedness between the test step and test function by their literal descriptions. To calculate the scenario similarity, SRTEF at first retrieves a set of historical test cases that contains test step(s) semantically similar to the given test step; then the scenario similarity between test step and test function is calculated according to the calling relation between retrieved test case and test function, and the co-occurrence relation among test functions. SRTEF has been successfully applied in Huawei. We evaluate SRTEF by using the dataset from Huawei and comparing with BIKER, reported as the best recommendation approach so far. The results show that SRTEF outperforms the BIKER approach by at least 49% in Mean Average Precision, 33% in Mean Reciprocal Rank, and 25% in Mean Recall.

Software traceability recovery, which reconstructs links between software artifacts, has become more and more vital to maintaining a software life cycle with the increase of software scale and complexity of software architecture. However, existing approaches mainly rely on information retrieval (IR) techniques. These methods are not very efficient at complex software artifacts which are mixed with multilingual texts, code snippets and proper nouns. Moreover, it is hard to predict new traceability links with existing approaches when requirements are changed or software functions are added, since these methods have not made the most of the final ranked lists. In this paper, we propose a novel approach WELR, based on word embeddings and learning to rank to recover traceability links. We use word embeddings to calculate semantic similarities between software artifacts and bring in query expansion and a weighting strategy during calculation. Different from other work, we leverage learning to rank to build prediction models for traceability links. We conducted experiments on five public datasets and took account of traceability links among different kinds of software artifacts. The results show that our method outperforms the state-of-the-art method that works under the same conditions.

User-generated reviews on the e-commerce site reflect consumers' sentiment about products, which can further direct consumers' purchasing behaviors and sellers' marketing strategies. In this paper, we propose a semi-supervised approach to mine the aspects of product discussed in Chinese online reviews and also the sentiments expressed in different aspects. We first apply the Latent Dirichlet Allocation model to discover multiaspect global topics of the product reviews, then extract the opinion short sentences based on sliding windows and pattern matching from context over the review text. The polarity of the associated sentiment is classified by the domain lexicon-based method. Finally the results are collected as features for the feedback for the machine learning method and applied in semantic orientation classification. The experiment results show that the novel method we proposed could help to discover multi-aspect fine-grained topics and associated sentiment, which helps to improve semantic orientation classification simultaneously.