Generating a class integration test order (CITO) is essential to reduce the overhead of test stub construction (the primary cost in integration testing) and to ensure system reliability in complex software systems. Although reinforcement learning (RL) has shown promise in automating CITO generation, existing methods suffer from unstable policy learning and limited robustness against structural perturbations and defect injection. These challenges stem from insufficient reward shaping and the lack of reliable oracles for validation. To address these limitations, we propose LM-CITO, a stability-aware RL framework that integrates Lyapunov-guided reward shaping with semantic validation through metamorphic testing (MT). Specifically, we design a Lyapunov energy function over class dependency graphs to promote monotonic structural convergence during training, and define metamorphic relations (MRs) to verify behavioral consistency under controlled perturbations. Extensive experiments on six real-world systems demonstrate that LM-CITO consistently produces more effective policies, yielding CITOs with significantly reduced stubbing costs compared to baseline models. Furthermore, MT verifies the capability of our MRs to detect defects in 19 injected bug variants, confirming the robustness of LM-CITO under various fault-induced perturbations. These results highlight the synergy of stability guidance and MR-based validation, offering an effective, principled solution for oracle-free RL in software testing.

Graph Neural Networks (GNNs) have effectively improved the performance of Cognitive Diagnosis Models (CDMs). Existing works have proposed a series of Graph-based Cognitive Diagnosis Frameworks (GCDFs) to enhance robustness to noise. However, these robust designs are often general methods for GNNs and are not designed for cognitive diagnosis, which undermines real cognitive information during the denoising process. Interestingly, a noteworthy phenomenon has been overlooked: even without robustness designs, GCDFs can still learn correct information in noisy environments. In this paper, we conduct a comprehensive empirical analysis of this issue. We found that noise primarily accumulates in lower singular components. Even in noisy environments, the principal subspaces of representations still remain stable. Based on these findings, we propose a Noise-aware Cognitive Diagnostic framework based on Low-rank Alignment, named NCDLA. The framework first performs low-rank reconstruction of the interaction matrix between students and exercises, retaining only larger singular values to achieve noise reduction. Then, the reconstructed interaction matrix and the original interaction matrix are combined with the Q matrix to form a noise-reduced heterogeneous graph and an original heterogeneous graph. In order to distinguish between the interaction patterns of correct and incorrect responses, we decompose the heterogeneous graph according to the type of response. NCDLA achieves denoising of student representations and exercises representations through a self-supervised strategy based on low-rank reconstruction and a spectral anchor regularisation method. Extensive experiments on three datasets demonstrate that NCDLA achieves optimal prediction performance and robustness.

Dynamic reasoning trees can help large language models solve complex tasks by explicitly structuring intermediate decisions.However, existing approaches often rely on manually specified subproblems or predefined decomposition patterns, which limits the effectiveness of reasoning and generalization.To solve this problem, we propose SyRA, a hierarchical reasoning framework based on MFR theory that supports the construction of adaptive reasoning trees and reliable error correction within a single LLM.Specifically, SyRA focuses on reasoning-tree construction, dynamically controlling branching and expansion using MFR principles to enable informative, non-redundant subproblem decomposition.In addition, it introduces a residual backtracking mechanism for adaptive cross-layer error correction, allowing the model to revise earlier reasoning decisions based on downstream feedback.Across eight reasoning benchmarks, SyRA significantly reduces logical errors and improves reasoning accuracy, while achieving a better balance between accuracy and reasoning time than the Chain-of-Thought, Decompose-Analyze-Rethink and Tree-of-Thought.Our

Cognitive diagnostics in the Web-based Intelligent Education System (WIES) aims to assess students' mastery of knowledge concepts from heterogeneous, noisy interactions. Recent work has tried to utilize Large Language Models (LLMs) for cognitive diagnosis, yet LLMs struggle with structured data and are prone to noise-induced misjudgments. Specially, WIES's open environment continuously attracts new students and produces vast amounts of response logs, exacerbating the data imbalance and noise issues inherent in traditional educational systems. To address these challenges, we propose DLLM, a Diffusion-based LLM framework for noise-robust cognitive diagnosis. DLLM first constructs independent subgraphs based on response correctness, then applies relation augmentation alignment module to mitigate data imbalance. The two subgraph representations are then fused and aligned with LLM-derived, semantically augmented representations. Importantly, before each alignment step, DLLM employs a two-stage denoising diffusion module to eliminate intrinsic noise while assisting structural representation alignment. Specifically, unconditional denoising diffusion first removes erroneous information, followed by conditional denoising diffusion based on graph signal to eliminate misleading information. Finally, the noise-robust representation that integrates semantic knowledge and structural information is fed into existing cognitive diagnosis models for prediction. Experimental results on three publicly available web-based educational platform datasets demonstrate that our DLLM achieves optimal predictive performance across varying noise levels, which demonstrates that DLLM achieves noise robustness while effectively leveraging semantic knowledge from LLM.

Traditional pedestrian trajectory prediction models focus on spatio–temporal data without proper consideration of individual interactions with the environment, mutual interactions, and contextual information, resulting in low prediction performance in real applications. In this article, we propose a new pedestrian trajectory prediction model called spatial social force graph neural network (SSF-GNN). First, SSF-GNN adopts a gate recurrent unit (GRU) network and a CenterNet network to capture pedestrian trajectory features and environmental features from historical trajectory sequences. Particularly, SSF-GNN can quantify pedestrian interactions and context-awareness information based on social force. Second, SSF-GNN employs a graph neural network to integrate social influence and hidden states of pedestrians. The distance between adjacent trajectory points is approximated by the weighted average summation of pedestrian historical trajectories. Third, SSF-GNN employs a new interaction function between pedestrians by considering the distance between pedestrians, as well as the movement speed of pedestrians in the social force model, to accurately predict trajectories of pedestrians. Extensive experiments are conducted on two famous datasets, and the results demonstrate SSF-GNN’s outperforms the state-of-the-art models, where average displacement error (ADE) is reduced by more than 25.6%, and final displacement error (FDE) is reduced by more than 15.4%. When predicting a pedestrian’s trajectory in the next eight frames of locations, SSF-GNN outperforms other models significantly with an accuracy of 69.71%.

With the widespread use of Graph Neural Networks (GNNs) for representation learning from network data, the fairness of GNN models has raised great attention lately. Fair GNNs aim to ensure that node representations can be accurately classified, but not easily associated with a specific group. Existing advanced approaches essentially enhance the generalisation of node representation in combination with data augmentation strategy and do not directly impose constraints on the fairness of GNNs. In this work, we identify that a fundamental reason for the unfairness of GNNs is the phenomenon of social homophily , i.e., users in the same group are more inclined to congregate. The message-passing mechanism of GNNs can cause users in the same group to have similar representations due to social homophily, leading model predictions to establish spurious correlations with sensitive attributes. Inspired by this reason, we propose a method called Equity-Aware GNN (EAGNN) towards fair graph representation learning. Specifically, to ensure that model predictions are independent of sensitive attributes while maintaining prediction performance, we introduce constraints for fair representation learning based on three principles: sufficiency, independence and separation. We theoretically demonstrate that our EAGNN method can effectively achieve group fairness. Extensive experiments on three datasets with varying levels of social homophily illustrate that our EAGNN method achieves the state-of-the-art performance across two fairness metrics and offers competitive effectiveness.

Database parameter automatic tuning is one of the challenging and difficult tasks that database administrators (DBAs) frequently encounter in artificial intelligence (AI) enabled database (DB) systems. Preferentially optimizing key parameters emerges as a critical point in addressing this issue, and it can help identify important parameters by exploring the interactions between parameters. Aiming to overcome the disadvantages of existing methods, we propose a collaborative multi-agents model called CMA+DB to automatically tune DB parameters in an effective and efficient fashion. CMA+DB integrates three components including SAPM (Single-Agent Pre-trained Model), MATM (Multi-Agent Joint Training Model), and PJTM (Probability-based Joint Training Model). SAPM applies the deep deterministic policy gradient to explore the impact of one single agent on DB performance, MATM uses multi-agent deep deterministic policy gradients to find agents that collaboratively work to improve DB performance, and PJTM can enhance parameter tuning by important agents based on a probabilistic selection factor. In the CMA+DB model, each agent is responsible for tuning a portion of the parameters, and multiple agents collaborate to recommend the optimal parameter configuration. This hybrid model can expand the number of tunable parameters in order to perform parameter tuning from the aspects of functions and parameter levels (i.e., global, DB, and session level). Experimental results reveal that CMA+DB obtains the fastest convergence performance (when reaching the largest throughput) of 14.83% faster than the state-of-the-art (SOTA) algorithms in the TPC-C benchmark on average. Essentially, after the phase of SAPM model training, CMA+DB outperforms the performance of the SOTA models in throughput. Furthermore, DB performance of CMA+DB can be improved by 1.758% through the phases of MATM and PJTM model training.

Class Integration Test Order (CITO) generation is essential to minimize testing cost in object-oriented software.Traditional methods based on static dependencies often producesuboptimal results, while recent approaches that incorporatedynamic dependencies typically neglect accurate stubbing costestimation and face scalability challenges. We propose DyCITO+,an extension of DyCITO, which originally modeled CITO generationas a Reinforcement Learning (RL) problem using Qlearning.However, DyCITO relies on tabular methods, and thislimits its scalability. DyCITO+ addresses this by introducingthree Deep Reinforcement Learning (DRL) algorithms: DeepQ-Network (DQN), Proximal Policy Optimization (PPO), andAdvantage Actor-Critic (A2C), to handle the complexity oflarge-scale systems more effectively. DyCITO+ builds on thedynamic dependency analysis mechanism from DyCITO, whichcaptures more accurate runtime relationships, including interfaceimplementation, abstract class inheritance, method overriding,and multilevel inheritance. We evaluated DyCITO+ on eightJava programs of varying sizes. The results show that DyCITO+significantly improves the scalability and effectiveness of CITOgeneration. Among the three DRL methods, A2C consistentlyproduces the lowest overall stubbing complexity, particularly inmedium- and large-scale systems.

Human activity recognition (HAR) is prone to performance degradation in real-world applications due to data missing between intra-sensor and inter-sensor channels. Masked modeling, as one mainstream paradigm of self-supervised pre-training, can learn robust representations across sensors in the data missing scenario by reconstructing the masked content based on the unmasked part. However, the existing methods predominantly emphasize the temporal dynamics of human activities, which limits their ability to effectively capture the spatial interdependencies among multiple sensors. Besides, different human activities often span across various spatial-temporal scales, which results in activity recognizer failing to capture intricate spatial-temporal semantic information. To address these issues, we propose RobustHAR, a new HAR model with multi-scale spatial-temporal masked self-supervised pre-training designed to improve model performance on the data missing context. RobustHAR involves three main steps: (1) RobustHAR constructs location-inspired spatial-temporal 3D-variation modeling to capture spatial-temporal correlated information in human activity data. (2) RobustHAR then designs multi-scale spatial-temporal masked self-supervised pre-training with semantic-consistent multi-scale feature co-learning for learning robust features at different scales. (3) Finally, RobustHAR fine-tunes the pretraining model with adaptive multi-scale feature fusion for human activity recognition. Extensive experiments on three public multi-sensor datasets demonstrate that RobustHAR outperforms existing state-of-the-art methods.

Fair graph neural networks aim to eliminate discriminatory biases in predictions. Existing approaches often rely on adversarial learning to mitigate dependencies between sensitive attributes and labels but face challenges due to optimisation difficulties. A key limitation lies in neglecting intrinsic causality, which may lead to the entanglement of sensitive and causal factors, discarding causal factors or retaining sensitive factors in the final prediction, especially on unbalanced datasets. To address this issue, we propose a Causality-inspired Disentangled framework for Fair Graph neural networks (CDFG). In CDFG, node representations are conceptualised as a combination of causal and sensitive factors, enabling fair representation learning by only utilising the causal factors. We first use a counterfactual data generation mechanism to generate counterfactual data with similar causal factors but completely different sensitive factors. Then, we input real-world data and counterfactual data into the factor disentanglement module to achieve independence and disentanglement between the causal factors and sensitive factors. Finally, an adaptive mask module extracts the causal representation for fair and accurate graph-based predictions. Extensive experiments on three widely used datasets demonstrate that CDFG consistently outperforms existing methods, achieving competitive utility and significantly improved fairness.

The generation of class integration test orders (CITOs) is a pivotal element in integration testing, which focuses on determining the optimal order for integrating classes while testing an object-oriented system. Due to a high number of dependencies and their possible error proneness, some classes are more critical than others in a program. Existing methods for handling these classes only assess risk in terms of their dependencies; they do not consider historical bug information as an additional indicator and mainly work on small programs. To overcome these limitations, this paper introduces Criticality-Driven CITO (CD-CITO) generation, an innovative approach to optimize CITOs by focusing on class criticality. CD-CITO assesses both the importance of a class in terms of its dependencies and the likelihood of defects, based on historical bug data, to determine a criticality score. Then, it reformulates the CITO generation problem as a Reinforcement learning (RL) task and uses the Advantage Actor-Critic (A2C) algorithm to address it. We propose a novel reward calculation strategy to guide the learning agent, balancing stubbing costs with the criticality values of classes to optimize the test order. To extract fault proneness information and assess the approach, the paper uses Defects4J, a data set that contains real bugs and patches of Java programs. The results obtained show that CD-CITO effectively identifies and prioritizes highly critical classes and also minimizes stubbing costs while generating CITOs, which makes it a valuable tool for integration testing.

Heterogeneous hypergraph neural networks are powerful tools to capture complex correlations among various nodes in Heterogeneous Information Networks (HINs). Despite satisfied performances of them, they are still plagued by the following problems: 1) They cannot capture the correlations in structural and semantic view at once, leading to topological information loss. 2) Due to the number of nodes being greater than the number of node types, node-level self-attention they used causes massive parameters and leads to high time consumption. 3) Interactions in meta-paths may be redundant, resulting in the correlations bias. To address the three issues, we propose an efficientMulti-ViewHeterogeneousHypergraphConvolutionalNetwork (MVH$^{2}$GCN). It first constructs relational and semantic hypergraphs based on different types of edges and meta-paths respectively, to represent the complex correlations in structural view and semantic view. Meanwhile, the clean semantic hypergraphs are generated by structure learning network to avoid redundancy. Then, an efficient hypergraph convolutional network is designed to learn node embeddings. By doing so, correlations in the two views are captured. Finally, the learned node embeddings from two views are aggregated via a gated embedding fusion module for downstream tasks. Experiment results demonstrate that MVH$^{2}$GCN is effective and efficient.

The multibehavior recommendation aims at alleviating the data sparsity problem and improving recommendation accuracy by exploiting the rich knowledge in auxiliary behaviors. However, existing methods focus on modeling the relationships between behaviors while ignoring users’ interaction intents, making it difficult to capture users’ fine-grained interest requirements, which leads to a decline in user experience. To address this issue, we propose a multibehavior intent disentangled recommendation (MBIDR) model. First, we design an intent-aware interaction classifier that automatically identifies various intents based on user and item characteristics, classifying interactions into different categories to better explore users’ fine-grained interests. Second, we develop an adaptive relation learning approach that enables the model to better capture the varying importance of different interaction patterns in relation to user preferences. Third, we introduce multitask learning and nonsampling loss, which effectively leverage richer supervisory signals to enhance model training performance. Finally, extensive experiments on two real datasets demonstrate the effectiveness of MBIDR over baselines, with the best improvement reaching 16.20%.

Effective expert selection is an important guarantee for academic evaluation. Recently, graph structure learning (GSL) have been used to model the complex relationships between candidates and experts in both professional fields and avoidance strategies. Most existing GSL models aim to learn the structure of homogeneous graph. However, they cannot learn the structure of heterogeneous graph (HG) because they ignore the complex relation attributes in HG. To fill this gap, we propose a relation enhanced heterogeneous graph structure learning (RE-HGSL) model that refines HG structure and learns node and relation representations simultaneously for expert selection in academic evaluation. The main idea of RE-HGSL is refining the HG structure by reconstructing edges through the similarity of relation triplet$<$head node, relation, tail node$>$. Specifically, we first set relation representation vector for each kind of relation to capture multiple relation attributes and learn node and relation representations to preserve multiple semantic information. Then, we construct multiple feature graphs where the edges are constructed according to the similarity of the relation triplet. Finally, the refined graph structure is generated by aggregating the original graph and the feature graphs, which maintains not only the part of original vital structure but also the refined edges.

Recommender systems (RS) have found extensive applications in web and internet services, yielding significant advantages for both users and service providers, including domains such as E-commerce and Movie platforms, News portals and so on. As a crucial branch of recommendation services, multibehavior recommendation aims to leverage auxiliary behavior data (e.g., page-view and add-to-cart) to improve the recommendation performance on target behavior (e.g., purchase). Multibehavior recommendation align more closely with real-world scenarios, resulting greater research significance. However, there are two-fold issues existed in the present models that adversely affect the service quality of multibehavior RS: 1) the behavior overlap (i.e., the interaction records between identical user-item pairs co-existed in different behavior data) hinders accurate user preference learning; and 2) the commonalities of user preferences over items under different behaviors are not well explored, thereby making behavior intercorrelations not been fully captured. To handle these, we propose a nonoverlapping heterogeneous graph multibehavior recommendation (No-HGMR) model. To tackle the first problem, we erase the overlap between multitype behavior data to construct a nonoverlapping heterogeneous graph. In this graph, each specific user-item pair is connected by only one type of edge, mitigating confusion and enabling more accurate user preference learning. To solve the second problem, we construct correlation features based on user properties and item attributes. These features are then incorporated into the user preference learning procedure, facilitating the understanding of users’ common preference over items under different behaviors. Extensive experiments on real-world datasets verify the effectiveness of No-HGMR as compared to competitive state-of-the-art methods.

The rapid development of Large Language Models (LLMs) creates new opportunities for recommender systems, especially by exploiting the side information (e.g., descriptions and analyses of items) generated by these models. However, aligning this side information with collaborative information from historical interactions poses significant challenges. The inherent biases within LLMs can skew recommendations, resulting in distorted and potentially unfair user experiences. On the other hand, propensity bias causes side information to be aligned in such a way that it often tends to represent all inputs in a low-dimensional subspace, leading to a phenomenon known as dimensional collapse, which severely restricts the recommender system’s ability to capture user preferences and behaviors. To address these issues, we introduce a novel framework named Counterfactual LLM Recommendation (CLLMR). Specifically, we propose a spectrum-based side information encoder that implicitly embeds structural information from historical interactions into the side information representation, thereby circumventing the risk of dimension collapse. Furthermore, our CLLMR approach explores the causal relationships inherent in LLM-based recommender systems. By leveraging counterfactual inference, we counteract the biases introduced by LLMs. Extensive experiments demonstrate that our CLLMR approach consistently enhances the performance of various recommender models.

Multimodal action recognition can use complementary information from multiple modality data to identify human behaviors, and has achieved remarkable results. However, existing multimodal fusion methods often overlook the difference in contribution within intra-joints and inter-joints, which limits them to discerning ambiguous actions when different actions with similar sequences. Besides, the modality gap hinders graph convolutional networks in extracting correlation information between multimodal action data. To solve the above problems, we propose a multimodal action recognition method based on Adaptive Multimodal Graph Representation Fusion model (AMGRF). Firstly, we use skeleton data and wearable sensor data jointly to depict human actions, and construct heterogeneous graph derived from skeleton graph and sensor graph to mine inter-modal correlation information. Secondly, we design adaptive multimodal graph representation fusion module to achieve node-level action feature fusion among intra-joints and inter-joints via Gumbel-Softmax. Finally, extensive experiments on three public datasets (CZU-MHAD, UTD-MHAD, and Berkeley-MHAD) substantiate the superiority of AMGRF over state-of-the-art methods.

The community plays a crucial role in understanding user behavior and network characteristics in social networks. Some users can use multiple social networks at once for a variety of objectives. These users are called overlapping users who bridge different social networks. Detecting communities across multiple social networks is vital for interaction mining, information diffusion, and behavior migration analysis among networks. This article presents a community detection method based on nonnegative matrix trifactorization for multiple heterogeneous social networks, which formulates a common consensus matrix to represent the global fused community. Specifically, the proposed method involves creating adjacency matrices based on network structure and content similarity, followed by alignment matrices that distinguish overlapping users in different social networks. With the generated alignment matrices, the method could enhance the fusion degree of the global community by detecting overlapping user communities across networks. The effectiveness of the proposed method is evaluated with new metrics on Twitter, Instagram, and Tumblr datasets. The results of the experiments demonstrate its superior performance in terms of community quality and community fusion.

In this article, we aim to offer an interpretable learning paradigm for incremental random weight neural networks (IRWNNs). IRWNNs have become a hot research direction of neural network algorithms due to their ease of deployment and fast learning speed. However, existing IRWNNs have difficulty explaining how hidden nodes (parameters) affect the convergence of network residuals. To address this gap, this article proposes an interpretable construction algorithm (ICA). Specifically, we first conduct a spatial geometric analysis of the network construction process and establish the spatial geometric relationship between the network residuals and hidden parameters to visualize the influence of hidden parameters on the convergence of the network residuals. Second, based on the spatial geometric relationship and node pool strategy, an interpretable control strategy with spatial geometry information is established to obtain hidden parameters conducive to the convergence of network residuals. In addition, to facilitate ICA to handle complex tasks of big data, this article proposes a lightweight ICA with low complexity, namely ICA+. Finally, it is proved theoretically that the ICA and ICA+ proposed in this article have universal approximation properties. The experimental results on two real-world datasets and seven benchmark datasets demonstrate the advantages of the proposed ICA and ICA+ in terms of fast learning, good generalization, and compactness of network structure.

It has become a cardinal task to identify fake information (misinformation) on social media, because it has significantly harmed the government and the public. There are many spam bots maliciously retweeting misinformation. This study proposes an efficient model for detecting misinformation with self-supervised contrastive learning. A B ayesian graph L ocal extrema C onvolution (BLC) is first proposed to aggregate node features in the graph structure. The BLC approach considers unreliable relationships and uncertainties in the propagation structure, and the differences between nodes and neighboring nodes are emphasized in the attributes. Then, a new long-tail strategy for matching long-tail users with the global social network is advocated to avoid over-concentration on high-degree nodes in graph neural networks. Finally, the proposed model is experimentally evaluated with two public Twitter datasets and demonstrates that the proposed long-tail strategy significantly improves the effectiveness of existing graph-based methods in terms of detecting misinformation. The robustness of BLC has also been examined on three graph datasets and demonstrates that it consistently outperforms traditional algorithms when perturbed by 15% of a dataset.

Recent studies have focused on using natural language (NL) to automatically retrieve useful data from database (DB) systems. As an important component of autonomous DB systems, the NL-to-SQL technique can assist DB administrators in writing high-quality SQL statements and make persons with no SQL background knowledge learn complex SQL languages. However, existing studies cannot deal with the issue that the expression of NL inevitably mismatches the implementation details of SQLs, and the large number of out-of-domain (OOD) words makes it difficult to predict table columns. In particular, it is difficult to accurately convert NL into SQL in an end-to-end fashion. Intuitively, it facilitates the model to understand the relations if a "bridge" [transition representation (TR)] is employed to make it compatible with both NL and SQL in the phase of conversion. In this article, we propose an automatic SQL generator with TR called GTR in cross-domain DB systems. Specifically, GTR contains three SQL generation steps: 1) GTR learns the relation between questions and DB schemas; 2) GTR uses a grammar-based model to synthesize a TR; and 3) GTR predicts SQL from TR based on the rules. We conduct extensive experiments on two commonly used datasets, that is, WikiSQL and Spider. On the testing set of the Spider and WikiSQL datasets, the results show that GTR achieves 58.32% and 71.29% exact matching accuracy which outperforms the state-of-the-art methods, respectively.

In recent years, many intelligent optimization algorithms have been applied to the class integration and test order (CITO) problem. These algorithms also have been proved to be able to efficiently solve the problem. Here, the design of fitness function is a key task to generate the optimal solution. To better solve the class integration and test order problem, we propose a new fitness function to generate the optimal solution that achieves a balanced compromise between the different measures (objectives) such as the total number of stubs and the total stubbing complexity in this paper. We used some programs to compare and evaluate the different approaches. The experimental results show that our proposed approach is encouraging to some extent in solving the class integration and test order problem.

With the development of location-based services, more moving objects can be traced and a great deal of trajectory data can be collected. Periodicity is very commonly used to analyse the habits of moving objects, so finding objects’ periodic patterns can aid in understanding their behaviour. However, objects’ periodic patterns are always unknown previously, and describing their periods with different granularities will create some surprised findings. This article proposes a multi-granularity periodic activity discovery (MPAD) approach for moving objects. First, a multi-granularity model is introduced to describe the spatial and temporal information of an object’s activities. Then, two algorithms, namely, spatial first and temporal first multi-granularity activity discovery algorithms, are provided to transfer objects’ activities into different granularities. Finally, a novel periodic discovery algorithm is described to find the periodicities of objects’ activities. Experiments on both synthetic and real datasets demonstrate both the efficiency and effectiveness of the proposed work and its notably improved running performance compared to the same algorithms. Additionally, the discovered periodic patterns are more practically significant.

In recent years, many approaches have been developed to determine the order of tested classes in interclass integration test. However, existing approaches are inaccurate, as they ignore the influence of abstract classes and polymorphism. In this paper, we propose a test-level-based approach to deal with class-integration-test order, in which both abstract classes and polymorphism are taken into account. First, based on interclass dependence analysis, we develop an edge-removing algorithm to eliminate cycles caused by static and dynamic dependencies, taking abstract classes and polymorphism into account. Then, after eliminating cycles, we propose a class-integration-test order algorithm based on test levels, including static and dynamic test levels. In this algorithm, we take into account the fact of some test levels infeasible caused by the characteristic of abstract classes that they cannot be instantiated and offer corresponding adjustment strategy. Finally, we design and implement a test level order generator. The experimental results show that the proposed strategy needs less test stubs than the most typically graph-based approaches. Copyright © 2014 John Wiley & Sons, Ltd.